Hydraulic drives are widely used in tuber crop machinery for travelling and working-unit operations to cope with complex field conditions. However, under low-speed high-torque operation, fluctuating loads, and multi-actuator working conditions, hydraulic systems often suffer from reduced energy efficiency and insufficient stability. This paper reviews typical applications of hydraulic technology in travelling and working-unit drive systems of tuber crop machinery, and analyzes key issues including energy efficiency under low-speed high-torque conditions, dynamic responses to load fluctuations, system coupling induced by multi-actuator operation, and stability constraints related to crop damage sensitivity. Recent advances in system optimization and control strategies are summarized.

To improve the utilization rate of rice straw remaining in the field after harvest, a pickup device suitable for collecting rice straw was designed, enabling the secondary utilization of crop residues. A solid model of the pickup device was developed using SolidWorks software, and kinematic simulation analysis of the virtual prototype was performed using ADAMS software. By adjusting the ratio between the pickup device speed and the forward speed of the machine, the motion trajectory of the spring teeth on the pickup roller was modified. The optimal rotational speed range of the pickup roller for achieving the best pickup performance was determined to be 54–137 r/min. A discrete element simulation system was established to model the soil–straw–implement interaction during the pickup operation using Python and EDEM software. Using Design-Expert software, a three-factor, three-level orthogonal experimental design was conducted to determine the optimal combination of operational parameters for the pickup device, including forward speed, pickup roller speed, and ground clearance. Field test results indicated that when the machine forward speed was 4.45 km/h, the pickup roller speed was 90.16 r/min, and the ground clearance was 27.73 mm, the rice straw pickup rate reached 97.22%, indicating that the machine achieved optimal straw pickup performance under these operating conditions.

Climatic characteristics, particularly temperature and ambient humidity, significantly affect the qualitative and quantitative indicators of flax production and fibre formation. Conventional technologies for obtaining flax fibre are based on natural dew retting, the efficiency of which depends on atmospheric moisture. The decrease in air humidity during summer periods due to climate change complicates the biological processes involved in the transformation of flax stems into retted straw. A separate harvesting technology involving low cutting of stems and their placement into windrows has been proposed to utilize productive soil moisture during retting and to accelerate seed harvesting. During field laying, windrows change their geometric parameters, become denser, and increase adhesion both between stems and with the soil surface, which requires periodic lifting and loosening. This paper presents the results of field experimental studies conducted using a developed experimental picker to determine rational structural and technological parameters based on a four-factor experimental design. Changes in windrow geometry and their interaction with the working elements of the picker were analysed. Optimal parameters of the picker for flax retting preparation were established. The study is aimed at developing a new technical solution for flax harvesting.

Accurate perception of the maturity of individual truss tomato fruits and in-situ counting in unstructured protected horticulture scenarios are critical prerequisites for driving selective automated harvesting, yield prediction and improving the level of refined management. Affected by factors such as the small fruit size and the complexity of the natural growth environment, vision-based maturity detection still faces considerable challenges. This paper proposes an improved method for maturity detection and counting of individual truss tomato fruits based on YOLO11n. Under the principle of maintaining a lightweight design, a fine-grained feature stream based on the P2 layer is developed, and the DySample operator is integrated to optimize the quality of feature fusion. Combined with the Selective Feature Refinement Module (EMA) and a four-head detector with full-scale coverage, the proposed method aims to maximize the models representation capability in capturing small long-distance targets and in dense occluded environments. A dataset of individual truss tomato fruits with three maturity labels (immature, turning, ripe) is constructed, and systematic comparative experiments are conducted on the original YOLO11n and the improved model. The experimental results show that the improved YOLO11n-DRE outperforms the original YOLO11n model in maturity detection accuracy, with P, R and mAP@0.5 increased by 0.65%, 0.63% and 1.07% respectively, and the model parameters reduced by 8.5%. This method demonstrates excellent detection performance, and provides a reference model and technical prerequisite for the maturity detection and yield estimation of individual truss tomato fruits.

Soybean (Glycine max) grain moisture content (MC) at harvest affects yield, storage, and processing quality, but traditional measurements are laborious and unsuitable for large-scale monitoring. This study aimed to develop an efficient method for estimating soybean (Glycine max) grain moisture content (MC) at maturity, addressing the limitations of traditional labor-intensive measurements. UAV-based multispectral imagery from a DJI Mavic 3M was used to extract spectral reflectance and vegetation indices (VIs). Three feature selection techniques (SHAP, RFA, ReliefF) and six regression models (PLSR, SVR, MLR, RFR, XGBoost, RR) were applied to identify key predictors and optimize performance. Results showed that SVR using spectral reflectance achieved the highest accuracy (R² = 0.763, RMSE = 1.473), while RFR performed best for combined spectral and VI features. The RE and NIR bands were the most sensitive to MC. The findings demonstrate that integrating UAV multispectral data with machine learning and feature selection enables accurate, rapid, and non-destructive prediction of soybean MC, supporting precision harvest and crop management.

This study focuses on the threshing device of a cross-axial flow drum and establishes a discrete element method (DEM) model to simulate the separation process of corn kernels within the device. The model reveals the motion behavior of corn kernels under different drum rotational speeds, feeding rates, and threshing gaps, as well as their spatial distribution along the axial direction of the drum. An orthogonal experimental design was employed to investigate the effects of various threshing parameters on the grain retention rate inside the device. The results indicate that drum rotational speed has the most significant influence on grain retention, followed by the feeding rate and the threshing gap. These findings provide an important theoretical basis for optimizing the threshing performance and improving the operational efficiency of the device. The optimal parameter combination was determined as follows: drum speed of 572.96 r/min, feeding rate of 1.5 kg/s, and threshing gap of 36 mm, which minimizes grain retention and ensures optimal operational performance.

Soil adhesion markedly increases operational resistance, diminishes efficiency, and elevates energy consumption in the soil contact components of agricultural machinery. This study introduces a method for reducing adhesion and facilitating detachment through electroosmosis, while also examining its effects on energy consumption via pull-out and slip tests. The results indicate that electroosmosis generates a water film at the metal interface, which significantly decreases the interfacial adhesion force. At 60 V and a soil moisture content of 30%, the pull-out force is reduced by 21.48 N, and the slip angle decreases by 55.8°. Compared to traditional methods, electroosmosis not only lowers energy consumption but also effectively mitigates wear and maintenance costs.

In order to address the challenges associated with wide moisture content variation in corn ears, significant operational fluctuations, and stringent kernel damage control requirements during threshing in experimental plots, a combined rod-tooth and spike-tooth axial-flow corn threshing device was designed. Based on the movement and force characteristics of the cobs during the axial-flow threshing process, the key component parameters of the machine such as the threshing drum, concave screen and flow guide structure were calculated and designed, and the three-dimensional model of the whole machine was built. Taking the discrete element method, the movement behavior of ears in the threshing drum was simulated and analyzed, and the influence of the combined threshing components on the axial transport characteristics of ears was focused on. At the same time, modal analysis of threshing drum was conducted by finite element method, and structural dynamic safety in operation speed range was verified. A multi-factor experiment was carried out on combined axial-flow corn threshing test bench, taking drum speed, feed rate and threshing gap as experimental factors and kernel breakage rate and unthreshed kernel rate as evaluation indexes. In this study, Design-Expert software was used to analyze the experimental results and optimize the parameters. Multi-objective optimization identified an optimal parameter combination with a drum speed of approximately 339 r/min, a feed rate of about 3.6 kg/s, and a threshing gap of around 45 mm. Field validation results showed good agreement with the model predictions, indicating that the combined axial-flow threshing device meets the operational requirements of small plots and exhibits good adaptability and operational stability.

To address the challenges of unstable planting spacing and susceptibility to operational fluctuations during the operation of electric-driven potato planters, an electric-driven spacing control system based on a BP neural network–PID controller was designed. The system uses actual output data from the rotational speed sensor, analog voltage signals from the control terminal, and control error information as inputs. By leveraging the online learning and adaptive tuning capabilities of the BP neural network, PID parameters are dynamically generated and optimized for real-time operating conditions, thereby achieving precise speed control of the seeding actuator. By integrating the structural design of the electric-driven potato planter with the spacing control mechanism, a mathematical model of the brushless DC motor and transmission system was established. Based on this model, a BP neural-network-based PID control strategy was developed. A MATLAB/Simulink simulation platform was constructed for comparative validation. Compared with a traditional PID controller tuned by empirical trial-and-error, the proposed method demonstrated superior control performance. The traditional PID exhibited approximately 10%–15% overshoot with oscillations during step response, whereas the neural network PID maintained a comparable rise time with negligible overshoot and a smoother response. Finally, both control algorithms were deployed on prototype machines for field trials to validate their effectiveness and engineering applicability under real-world conditions. Field test results indicated that under neural network control, the maximum row spacing error was 1.1 cm, with an average absolute relative error of 2.7%, meeting the row spacing accuracy requirements for electric-driven potato planters. These findings provide a theoretical basis and practical reference for the design of row spacing control systems in potato planters.

Reliable operation of agricultural machinery depends on the structural integrity of its steel components during manufacturing. To accurately detect minor surface defects on steel components of agricultural machinery in complex environments, this study proposes an improved detection model, CGC-YOLOv11n, based on the YOLOv11n architecture. The research focuses on enhancing real-time, high-precision defect detection for agricultural machinery maintenance, addressing challenges such as subtle defects under dust, vibration, and foreign object conditions. First, Converse2D reverse convolution is integrated into the C3k2 module to enhance fine-grained feature representation for subtle and blurred defects. Second, the GESA module replaces C2PSA, leveraging dynamic sparse attention to strengthen multi-scale aggregation and focus on key target cues. Third, a Coordinated Detail-Preserving Contextual Fusion (CDPCF) module—an innovative extension of DPCF—is embedded in the neck, employing adaptive content-aware gating to synergistically balance high- and low-resolution features. Experimental results demonstrate that CGC-YOLOv11n achieves a precision of 74.85%, a recall of 74.16%, and a mean average precision (mAP@0.5) of 79.81%, representing an improvement of 1.83 percentage points over the baseline YOLOv11n. Across datasets including NEU-DET and a self-collected farm machinery set, CGC-YOLOv11n delivers superior detection performance. The improved model exhibits robust capabilities in agricultural machinery maintenance, providing technical support for reliable agricultural operations.

To address the low efficiency of traditional pig behavior monitoring methods, this study proposes a swine motion information recognition algorithm and develops a corresponding monitoring system. The system adopts a front-end/back-end separated architecture. The front-end provides video playback and control, multi-target identification, and trajectory visualization. The back-end performs motion detection and background modeling using the MOG2 algorithm and generates trajectory heatmaps through DBSCAN-based clustering. Two operational workflows are supported, namely manual annotation and automatic feature extraction. The system calculates key motion parameters, including velocity and momentum, and enables the export of the processed data. Experimental results demonstrate that the proposed system can effectively analyze swine motion characteristics and trajectory information, providing an accurate and efficient monitoring solution for large-scale pig farming, with practical value for optimizing husbandry management and improving animal health.

To address the issue of missing discrete element simulation parameters for sweet sorghum seeds, this study systematically calibrated their contact parameters using a method combining physical experiments and numerical simulations. First, a discrete element model of sweet sorghum seeds was constructed via 3D scanning and the multi-sphere filling method, and their basic physical parameters were measured (mean triaxial dimensions: 4.51 mm × 3.20 mm × 2.40 mm, density: 1.156 g/cm³, moisture content: 9.8%). The static friction coefficient (0.303), rolling friction coefficient (0.038), and coefficient of restitution (0.534) between the seeds and polylactic acid (PLA) material were calibrated using inclined plane sliding/rolling tests and free-fall collision tests. Based on the physical test results of the dynamic angle of repose (measured value: 34.61°), the inter-seed static friction coefficient and rolling friction coefficient were screened out as significantly influential parameters via a single factor test. A quadratic regression model was then used to establish a mapping relationship between parameters and the angle of repose, optimizing to obtain the optimal parameter combination (static friction coefficient: 0.124, rolling friction coefficient: 0.020). Simulation verification showed that under this parameter combination, the simulated dynamic angle of repose was 34.59°, with a relative error of only 0.03% compared to the actual value. The parameter calibration method established in this study features high precision and good repeatability, providing reliable parameter support for the discrete element simulation of sweet sorghum sowing equipment, and holding significant engineering application value for optimizing seed metering device design and reducing seed damage rates.

To address the limitations of existing detection methods for imperfect maize kernels during grain acquisition and storage — specifically limited detection categories, difficulty in identifying minute defects, insufficient robustness in individual kernel extraction, and incomplete information from single-view inspection — this paper proposes a multi-stage detection method based on CALFNet. The proposed method begins with image acquisition using a standardized imaging system, followed by a single-kernel extraction model that integrates YOLOv8 with the watershed algorithm to segment densely distributed maize kernels. The Hungarian algorithm is then employed to automatically match the front and back images of the same kernel. Based on this process, a dual-stream feature fusion classification network, CALFNet, was constructed, integrating EfficientNet-B0 and MobileNetV2 to fuse visual information from both sides of the kernels. A GUI-based detection system was subsequently developed, and the DeepSeek V3 large language model was incorporated to analyse the classification results, enabling the automatic generation of quality evaluation reports and production guidance recommendations. Experimental results show that the CALFNet model achieved a classification accuracy of 99.16% on the test set, outperforming the comparative models. In a full-pipeline integrated test on 506 real-world samples, the overall recognition and classification accuracy reached 96.05%. This study provides a feasible solution for the intelligent assessment of maize quality.

To address the limitations of existing detection methods for imperfect maize kernels during grain acquisition and storage — specifically limited detection categories, difficulty in identifying minute defects, insufficient robustness in individual kernel extraction, and incomplete information from single-view inspection — this paper proposes a multi-stage detection method based on CALFNet. The proposed method begins with image acquisition using a standardized imaging system, followed by a single-kernel extraction model that integrates YOLOv8 with the watershed algorithm to segment densely distributed maize kernels. The Hungarian algorithm is then employed to automatically match the front and back images of the same kernel. Based on this process, a dual-stream feature fusion classification network, CALFNet, was constructed, integrating EfficientNet-B0 and MobileNetV2 to fuse visual information from both sides of the kernels. A GUI-based detection system was subsequently developed, and the DeepSeek V3 large language model was incorporated to analyse the classification results, enabling the automatic generation of quality evaluation reports and production guidance recommendations. Experimental results show that the CALFNet model achieved a classification accuracy of 99.16% on the test set, outperforming the comparative models. In a full-pipeline integrated test on 506 real-world samples, the overall recognition and classification accuracy reached 96.05%. This study provides a feasible solution for the intelligent assessment of maize quality.

Accurate prediction of greenhouse temperature and humidity variations was essential for optimizing crop growth conditions. To improve prediction accuracy under complex nonlinear environments, this study proposed an Improved Sparrow Search Algorithm–optimized Bidirectional Long Short-Term Memory model (ISSA-BILSTM). By introducing Tent chaotic mapping, an adaptive discoverer ratio mechanism, and a Lévy flight disturbance strategy, the proposed approach enhanced population diversity, balanced global exploration and local exploitation, and improved the convergence stability of hyperparameter optimization. Validation results showed that the proposed model achieved an R² of 0.9777 (MAE = 0.0159) for temperature prediction and an R² of 0.9762 (MAE = 0.0213) for humidity prediction, outperforming standard BILSTM and SSA-LSTM models. The proposed model enabled accurate short-term prediction of temperature and humidity, providing effective support for intelligent environmental regulation and contributing to reduced energy consumption and production costs.

To address the issues of slow convergence in traditional methods, this study integrates adaptive gradient correction with a belief update mechanism based on the AdaBelief algorithm, and proposes a multimodal scenario control model for low-temperature grain storage. Experimental results indicate that the proposed model can maintain the grain storage temperature at approximately 12°C under various environmental conditions, and the average conductivity of wheat, rice, corn, and soybean is kept below 45 µs.cm-1. These findings demonstrate that the proposed model significantly improves the level of intelligent control in low-temperature grain storage systems and provides a novel approach for the precise regulation of grain storage environments.

To clarify the impact of high-voltage pulsed electric field (HPEF) pretreatment on the rheological properties of Sanbai melon juice, experiments were conducted using two different juice concentrations. The study examined the effects of HPEF pretreatment on steady-state shear viscosity, the influence of temperature on steady-state shear viscosity, and the dynamic viscoelastic properties of the juice. The results showed that the shear stress increased with increasing shear rate, indicating that the juice exhibited pseudoplastic behavior. As temperature increased, the viscosity of the juice decreased gradually. Within the angular frequency range of 0.1–100 rad/s, both the storage modulus and the loss modulus increased with increasing angular frequency. Furthermore, the viscoelastic properties of the juice improved as the HPEF pretreatment intensity increased, with the elastic characteristics becoming more dominant than the viscous characteristics. These findings provide a theoretical basis for the processing and utilization of Sanbai melon juice.

To address the high threshing loss rate and significant seed breakage during mechanized sesame harvesting, a threshing device suitable for combine sesame harvesters was designed. The threshing characteristics and mechanical requirements of sesame were analyzed, and a method in which threshing drums and concave screens operate in tandem was proposed. Based on the structural parameters of sesame capsules and the requirements of the threshing process, a segmented rib-bar + bar-tooth combination differential drum was designed. The device employs low-speed rib-tooth kneading for threshing, reducing seed damage in the main threshing section, while the high-speed section uses bar-tooth impact action for threshing, reducing seed carryover loss. Field trials were conducted using an innovatively developed sesame combine harvester prototype, with loss rate, impurity content, and seed breakage rate evaluated as key indicators. The results indicate that the designed threshing device achieved a threshing loss rate of 4.09%, a grain breakage rate of 1.98%, and an impurity rate of 1.81%, all meeting the requirements for mechanized sesame harvesting. These findings provide valuable support for the design of combine harvesting equipment for small-seeded crops such as sesame.

With the speedy development of information technology, smart agriculture has become the key to the transformation and upgrading of modern agriculture. To improve the precision and practicality of data analysis, a data analysis model for grain harvesters based on a combination of K-means and Apriori is designed. This model collects grain harvester data in real-time, uses the K-means for preliminary clustering, and integrates Apriori algorithms to dynamically adjust clustering centers to improve clustering accuracy. At the same time, the model introduces ResNet to extract image features of grain harvesters, thereby enhancing the comprehensiveness of data analysis. Comparative experiments show that the algorithm has a high adjusted Rand index of 0.92, an F-value of 0.89, a convergence time of only 12.4 seconds, and a clustering accuracy of 95% for agricultural machinery databases. The analysis of actual operational data of grain harvesters shows that their faults are concentrated in the transmission device, accounting for up to 45%. When operating under high load, the work efficiency drops sharply, and when the rated power exceeds 30%, the work efficiency is only 72%. When working in a wet and muddy environment, the failure rate reaches 42.8%. From the above results, the data analysis model for grain harvesters based on the combination of K-means and Apriori algorithm proposed in the study can perform cluster analysis on the data of grain harvesters, laying a solid foundation for the sustainable development of smart agriculture.

To address severe soil compaction, high draft resistance, and low subsoiling efficiency in saline-alkali soils, a soil-breaking subsoiling shovel aimed at reducing draft and enhancing soil comminution was designed. A discrete element method (DEM) soil model with graded particle sizes was established using the Hertz–Mindlin contact model with JKR (Johnson–Kendall–Roberts) adhesion. Simulation analyses were combined with an orthogonal experimental design to optimize the shovel’s structural parameters and operating conditions. The optimal parameter combination—blade edge angle of 56.97°, blade inclination of 44.45°, and forward speed of 0.69 m·s?¹—resulted in a simulated draft of 2731.74 N and a maximum particle velocity of 2.75 m·s?¹. Field tests conducted at 0.69 m·s?¹ measured a draft of 2885.62 N, with a relative error of only 4.49% compared with the simulation, indicating high predictive reliability of the model. The mean soil comminution rate reached 57.79%, which is 64.5% higher than the conservation-tillage threshold (=35%). These results demonstrate the usability and effectiveness of the proposed shovel. The optimized mechanical design reduces draft resistance while significantly improving soil fragmentation, and its overall operating performance meets the agronomic requirements for saline–alkali land improvement. Consequently, this design reduces energy consumption, enhances the soil environment for root growth, and exhibits strong agronomic applicability and environmental friendliness.

Tomatoes (Solanum lycopersicum) are not only a staple in cuisines worldwide but also a subject of scientific interest due to their health benefits and distinct ripening process. Recognizing the ripest and most flavorful tomatoes has led to innovative research combining technology and agriculture. In this context, image processing emerges as a promising tool to discern the quality of tomatoes, particularly through color analysis. This study explores the effectiveness of a region-based image processing system in identifying red, ripe tomatoes. Currently, this process is done by hand, which takes time and can lead to mistakes-developed a machine learning-based device that utilizes computer vision and image processing techniques to detect ripe tomatoes with high accuracy. By employing algorithms that analyze color, texture, and shape, our technology can identify the optimal harvest time, making the process faster, more efficient, and more cost-effective. Automating tomato harvesting is crucial to addressing the labor crisis and enhancing the effectiveness of the present harvesting process. The actualization of automated harvesting depends on the ability to precisely recognize fruits. Fruit that is harvested at its peak maturity has the maximum levels of taste, vitamins, and sale value, which optimizes financial gains. There is now an inadequate rate of identification and failure to identify because of the blockage of specific fruits by vegetation and unwanted fruits, as well as the color change brought on by light. In order to identify tomato fruits in difficult circumstances, this research suggests a tomato identification system using the enhanced YOLOv8 framework. According to the models test evaluation, the YOLOv8-Tomato models mAP0.5 was 86.9%, its recall rate was 98%, and its accuracy and precision were 94% and 90%, respectively.

Peanut sprout orientation detection is a critical step in achieving automated production. However, the small target size and dense distribution of peanut sprouts in plug trays impose higher requirements on the model’s ability to extract features from small objects and discriminate in densely populated scenes. In addition, the limited computational resources of embedded devices restrict the deployment of complex models. To address these challenges, this study proposes a lightweight peanut sprout orientation detection model named FSL-YOLO, based on YOLOv8. The proposed model introduces improvements in four main aspects. First, a Fast-CA module, integrating Coordinate Attention with FasterNet, is incorporated into the backbone network to enhance the perception of dense small targets while reducing the number of parameters and computational cost. Second, a lightweight downsampling module (LWDS) is designed to replace traditional convolution operations, further improving detection performance. Third, spatial and channel reconstruction convolution (SCConv) is introduced into the neck network to optimize the C2f module, thereby enhancing feature representation capability and model robustness. Fourth, an efficient lightweight detection head, Detect-L, is constructed to further reduce the model size. Experimental results demonstrate that FSL-YOLO achieves both high accuracy and lightweight performance. The model attains an mAP50 of 96.1%, representing a 2.4% improvement over the original YOLOv8, while reducing floating-point operations (FLOPs) by 51.9% and the number of parameters (Params) by 50%. These results indicate that the proposed model effectively balances detection accuracy and computational efficiency, providing a solid technical foundation for the implementation of automated peanut sprout production systems.

To address the limitations of current rice straw collection methods in China—where straw left in the field for subsequent collection is prone to soil contamination and significant losses, while non-falling harvesting combined with simultaneous baling results in low bale density and reduced harvesting efficiency—a device for simultaneous crushing and spraying of rice straw during harvesting was designed. The device integrates harvesting, straw crushing, spraying, and a vehicle-mounted baling system. Key structural parameters were determined based on the feed rate of the rice combine harvester. Four performance indicators were defined to evaluate straw crushing quality, spraying performance, and straw loss: crushing rate, straw spray mass flow rate, straw spray range, and leakage ratio. To validate the performance of the proposed device, field experiments were conducted during the rice harvesting season in Anhui Province, China, using both conventional and brittle rice varieties. Straw samples were collected before and after crushing, and the crushing rate and segment length distribution uniformity were measured. The experimental results showed that, at normal harvesting speeds, the crushing rate of Yong you 4901 reached 74.75%, while that of Ke Cui Geng No. 1 reached 96.97%, both satisfying the silage feed cutting length requirement of =10mm. The straw spray mass flow rate reached 5.30 kg/s, meeting the design requirement of =1.67 kg/s. The average spreading width was 2.92 m, with a maximum spray distance of 7.71 m, meeting the long-range spray design specification. Straw leakage accounted for 10.26%, indicating a relatively minor level of material loss. These results demonstrate that the proposed rice straw crushing and spraying device exhibits excellent crushing performance and long-range, concentrated spraying capability without adversely affecting normal rice harvesting efficiency. This study provides a useful reference for the design of efficient and low-cost rice straw collection equipment.

To study the performance of the developed teff transplanting machine, the parameters used were hill spacing (cm), number of seedlings per hill (No.), transplanting depth (cm), missing hills (%), defective hills (No.), buried hills (%), damaged hills (%), floating hills (%), transplanting efficiency (%), time requirement (min), fuel consumption (L), field capacity (ha h-1), and field efficiency (%). The experiments were carried out using a randomized complete block design (RCBD) comparing machine transplanting and manual transplanting. Both transplanting methods were evaluated at three seeding rates of 60, 80, and 120 g per tray and three forward speeds of 0.6, 0.8, and 1.2 km h-1. Each treatment was replicated three times. Data analysis was performed using ANOVA, and the comparison of means was determined using the Least Significant Difference (LSD) test at the 5% level of significance. The mean values of hill-to-hill spacing (13.82 and 12.96 cm), number of seedlings per hill (3.94 and 5.00 No.), and transplanting depth (3.79 and 4.04 cm) for the two treatments, respectively, showed no significant difference at the 5% probability level. However, the mean values of missing hills (9.69 and 12.72%), floating hills (1.78 and 4.61%), buried hills (1.39 and 3.36%), damaged hills (2.22 and 7.72%), defective hills (15.11 and 28.56%), and transplanting efficiency (84.89 and 71.44%) differed significantly at the 5% probability level. The actual field capacity, effective field capacity, and field efficiency of the machine and manual transplanting were 0.055 and 0.011 ha h-1, 0.037 and 0.0099 ha h-1, and 67.27% and 87.61%, respectively.

This study addresses the limitations in current finite element analysis (FEA) models of wind-induced response in plastic greenhouses, focusing on the oversimplification of modeling and the neglect of fluctuating wind effects. By deriving dynamic equations of motion for the greenhouse under wind loading and incorporating fluctuating wind through the Davenport wind speed spectrum, the study uses ABAQUS software to create both binding and contact models for a flat-elliptical pipe greenhouse (FEPG). The results emphasize the significant impact of fluctuating wind and the interactions between structural components on wind-induced responses. This work contributes to the design of more resilient greenhouses by enhancing the accuracy of wind response predictions, thereby improving the theoretical framework for FEPG design and providing a more comprehensive approach to wind-induced vibration analysis.

A sliding mode active disturbance rejection tracking control scheme based on the Smith predictive control structure is proposed for the front steering wheel of tractors, aiming to address the challenges of time delay and unknown disturbances in the angle tracking process. In the control system design, the effects of time delay and disturbances on the angle tracking performance are thoroughly considered, based on the established steering system model. A time delay processing structure is designed based on the Smith predictive control principle, and linear extended state observer is designed to realize synchronous estimation for the un-delayed angle output and the controller output. A simple Sliding Mode Controller (SMC) is introduced to replace the linear feedback controller in active disturbance rejection control (ADRC) to improve the accuracy and disturbance rejection performance. Simulation charts and performance evaluating indexes show that the proposed control method has obvious advantages in tracking accuracy, robustness and disturbance rejection in comparison with other methods. The angle tracking error is no more than 0.055rad even under the interference of white noise signal. The proposed method effectively mitigates the impact of time delay and disturbance, it can significantly enhance the dynamic response performance of the steering system.

In most mid-to high-latitude regions, trees are frequently subjected to severe freeze-thaw stress during the overwintering period, yet accurately detecting their freeze-thaw status remains challenging. In this study, Malus spectabilis, a common ornamental tree species in northern China, was selected as the research subject. A self-developed stem water content sensor based on the standing wave ratio (SWR) principle, in combination with a miniature thermocouple array, was employed to achieve in situ and non-destructive monitoring of internal stem water dynamics and radially stratified stem temperature. Furthermore, an Internet of Things (IoT)-based plant freeze-thaw monitoring system was established. The temporal variation characteristics of these parameters during overwintering were analyzed, on the basis of which a computational model of the freeze-thaw process was developed, and a novel method for detecting stem freeze-thaw dynamics was proposed. The results revealed that alternating freeze-thaw cycles in stems are accompanied by distinct endothermic and exothermic phenomena, with the freezing and thawing processes progressing radially from the outer to the inner stem layers—allowing the migration trajectory of freeze-thaw peaks to be tracked. In addition, different types of plant fiber materials were applied to stem tissues to verify the effectiveness of cold-resistance measures. This study provides new insights into the mechanisms regulating tree cold hardiness during overwintering and offers practical references for the scientific management of trees in cold regions.

To solve the problems of straw accumulation and crop injury caused by the inability of the straw-raking motor on a designed soybean inter-row weeder to adjust its operating speed in real time according to the machine’s forward speed, a variable-speed operation method based on dual-speed measurement using GNSS positioning and an encoder is proposed. By setting operational parameters such as working mode and straw density, the system dynamically adjusts the rotational speed of the straw-raking motor in real time based on GNSS and encoder data, ensuring synchronization with the implement’s forward speed. Field performance tests were conducted at three working speeds (3, 5, and 7 km·h-1), with and without the straw-raking control system as experimental controls. The results show that at 7 km·h-1, the average weed removal rate increased by 2.52%, the average straw coverage rate increased by 4.95%, and the average seedling damage rate decreased by 2.28%. These findings provide practical technical references for the implementation of mechanical weed control systems.

In response to the lack of machines for cutting dried chili peppers into segments and the problems of low efficiency and high damage rate of existing segmenting machines, a rotary knife-style dried chili pepper segmenting machine was designed. The working mechanism of the rotary knife in coordination with the drum for cutting was explained, and structural parameters were determined based on the motion characteristics of the rotary knife and practical requirements. Three factors affecting chili pepper segmenting efficiency were identified, and the chili pepper segmenting qualification rate and damage rate were used as experimental indicators. Experiments and data processing were conducted using software, a regression model between the experimental indicators and influencing factors was established, and the optimal parameter combination was determined. The experimental results showed that when the rotary knife speed was 51 r/min, the conveyor belt speed was 0.6 m/s, and the drum speed was 51 r/min, the device damage rate was 1.42%, and the cleaning rate was 98.21%, meeting industry standards. This study can provide a reference for the research of rotary knife-style dried chili pepper segmenting machine technology and equipment.

Efficient and uniform spray application is essential for effective pest management and sustainable rice production. This study evaluated the field spray uniformity of a Remotely Piloted Aircraft System (RPAS) sprayer operated at two flight speeds (3 and 5 m/s) and a constant flight altitude of 3 m above the rice canopy. Field experiments were conducted under actual crop conditions, with the experimental setup and spray operations documented to enhance methodological transparency and reproducibility. Water-sensitive papers were systematically arranged across the spray swath to collect droplet samples, and image analysis software was used to determine droplet size distribution (Dv0.1, Dv0.5, Dv0.9), droplet density, deposition volume, and distribution uniformity. Results indicated that increasing flight speed reduced droplet size and slightly affected coverage uniformity. The median volumetric diameter (Dv0.5) decreased from 255.56 µm at 3 m/s to 221.58 µm at 5 m/s, corresponding to medium–coarse and fine–medium spray classifications, respectively. Droplet density was higher at 3 m/s, whereas deposition volume was slightly greater at 5 m/s. The coefficient of variation (CV%) for Dv0.5 remained within acceptable limits for agricultural spraying at both speeds, indicating satisfactory spray distribution. Overall, the RPAS sprayer demonstrated consistent field performance, with 3 m/s providing the most balanced combination of droplet size, coverage, and uniformity, supporting its potential as a viable alternative to conventional ground-based sprayers in precision rice production.

Accurately identifying rice growth stages in cold regions is challenging due to subtle morphological differences between key stages (e.g., tillering, jointing-booting) and complex field conditions, often leading to inefficient water use. This paper proposes an improved YOLOv12n-based method for rice growth stage recognition. First, a Deformable Large Kernel Attention (D-LKA) mechanism is introduced to enhance the models ability to capture multi-scale morphological features of rice plants through large receptive field convolution and adaptive sampling grids. Second, a weighted Bidirectional Feature Pyramid Network (BiFPN) and a Dynamic Upsampling module (DySample) are employed to construct an efficient multi-scale feature interaction pathway, improving the models perception of details in key plant parts. Finally, the Normalized Wasserstein Distance (NWD) is adopted to optimize the small-target detection strategy, effectively mitigating the under-detection of small-scale features. A cold-region rice growth stage image dataset was constructed for training and evaluation. Results show that the proposed YOLOv12n-DBD model achieves a precision of 93.4%, a recall of 89.7%, a mean Average Precision (mAP@0.5) of 94.4%, and an inference speed of 121.9 FPS. The mAP@0.5 represents an improvement of 4.9 percentage points over the baseline model, outperforming current mainstream detection models while maintaining real-time performance. A mobile recognition system was also developed to provide a convenient solution. The proposed YOLOv12n-DBD model effectively balances recognition accuracy and computational efficiency in the complex environments of cold-region rice fields, offering reliable technical support for growth-stage-specific field management.

This study focused on three tea cultivars from the Jianghan Plain to construct an inversion model between multispectral features and chlorophyll content in tea leaves. Based on 120 samples across two growth stages, indoor multispectral imaging technology was used to simultaneously acquire leaf multispectral data and SPAD values. Through the analysis of the spectral-chlorophyll response mechanism and the evaluation of feature wavelength autocorrelation, the Iteratively Retained Informative Variables (IRIV) algorithm was integrated for feature selection. An evaluation system consisting of seven machine learning models, including Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR), was established. The results showed that the model combining the adjacent band change rate features selected by IRIV with Multiple Linear Regression (MLR) achieved the optimal inversion accuracy (R²=0.785, RMSE=4.241). Additionally, the vegetation index-MLR combination (R²=0.791, RMSE=4.222) and the mixed feature-LASSO combination (R²=0.773, RMSE=4.403) performed prominently under different feature dimensions. This study provides a feature engineering scheme with strong interpretability and a model optimization path for hyperspectral non-destructive detection of tea physiological parameters.

To address the problems of ginger stalks being easily broken and incompletely cut during the clamp-pull ginger harvesting process, two ginger varieties, small yellow ginger and red ginger, were selected as experimental subjects. The basic biophysical parameters of the two varieties were measured, and the mechanical properties of their stalks were tested through shear, compression, tensile, and pull-out tests. The results showed that the mechanical properties of red ginger stalks were superior to those of small yellow ginger, and the stalks were less prone to breakage during harvesting. Moreover, the mechanical properties of the middle part of red ginger stalks were better than those of the upper and lower parts, making it more suitable as the clamping position in clamp-pull harvesting. The maximum shear strength of red ginger stalks was 1.42 MPa, the maximum compressive strength was 0.21 MPa, the maximum tensile strength was 4.31 MPa, and the average pull-out force was 68.45 N. This study provides a basis for low-damage ginger harvesting and for the selection of varieties suitable for mechanized harvesting.

The transportation, distribution, and collision dynamics behaviors of rice grains in the polishing machine critically influence the polishing performance. However, existing research has predominantly concentrated on the polishing mechanisms of laboratory-scale polishing machines, where deviations induced by scaling effects are inherently present. This study utilizes the discrete element method to numerically simulate the operational dynamics of industrial-scale rice polishing systems, with subsequent experimental validation confirming the simulations reliability. The movement characteristics of the particles in the polishing chamber were analyzed, and the influence mechanism of the key components of the sieve on the particle movement was revealed. The simulation results show that as the gap between the polishing roller and the rice sieve increases, the number of particles in the polishing chamber increases, while the axial, tangential and combined velocities of the rice grains as well as their velocity fluctuations decrease, thereby reducing the uniformity of transportation and the collision intensity. Furthermore, influenced by the gravitational force of the rice grains, the movement of the rice grains in the horizontal polishing machine exhibits a certain degree of irregularity. This radial irregularity in the movement promotes the alternation of the rice grains between the inner and outer rings, thereby enhancing the uniformity of the polishing of the rice grains. This research provides theoretical support for the structural design and process optimization of rice polishing equipment.

Accurate soybean field phenotyping is increasingly important for breeding. However, traditional measurement methods are labor-intensive and subjective, while UAV-based approaches are challenged by complex backgrounds and densely distributed small targets. This study first develops UAV-ZSAR to transform oblique UAV images into horizontal-view images and reconstruct plant geometry. A lightweight point-based model, Soy-MOPNet, is then proposed for fast and parallel detection of soybean seeds and stem nodes. The model incorporates the proposed SDConv, optimized hierarchical dilated convolution (HDC) principles, and PBOS to enhance adaptive feature fusion, receptive field design, and multi-branch training stability, respectively. Based on the detected keypoints, six phenotypic traits are extracted in parallel, providing comprehensive support for field phenotyping, breeding selection, and precision agricultural management.

To enhance the mechanization and intelligence of rice seedling cultivation, this study addresses the problems of high labor intensity, low efficiency, and difficulties in on-site monitoring associated with manual tray lifting machines. A remote operational monitoring system for fully automated rigid tray lifting machines based on wireless communication technology was designed and implemented. The system adopts a Browser/Server (B/S) architecture to establish a cloud-based monitoring platform, including the design of a front-end data visualization interface, the development of back-end data processing and storage solutions, and the definition of the monitoring data structure. The platform integrates functions such as chuck alarm monitoring, rice seedling tray counting, and motor status monitoring. It relies on Programmable Logic Controller (PLC) and Data Transfer Unit (DTU) modules to achieve real-time collection and transmission of operational information. Field experiments conducted in three seedling nurseries in Heilongjiang Province, China, showed that the communication data reception rate consistently exceeded 94.87%, reaching a maximum of 100%. The reception probability of chuck alarm information reached 100%, with an average response time of approximately 0.21 s. The system operated stably without causing damage to the rice seedling trays. The results indicate that the proposed system demonstrates excellent stability and real-time performance in agricultural production environments, providing effective technical support for the promotion and application of intelligent rice seedling cultivation equipment.

To determine the optimal water-saving irrigation quota for summer maize in the salinized farmland of the Lupotan area (near Shaanxi Province, Northwest China), the parameters of the SWAP (Soil-Water-Atmosphere-Plant) model were calibrated and validated using field experimental data from 2018 to 2019. The results showed that the simulated values of soil water content, soil salt content, and summer maize yield were in good agreement with the measured values. Under different irrigation scenarios, soil water flux, cumulative soil water flux, soil salt flux, and cumulative soil salt flux at the lower boundary of the crop root zone and storage zone decreased with decreasing irrigation quota. When the irrigation quota was reduced to 70% IQ and 60% IQ, the changes in cumulative soil water flux and cumulative soil salt flux were small. Soil water could be stably stored in the 0–100 cm soil layer to meet the growth requirements of summer maize. When the irrigation quota was 3500 m3·ha-1 (70% IQ), the yield reduction of summer maize was less than 10%. Therefore, 3500 m3·ha-1 was identified as the optimal irrigation quota for summer maize from the perspective of soil water-salt flux and crop yield. The SWAP model can effectively simulate and predict soil water-salt transport and water-saving irrigation quotas for summer maize in salinized farmland. This study provides technical support for the efficient utilization of water resources and guidance for agricultural production practices in Northwest China.

This paper presents a multi-objective optimization approach for unmanned agricultural vehicles operating in complex farmland environments. To overcome the limitations of traditional Model Predictive Control (MPC) and heuristic algorithms, a Dung Beetle Optimization-based MPC (D-MPC) multi-objective optimization method is proposed. Specifically, a kinematic model of the unmanned agricultural vehicle is established, incorporating the operational characteristics of complex farmland conditions. The Dung Beetle Optimization (DBO) algorithm is integrated into the MPC framework to enhance performance by leveraging the population-based search behavior of dung beetles. This integration improves both control accuracy and computational efficiency by dynamically adjusting control inputs based on real-time motion predictions, enabling more precise trajectory optimization. Experimental validation is conducted through a dual-verification approach, including both simulation and real-vehicle tests. The results indicate that, compared with conventional control methods, the proposed approach improves trajectory tracking accuracy by approximately 50% and 75% in two representative simulation scenarios, while increasing the battery State of Charge (SOC) by 0.1% and 0.12%, respectively. In real-vehicle experiments, trajectory tracking accuracy is improved by 70%, and SOC is increased by 0.015%.

To address the lack of accurate and reliable straw models for simulating rice straw cutting, this study uses rice straw as the research object and constructs a discrete element model of straw using EDEM software to calibrate and optimize the bonding parameters. First, the physical parameters of rice straw were measured. For the bonding model parameters, a preliminary experiment was conducted to determine the range of bonding parameters, and the significance of each bonding parameter was analyzed using Plackett-Burman experiments to identify parameters with a significant impact on straw shear force. Then, a steepest climb test was conducted to determine the optimal combination of bonding parameters. Finally, a Box-Behnken simulation experiment was performed on the optimization interval, and a regression equation was established and solved to obtain the optimal parameter combination. The results show that the normal bonding stiffness, tangential bonding stiffness, and bonding radius have a significant impact on the straw shearing model. The optimal combination is 3.092×10¹° Pa, 4.452×10¹° Pa, and 0.496 mm. The established rice straw model is reliable and stable through multiple simulation experiments. This study provides an accurate and efficient method for optimizing the structure and motion parameters of rice straw crushing blades.

Smallholder mechanization in Ethiopia remains low and operationally inefficient, constraining productivity and food security. This study develops and tests a data-driven framework that integrates GIS, Tabu Search, and agronomic scheduling to optimize machinery utilization for the Hitosa Farmers Cooperative Union. A mixed-methods design combined a full census of the unions fleet, GPS field tracking, climate and land-use GIS analysis, CROPWAT-based crop calendars, and time-motion measurements. Optimization as performed using Tabu Search and integer/nonlinear programming implemented in MATLAB and ArcGIS to minimize total travel distance, balance workload, and reduce total operational cost. Spatially differentiated scheduling and route optimization reduced machinery idle time by 30% and produced average fuel savings of 11.28% (23.34% in Hetosa Woreda). According to the assessment, there were 1,230–1,300 hours of inefficient travel and 123,852 ETB in direct labor losses; the estimated revenue losses from combine harvesting and foregone tillage were 2.16 million ETB and 3.33 million ETB, respectively. According to a geospatial demand analysis, 117 tractors (130 HP) or 94 tractors (150 HP) were needed for seedbed preparation, and 137 combine harvesters (220 HP) were estimated to be needed throughout the unions service area. While prioritizing equipment allocation based on crop timeliness and terrain, the updated scheduling and routing decreased the overall distance variance between machines. Cooperative mechanization yields quantifiable economic and environmental benefits when GIS, optimization algorithms, and agronomic calendars are integrated. GPS-enabled devices, centralized logistics, and scheduled fleet replacement are among the priorities; cooperative systems can be compared using the suggested framework to improve service sustainability, dependability, and cost-effectiveness.

As the core component of the tractor operation system, the electro-hydraulic hitch system is the key equipment to realize the implement hitching, lifting adjustment, tillage depth control and adaptive posture control. The hydraulic valve-controlled cylinder system serves as the core control unit of agricultural tractor electro-hydraulic hitch systems. Given that the hydraulic system of tractor hitch devices exhibits strong nonlinearity, uncertainty, and time varying parameters, this study focuses on the valve-controlled cylinder electro-hydraulic servo system and proposes a composite control strategy that integrates linear active disturbance rejection control (LADRC) with parameter optimization via genetic algorithm (GA). This strategy utilizes the linear extended state observer (LESO) of LADRC to estimate and compensate for the total disturbance of the system in real time, while adaptively adjusting the parameters of LADRC using the multi objective optimization characteristics of GA, overcoming the problems of observation lag and gain conflict in traditional trial and error parameter tuning. To verify the control performance, a co-simulation model of the servo valve-controlled cylinder system based on MATLAB-AMESim platform was constructed. The simulation results demonstrate that GA-LADRC achieve significantly superior control performance compared to the PID controller, and in step signal tracking, GA-LADRC reduces both the overshoot and tracking error by more than 50% compared to general LADRC. For sinusoidal signal tracking, GA-LADRC exhibits an 18% reduction in phase lag, an 18.3% improvement in accuracy, and a 34.8% decrease in the integral square error (ISE) compared to general LADRC. Furthermore, under disturbance conditions, GA-LADRC also demonstrates superior anti-interference ability. These results confirm the stability and effectiveness of the proposed GA-LADRC strategy, and the developed method is expected to provide technical support for the fine plowing operation of tractors.

This study established an integrated experimental–simulation framework for optimizing the green-husk dehusking process of macadamia nuts. The objective was to quantify the effects of drum speed, minimum gap, and feed rate on dehusking performance and nut integrity. The nut damage rate was determined by visual inspection, identifying visible cracks or fractures on the nut surface due to the brittleness of the green husk and the hardness of the nut. Mechanical characterization was conducted through static loading tests in three orthogonal orientations to determine the anisotropic fracture behavior of green husks. Discrete element simulations (EDEM) were calibrated using measured friction, restitution, and density parameters and validated through a repose-angle test. A Box–Behnken design with response surface methodology was applied to evaluate interactive effects among process parameters and derive an optimal operating region. The equivalent peeling force of green husks ranged from 0.30 to 2.00 kN. Drum speed was the dominant factor influencing dehusking efficiency (p < 0.01), while minimum gap and feed rate had weaker main effects. The validated model predicted a stable high-performance window at 300–400 r min?¹, 9–10 mm minimum gap, and 6–10 kg min?¹ feed rate. Under optimal conditions (400 r min?¹, 9.5 mm, 6.3 kg min?¹), the measured dehusking rate reached 95.4% with a nut damage rate of 4.76%. These results demonstrate that the combined experimental–DEM (Discrete Element Method) approach provides a reliable basis for parameter tuning and structural design of macadamia dehusking equipment, enabling improved efficiency and reduced nut damage.

Aiming at the problems of poor adaptability and low operational safety of agricultural machinery in hilly and mountainous areas, this study conducts the design and experimental research on a high-precision autonomous positioning system for the multi-crop combined harvester. The Beidou and Inertial Measurement Unit (IMU) high-precision positioning system based on Network Real-Time Kinematic (NRTK) was designed, and the hardware selection and software development of the autonomous positioning system were completed. The NRTK fixed base station in complex field environments was deployed Based on 4G communication, and dynamic differential calculations were performed with the random positions of the machinery to accurately obtain positioning data. Using the STM32L475 chip as the core information processor, efficient processing of real-time position information of combined harvester was realized based on coordinate conversion and attitude error correction of the autonomous positioning system. Performance tests were carried out using a self-developed the combined harvester. The results show that: the positioning error of the designed autonomous positioning system is less than 2 cm; when the harvesters speed ranges from 0.4 to 1.2 m/s, the maximum speed measurement error is less than 0.05 m/s, and the average speed measurement error is approximately 0.014 m/s, which meets the autonomous positioning accuracy requirements of the machinery.

To improve the efficiency and standardization of strawberry production and to address the low automation level and unstable planting quality of existing transplanters, this study developed a fully automatic strawberry transplanter and optimized its core planting mechanism. The machine and planting device were structurally designed based on the biological characteristics of strawberry seedling plugs and agronomic requirements, with the aim of enhancing seedling uprightness and overall operational performance. A coupled DEM–MBD simulation approach was applied to establish a dynamic interaction model among the planter, soil, and seedling plugs within the EDEM–RecurDyn co-simulation environment. Using seedling uprightness as the primary performance indicator, single-factor experiments were conducted to clarify the effects of opening angle, planting depth, and planting frequency on planting quality. A three-factor, three-level Box–Behnken design was then used to optimize the parameters, and an optimal combination of a 27° opening angle, 130 mm planting depth, and 48 plants/min planting frequency was obtained, corresponding to a predicted uprightness of 86.72°. Field validation showed that the planting qualification rate remained consistently above 90% and the coefficient of variation of plant spacing was below 4%, outperforming current operational standards. These results confirmed the efficiency and reliability of the proposed planting mechanism and provided a practical foundation for developing precision transplanting equipment for protected horticultural crops using coupled simulation and parameter optimization.

Agricultural intelligence is an important trend in the development of modern agriculture. However, agricultural UGVs that rely on manual intervention or fixed path planning suffer from low operational efficiency and poor adaptability. To address these issues, an obstacle-avoidance UGV based on an STM32 microcontroller is designed in this study. The UGV uses a motor driver chip for motion control, Bluetooth for signal transmission, and an integrated image transmission module, which effectively improves obstacle recognition and rapid obstacle avoidance compared with conventional agricultural UGVs. Experimental results show that the steering angle control accuracy of the vehicle ranges from 90.0% to 98.9%, while the obstacle detection accuracy ranges from 92.5% to 98.3%. The UGV is able to stably complete obstacle-avoidance tasks under field conditions.

This research aimed to develop a robust crop row detection framework for tobacco fields, where existing methods have not addressed the unique challenges of stratified harvesting, which progressively alters plant morphology from dense lower canopy structures to sparse upper leaf arrangements across multiple collection cycles. CropRowSeg-YOLO (CRS-YOLO), an instance segmentation framework built on YOLOv11, was developed and integrates three core innovations: a Spatial Enhanced Calibration Block (SECB) utilizing depth-wise separable convolutions for spatial feature enhancement; a Hierarchical Adaptive Segmentation Head (HASH) employing asymmetric kernels for long-range dependency capture; and an Enhanced Post-processing Algorithm (EPA) for mask-to-trajectory conversion. Experimental validation in tobacco fields demonstrated 98.4% AP@50 for instance segmentation, with 96.6% precision and 94.2% recall. The model requires 5.1 GFLOPs and 2.7 M parameters, processing individual frames in 2.8 ms, with the complete pipeline executing in 16.99 ms at a resolution of 1920 × 1080.

To address the problems of unstable sowing depth and poor system coordination in corn precision sowing operations, an integrated monitoring and control system was developed. The system achieves closed-loop control of sowing depth by applying controllable downward pressure via a hydraulic circuit, combined with feedback from pin-type pressure and angle sensors. A coupled cooperative controller (SPC-SFMC-X2214A) was implemented to connect the tractor and planter CAN networks, enabling navigation data parsing and fault linkage. A CODESYS-based interface was developed for real-time data visualization and parameter configuration. Field tests showed that at operating speeds of 6–10 km/h, the sowing control error remained =2.00%. The response time of the seeding rate was 0.85 s (for 90–225 kg/hm²), exceeding the design requirement of less than 1 s. The developed system provides an intelligent and adaptive solution for improving the quality of corn precision planting.

To address the low inspection efficiency and limited recognition accuracy in sorghum grain quality assessment for brewing enterprises, this study proposes YOLOv11-SPA, an efficient and real-time detection model based on an improved YOLOv11n architecture. First, the space-to-depth convolution module (SPDConv) is introduced into the backbone network to replace conventional convolution blocks, effectively mitigating the loss of spatial information for small targets caused by downsampling operations. Second, the parallelized patch-aware attention (PPA) module is integrated into the neck network to enhance local feature representation and improve the detection of subtle defect features such as moldy and cracked grains. Third, an adaptive threshold focal loss (ATFL) is proposed to dynamically adjust sample weights, improving the model’s discrimination capability for visually similar categories (e.g., grains with husk residue and intact grains). Experimental results on a self-constructed sorghum seed dataset show that YOLOv11-SPA achieves 80.1% Precision, 79.7% Recall, and 85.9% mAP50, outperforming the baseline YOLOv11n by 5.6, 5.9, and 6.2 percentage points, respectively. With only 3.4 M parameters, the proposed model achieves an inference speed of 205 FPS, meeting real-time detection requirements while maintaining high accuracy. These results demonstrate that YOLOv11-SPA provides an effective solution for automated sorghum grain defect inspection and offers promising potential for intelligent quality control in the modern brewing industry.

Orchard flower thinning is essential for improving fruit quality and stabilizing yield in modern orchards, yet traditional manual and chemical methods no longer satisfy the demands of mechanized orchard management. To address the characteristics of China’s dwarf and dense orchard planting system, a hydraulic-driven vehicle-mounted flower-thinning machine was developed. Dynamic simulations of the flower-thinning process were conducted using ANSYS/LS-DYNA to analyze the effects of thinning shaft rotational speed, traveling speed, and thinning radius on the striking force. A regression model for the striking force was established using single-factor experiments and a three-factor, three-level Box-Behnken design. The results showed that all three factors significantly influenced the striking force, with notable interaction effects. When the thinning radius was 0.4 m, the optimal operating parameters were a rotational speed of 290 r·min?¹ and a traveling speed of 5.4 km·h?¹, corresponding to an effective thinning range of 0.4–0.6 m. Under these conditions, the striking forces obtained from simulation and indoor bench tests were 5.083 N and 5.22 N, respectively, with a relative error of 2.7%. Field experiments further demonstrated an average thinning rate of 37.8% under the optimal parameters, achieving effective thinning while reducing damage to branches and leaves.

To address edge blurring, soil-clod interference, and limited feature representation in potato image segmentation under sticky-soil conditions, this study proposes an improved MTFormer segmentation model. The model combines the complementary strengths of convolutional neural networks and Transformers, and further enhances segmentation performance through a multi-stage optimization strategy. For more informative and robust feature learning, a residual CNN-based extractor is introduced to strengthen multi-level feature representations. In addition, an MS-CAM attention mechanism is used to reduce channel redundancy, which helps mitigate adhesion-related target confusion in challenging scenes. Building on these features, the TBFE module promotes cross-channel feature fusion, while a Fourier-based FFCM structure compresses and reconstructs deep features in the frequency domain to improve feature compactness. Experiments on our self-built dataset show that MTFormer achieves an F1 score of 85.19%, an mIoU of 84.62%, and a pixel accuracy of 95.67%. Compared with the baseline model, U-Net, and DeepLabV3+, pixel accuracy increases by 1.75, 0.35, and 1.67 percentage points, respectively. Overall, the proposed approach improves segmentation reliability by strengthening feature representation while limiting unnecessary computation, providing practical support for accurate potato segmentation in sticky-soil environments.

To address the challenges of high tillage resistance, high energy consumption, and suboptimal subsoiling performance associated with soil compaction management and conventional subsoiling operations, this study integrates gas-liquid two-phase jet soil-breaking technology with ultrasonic vibration drag reduction technology to design and develop an ultrasonic vibration-assisted gas-liquid two-phase jet subsoiler. With a subsoiling depth of 30 cm and an operating speed of 3 km/h selected as standard test conditions, a systematic investigation of the subsoiler’s operational performance was conducted using simulation modeling, soil bin tests, and field experiments. The study examined the variations in tillage resistance, soil disturbance area, and soil porosity. Experimental results indicate that, compared with conventional mechanical subsoiling, the combined application of ultrasonic vibration and gas-liquid two-phase jet reduces tillage resistance by 17.5% and increases the soil disturbance area by 7563.3 mm2. At a soil depth of 30 cm and a horizontal width of 30 cm, the maximum increase in soil porosity reaches 0.115 mm. Comprehensive analysis demonstrates that the ultrasonic vibration-assisted gas-liquid two-phase jet subsoiling technology significantly reduces tillage resistance, enhances soil disturbance, and improves soil pore structure, thereby offering a new technological solution for low-drag, high-efficiency subsoiling operations.

To address the issue of 3D reconstruction information loss caused by occlusion during single-view camera acquisition of crop phenotypic parameters, this study proposes a detection method for tomato plant phenotypic parameters based on multi-view 3D point cloud reconstruction. The Kinect 2.0 sensor was employed to acquire point cloud data of tomato plants from three different viewpoints. Background noise was effectively removed using a combination of Conditional Filtering and Statistical Outlier Removal methods. By extracting surface normal features and calculating Fast Point Feature Histograms (FPFH), the Sample Consensus Initial Alignment (SAC-IA) and Iterative Closest Point (ICP) algorithms were utilized to accomplish coarse and accurate registration of the point clouds, respectively, ultimately achieving 3D reconstruction. Experimental results demonstrated that the reconstructed 3D model of the tomato plant was clear in outline and complete in structure. For the phenotypic parameters of plant height, canopy width, and leaf angle, the coefficients of determination (R²) between the calculated and manually measured values were 0.98, 0.94, and 0.89, respectively, with Root Mean Square Errors (RMSE) of 0.75 cm, 1.10 cm, and 4.43 °. Compared to single-view measurements, the accuracy of plant height and maximum canopy width derived from multi-view reconstruction increased by 15.31% and 13.12%, respectively. This method provides technical support for the rapid and accurate extraction of phenotypic parameters in tomato plants.

To achieve stable abnormal egg detection under edge device deployment conditions, this paper proposes a CSDE-DETR-R18 abnormal egg detection model based on an optimized RT-DETR-R18. The CSFM module is incorporated into the feature fusion path of the RT-DETR-R18 model to aggregate multi-scale features. Subsequently, the models ability to extract low-contrast features is enhanced by replacing the standard convolutions in the backbone network with the DEConv module, which improves its recognition capability for abnormal egg characteristics such as light-colored spots. Finally, the NWD+AIOU mixed loss function is employed to improve the models localization accuracy for minute targets (such as fine cracks, specks, etc.). The experimental results demonstrate that the CSDE-DETR-R18 model achieved mAP@0.5, P, and R of 87.8%, 88.9%, and 87.3%, respectively, representing improvements of 3.4, 3.1, and 0.5 percentage points over RT-DETR-R18. Test results from the Raspberry Pi deployment revealed that CSDE-DETR-R18 demonstrated a significant advantage in inference stability compared with the YOLOv8 series, one of the most advanced detectors currently available. This is of great engineering significance for tasks requiring stable timing performance at equal heights, such as robotic arm sorting.

The millet threshing mixture is the primary material processed during post-combine cleaning operations and mainly comprises components such as seeds, millet panicle clusters, and short stalks. These components exhibit significant differences in material properties, which consequently affect cleaning efficiency. In this study, the morphological dimensions and mechanical parameters of each component in Longgu 31 millet threshing mixtures were systematically measured, including Poisson’s ratio, elastic modulus, shear modulus, angle of repose, static and dynamic friction coefficients, and the coefficient of restitution. The discrete element method (DEM) was employed to establish particle models for millet seeds, millet panicle clusters, and short stalks. Key contact parameters—namely static and dynamic friction coefficients and the coefficient of restitution—were calibrated using the Plackett–Burman design, the steepest ascent method, and the Box–Behnken response surface methodology. The results reveal substantial differences in the physical parameters among the components. The calibrated simulation values of the angle of repose show high consistency with experimental results, with an average error of less than 5%, thereby validating the reliability of the model and parameter system. The robust model and key parameters obtained in this study provide a solid data foundation for elucidating the mechanism of material motion during millet cleaning and for optimizing the design of cleaning and screening equipment. The findings also provide valuable references for simulation studies on the cleaning of small-particle-size minor grains.

This study aims to systematically evaluate the effects of eight distinct photovoltaic mounting systems at the National Photovoltaic Energy Storage Demonstration Platform on the growth performance of Suaeda glauca and soil properties in saline–alkali land. The results showed that the dual-axis tracking support system between panels (SJ) significantly outperformed the other treatments (P<0.05), achieving the highest plant height (36.20 cm), stem diameter (1.84 mm), and leaf width (2.11 mm), as well as higher dry matter accumulation and fresh biomass. In addition, this system exhibited the highest total potassium content (44.36 mg/kg) and the highest D-value (1.30) based on comprehensive membership function evaluation. The application of the dual-axis tracking support system enhances the yield of Suaeda glauca and promotes soil organic matter recovery, thereby achieving the dual objectives of improving photovoltaic system productivity and supporting ecological restoration of halophytic vegetation.

To address the problems of high ear loss rate and kernel breakage during the harvesting process of small-plot corn harvesters, this study optimized the material of the crop gathering chain based on the existing ear-stripping device of the corn harvester header. The rotational speed of the crop gathering chain was determined according to the corn row spacing and the forward operating speed of the harvester. This configuration ensures that when the harvester advances by one row spacing, the teeth of the crop gathering chain move to the next tooth position, thereby reducing material loss and minimizing ear loss. A virtual orthogonal experiment was conducted using three test factors: machine forward speed, crop gathering chain material, and ear-stripping roller rotational speed. The optimal parameter combination was determined as follows: machine forward speed of 0.83 m/s, crop gathering chain material composed of alloy steel + rubber, and ear-stripping roller rotational speed of 260 r/min. Under these conditions, the ear loss rate was 0.568%, effectively ensuring low-loss ear-stripping performance.

This study assesses the impact of agitation speed (150, 300, 450 rpm) and temperature (25°C, 35°C) on the growth kinetics of Saccharomyces cerevisiae cultivated for 46 h in a 3 L stirred-tank bioreactor with malt extract medium. Cultures were inoculated with 2 mL fresh suspension derived from PDA (Potato Dextrose Agar) grown colonies. Cell density was monitored spectrophotometrically, and final biomass quantified gravimetrically (gDW·L?¹). The study demonstrates that the interaction between agitation and temperature governs Saccharomyces cerevisiae growth, with values for specific growth rate (???????? ) ranging from 0.122 h-1 to 0.290 h-1. The condition of 450 rpm at 35°C facilitated optimal oxygen transfer, resulting in the highest biomass concentration of 4.15 gL-1.

Aiming to address the scarcity of semi-continuous experimental studies on chicken manure (CM) and corn stover (CS), as well as the unclear patterns of microbial community changes during digestion, this research employs both sequential batch and semi-continuous experimental methods for investigation. Sequential batch experiments showed that the anaerobic co-digestion (ACoD) of CM and CS had the highest cumulative methane yield (82.45(±0.80) mL/g VS), high Rm and small ?, while the ammonia nitrogen (NH4+-N) concentration generated during the reaction process was below the threshold of inhibitory concentration, indicating that CM and CS were more suitable as an anaerobic digestion (AD) substrate. The semi-continuous experiments showed that the maximum total daily gas production rate of 649.15 mL/gVS was achieved at an organic load rate (OLR) of 0.31 gVS/(L·d). NH4+-N concentration up to 4214.5 mg/L inhibited the semi-continuous flow digestion system. Microbial analysis showed that Firmicutes, Bacteroidota, and Proteobacteria were the dominant phyla throughout the AD experiment. Sedimentibacter, Breznakibacter, Methanosarcina, and Methanobacterium were significantly and positively correlated with methane production and improved the performance of CM treatment with CS. This study analysed the semi-continuous AD of CM and CS under different OLR, which can provide a reference for engineering applications.

This study proposes a solar energy collection and release device based on a matrix pipe system installed on the north wall of a solar greenhouse. The indoor thermal environment was analyzed theoretically, and finite element simulations using ANSYS were conducted, along with comparative experiments at the College of Engineering, Heilongjiang Bayi Agricultural University. The results showed good agreement between theoretical predictions, simulation results, and experimental data. The proposed device significantly enhances greenhouse thermal insulation and solar energy utilization efficiency, providing a useful reference for the application and dissemination of similar thermal storage systems in solar greenhouses.

To enhance the conveying efficiency of screw conveyors, reduce energy consumption during material transport, and improve particle integrity, this study proposes a multi-objective optimization framework integrating the NSGA-II algorithm with entropy-weighted TOPSIS. Discrete Element Method (DEM) simulations, conducted using EDEM software, and an optimal Latin hypercube sampling design were employed to systematically obtain high-fidelity data on mass flow rate and energy consumption under various operating conditions. A surrogate performance model relating key geometric and operational parameters — including pitch, inclination angle, and rotational speed — to mass flow rate and energy consumption was developed using least squares regression. Subsequently, the NSGA-II algorithm was applied to the surrogate model to generate a Pareto-optimal solution set. The entropy-weighted TOPSIS method was then used to rank and identify the optimal compromise solution from the Pareto frontier. Experimental validation of the optimized design demonstrated significant improvements: the mass flow rate increased by 15.77%, energy consumption decreased by 26.16%, and particle degradation was considerably reduced. These results provide practical, data-driven guidance for the rational design and energy-efficient operation of screw conveyors.

The highly similar visual appearance among different cultivated plant species makes fine-grained plant cultivar retrieval a challenging task. Considerable efforts have been devoted to this problem, and significant progress has been achieved in recent decades. This paper proposes a simple and effective method for fine-grained plant cultivar retrieval. The main contributions are threefold. First, experimental analysis indicates that image resolution plays a crucial role in fine-grained plant retrieval, with 896×896 pixels representing the most cost-effective resolution. Second, a radial basis kernel function is employed to capture the nonlinear channel correlation of the feature map, enabling the extraction of more discriminative features. In addition, Log-TiedRank is applied to improve robustness to noise and to obtain a more compact representation. Finally, two types of deep features extracted from two convolutional neural networks are fused to further enhance retrieval performance. Compared with state-of-the-art methods, the proposed approach improves the retrieval rate by 15.71%, 15.95%, 14.02%, and 8% on the SoyCultivar200 dataset, 4.68% on PeanCultivar100, and 4.01% on the Mulberry dataset, demonstrating the effectiveness and superiority of the proposed method.

Crop semantic segmentation plays a crucial role in precision agriculture, enabling applications such as growth monitoring, yield prediction, and pest control. However, deep learning methods, such as U-Net, rely heavily on large amounts of labelled data, which are costly and time-consuming to obtain in agricultural settings. To address this limitation, a semi-supervised maize segmentation method based on an improved Vision Transformer within a student-teacher framework is proposed. The model leverages limited labelled data and abundant unlabelled data through consistency training and confidence-based self-training. Experimental results demonstrate that the proposed method achieves a mean Intersection over Union (mIoU) of 0.661, representing a 14.3% improvement over U-Net. These results confirm its effectiveness in reducing annotation costs while achieving superior accuracy in complex farmland environments.

Rotary tillage in cohesive, salt-affected soils requires significant energy, making the optimization of blade geometry essential to reduce resistance. This study takes inspiration from the digging claw of the brown bear (Ursus arctos) to design a bio-inspired rotary blade and assess its performance using a calibrated discrete element method (DEM) model and soil bin experiments. The DEM model was specifically calibrated for the salt-affected soils of the Yellow River Delta. Key parameters for the study were identified using a Plackett-Burman test and optimized through a central composite design, with experimentally measured draft force as the response variable. The performance of the bio-inspired blade was compared to that of a conventional IT-type blade, focusing on torque demand and soil fragmentation. Simulation results indicated that the bio-inspired blade reduced torque by up to 13% across a rotational speed range of 160-320 RPM and by up to 11% at tillage depths of 60-140 mm. Soil bin tests reinforced these findings, showing that the bio-inspired blade required 17.8% less torque than the IT-type blade. However, the IT-type blade achieved a slightly higher soil fragmentation rate, exceeding the bio-inspired designs performance by 3.9%. This study demonstrates that biomimetic design can significantly reduce energy requirements for rotary tillage while maintaining effective soil fragmentation.

Improper determination of tomato harvest maturity often leads to uneven ripening, overripening, decay, and softening damage during transportation, resulting in substantial postharvest losses. To provide an objective basis for tomato maturity classification and fruit counting, this study developed an improved lightweight model for tomato maturity detection and counting, named CMLE-YOLO. Built upon YOLOv11, the proposed model incorporates a Cross-Fusion and Multi-scale Attention (CFMA) module into the backbone and neck to enhance spatial feature interaction and global context modeling. In addition, a Lightweight Quality-Aware Detection Head (LQAD) head was designed to improve the consistency between classification confidence and localization accuracy while reducing parameter redundancy. A dataset containing 2,000 images and 8,593 annotated tomato instances was constructed for model training and evaluation. Experimental results showed that CMLE-YOLO achieved strong performance in detecting three tomato maturity stages, namely green, half_ripened, and fully_ripened, with a mAP@50 of 0.8508, outperforming several mainstream detectors, including YOLOv5, YOLOv6, and YOLOv8. The model also remained lightweight, with only 2.13 M parameters and 5.2 GFLOPs, indicating lower computational complexity than most comparative models. Overall, CMLE-YOLO achieved a favorable balance between detection accuracy and efficiency, providing technical support for real-time harvesting management, automated grading, and yield estimation in tomato production systems

To determine the optimal harvesting period for mechanized harvesting of Lycium barbarum L. (L. barbarum), fruits from different harvest batches within the same harvesting season were used as the experimental materials, and a comprehensive evaluation was conducted based on fresh-fruit ripeness and damage resistance. Fruit weight, fruit–pedicel detachment force, firmness, soluble solids content, color difference, moisture content, and damage rate were measured on different sampling dates in two consecutive harvest batches (HP1 and HP2). Their variation patterns were analyzed, and a comprehensive evaluation model was established based on correlation analysis and principal component analysis to rank and optimize fruit quality across different sampling dates. The results showed that, with the progression of sampling dates, the ripeness of fresh L. barbarum fruit gradually increased, whereas damage resistance exhibited a stage-dependent variation pattern, and significant correlations were observed among the measured indicators. The dual-index weighted comprehensive evaluation indicated that the optimal harvesting period for both harvest batches was day 7, corresponding to an appropriate harvesting interval of 7 d. These results provide a theoretical basis for determining the harvesting period for mechanized L. barbarum harvesting.

To address issues related to fertilization accuracy and uniformity under field conditions affected by terrain undulations and load fluctuations, an electric precision fertilization control system based on a Siemens S7-200 SMART PLC was developed. The system employs a stepper motor as the actuator and incorporates an incremental encoder with 2,000 pulses per revolution to provide closed-loop speed feedback. A PID parameter optimization method based on differential evolution (DE) is proposed, which performs global optimization using fitness functions defined by tracking error and dynamic performance. Comparative simulations of DE-PID and conventional PID were conducted in MATLAB, followed by field experiments in Dongying City, Shandong Province. The results show that, under conventional PID control, the maximum relative error, average relative error, and coefficient of variation were 4.2%, 3.68%, and 0.36%, respectively, whereas under DE-PID control, these values decreased to 3.2%, 2.92%, and 0.23%, respectively. These findings indicate that the DE-PID strategy effectively improves fertilization accuracy and uniformity, providing a reference for the precise control of external-grooved wheel-type fertilization equipment.

Soils in Chinas Yangtze River Basin are heavy and cohesive. During simulation studies of rotary tillage preparation, obtaining accurate soil fragmentation patterns has been challenging due to the lack of precise soil discrete element parameters. This study calibrated the physical and contact parameters of heavy clay soils using the EDEM discrete element method. Soil density, shear modulus, Poissons ratio, collision recovery coefficient, static friction coefficient, and rolling friction coefficient between soil-soil and soil-tiller components were experimentally determined. Soil penetration tests were conducted using the Hertz-Mindlin with JKR contact model in EDEM software. A Plackett-Burman design identified four parameters significantly influencing soil penetration stiffness: soil-soil collision recovery coefficient, soil-soil static friction coefficient, JKR surface energy, and soil-soil rolling friction coefficient. Building upon this, a second-order regression model linking soil firmness to key parameters was established via Box-Behnken experiments. An optimization algorithm was then employed to determine optimal parameter values, yielding the following combination: soil-soil static friction coefficient 0.441, soil-soil collision recovery coefficient 0.537, and JKR surface energy 9.551 J/m². Validation results demonstrated that under optimal parameters, the simulation error of soil stiffness compared to experimental data was only 3.0%, confirming the accuracy of the calibrated parameters.

To address the problems of low efficiency, strong subjectivity, and high cost associated with traditional tobacco leaf detection methods, an identification model suitable for tobacco leaves was developed to achieve rapid and non-destructive detection and to support the standardization of tobacco production. In this study, the Convolutional Block Attention Module (CBAM) was improved to enhance the feature extraction capability of tobacco leaves and highlight key feature information. MobileNetV3 and a Deformable Convolution Network were integrated to optimize the model structure, thereby reducing the number of parameters and computational complexity. Based on these improvements, a tobacco leaf detection model was constructed. Experimental results showed that the proposed algorithm achieved an accuracy of 97.01% with a loss value of 0.09, outperforming the Multi-Marker Similarity Assessment method and the Gorilla Troop Optimization Algorithm. The constructed model achieved an accuracy of 94.65%, a recall of 91.24%, and an F1 score of 93.54%. The model contains 9.36 M parameters and has a size of 50.69 MB, demonstrating better performance compared with the reference models. The results indicate that the improved tobacco leaf detection model can significantly enhance detection efficiency and accuracy. This study provides a useful approach for precise tobacco leaf detection in complex field environments and contributes to the development of modern intelligent agriculture.

This study focused on Codonopsis pilosula by integrating meteorological and morphological data to establish a meteorology–phenotype correlation model and a three-dimensional (3D) phenotypic structure model. Spearman correlation analysis and an improved three-way K-means clustering method were applied to investigate 3D phenotypic variation and identify correlation thresholds. Based on these analyses, a dynamic 3D growth tracking model was developed to support real-time monitoring of ecological factors and targeted agronomic interventions. Validation using measured data revealed significant relationships between environmental variables and plant growth. Specifically, a 5% increase in relative humidity at 40 cm height (15.2–22.5 °C) corresponded to an approximately 10% increase in root growth. For nitrogen content (0.6–8.0), each unit increase resulted in an approximate 8% increase in root growth. Within the temperature range of 16.94–30.81 °C, each 1 °C increase led to an approximately 7% increase in stem growth. Within the effective range of global horizontal irradiance (GHI, 255–350), each 10-unit increase resulted in a 12% increase in stem growth. For air relative humidity (ARH, 50.3–60.9%), each 5% decrease corresponded to an approximately 6% increase in leaf growth, while for phosphorus content (1.2–9.8), each unit increase led to a 9% increase in leaf growth.

To address the challenges of low precision and limited mechanization in the precision seeding of field-grown Fritillaria, a belt-type precision seeder with metering cells was developed. Based on TRIZ theory, a conical–cylindrical cell with a hemispherical bottom was designed. The belt metering mechanism, driven by an electric motor, operated in coordination with a brush-type seed-retaining plate to achieve precision seeding of Fritillaria. The overall design of the machine was validated using EDEM simulation. Orthogonal experiments were conducted with forward speed, cell diameter, and seed drop height as test factors, and the seeding qualification rate as the evaluation index. The results showed that the optimal parameter combination was a forward speed of 0.88 km/h, a cell diameter of 24 mm, and a seed drop height of 75 mm. Under these conditions, the qualification rate reached 93.16%, meeting the requirements for precision seeding of Fritillaria.

Temperature uniformity (s) during forced-air precooling of fruits and vegetables plays a critical role in preserving quality and extending shelf life. However, inconsistencies in evaluation criteria for calculating s remain a significant challenge. In this study, cherries were selected as the model system, and computational fluid dynamics (CFD) simulations were employed to analyze three commonly used temperature uniformity indices, namely s1, s2 and s3. The results show that s1 is highly sensitive to variations in average temperature during forced-air precooling and becomes unsuitable when the average temperature approaches 0 °C. Moreover, s1 yields relatively large values, often exceeding 1. Both s2 and s3 primarily reflect variations in the temperature range during precooling. However, since s2 incorporates temperature in Kelvin in its denominator, its values are generally below 0.1. In contrast, s3 remains stable within the range of 0 to 1 throughout the precooling process. Overall, the results indicate that s3 is the most suitable index for evaluating temperature uniformity in fruits and vegetables during forced-air precooling.

To address the limitations of traditional water quality detection systems, such as high costs and inadequate real-time performance, a multi-source sensor collaborative monitoring solution tailored for agricultural water environments is proposed in this study. Centered on an STM32F103 microcontroller, the system integrates DS18B20 temperature, infrared turbidity, and composite pH sensors, enabling real-time dynamic monitoring of parameters via a dedicated mobile application. A 24-hour continuous operation test in a static water environment demonstrates that the system can accurately and objectively capture the physical settling and thermodynamic response characteristics of water samples, while maintaining long-term stability without crashes. Operating stably at 5V, the system achieves a rapid data response and refresh cycle of =2s and exhibits excellent measurement accuracy: the pH error is controlled within ±0.3, the temperature error is ±0.5?, the turbidity resolution is 1 NTU, and the overall measurement error remains within ±5%. This system provides crucial real-time data for agricultural non-point source pollution control and offers significant technical value for ensuring water safety irrigation, developing smart agricultural water management, assessing pollution status, and formulating effective pollution mitigation strategies.

Aiming at the low accuracy of wheat kernel segmentation and counting caused by severe adhesion, blurred boundaries and complex field environment in wheat breeding tests, this study proposed an improved High-Resolution Network (HRNet) model optimized by integrating coordinate attention (CA), pyramid pooling module (PPM) and Lovasz-Softmax loss function. A pixel-level labeled wheat kernel segmentation dataset (3600 images, 5 national standard spike types) was constructed based on field samples from the Huang-Huai-Hai winter wheat region. The proposed model achieved a mean Intersection over Union (mIoU) of 88.3% on the laboratory test set, and a counting determination coefficient (R²) of 0.9135. For agricultural engineering application, the trained model was imported into a breeding phenotype analysis workstation, forming a complete application process of "field sampling ? standardized image acquisition ? batch model inference ? automatic counting ? phenotype data output". Field verification showed that the model realized high-throughput counting with an efficiency of 500 spikes per hour (38 times higher than manual counting), and maintained stable accuracy in complex field environments. This method can provide efficient technical support for high-throughput wheat phenotyping detection and precision agriculture.

The accuracy of discrete element simulations for silage corn stover is highly dependent on the precise calibration of model parameters. Addressing the relative scarcity of research on identifying DEM parameters for silage corn stover, this study constructs a simplified DEM model based on the Bonding constitutive model for granular materials. Parameter calibration is performed using experimental data on key physical and mechanical properties of the stover. Using the entire shear history stress-strain curve as the calibration benchmark, Gaussian process regression was introduced as a surrogate model. With mean squared error (MSE) as the objective function, a Bayesian optimization algorithm was employed to accurately identify the bonding parameters of the DEM model. The optimal parameter combination yielding the minimum MSE (MSE = 0.0072) was obtained: normal bonding stiffness, tangential bonding stiffness, normal strength, and shear strength were 5.12 × 10? N/m³, 1.28 × 108 N/m³, 1.60 × 107 Pa, and 2.48 × 106 Pa, respectively. To validate this parameter set, three-point bending tests were conducted and compared with simulation results. The bending stress-strain curves from the discrete element model closely matched experimental trends and peak characteristics, confirming the models accuracy. The proposed Bayesian optimization-based parameter calibration method demonstrates high precision and efficiency. It provides reliable references for discrete element simulations of silage corn stalk processing and the design optimization of key components in harvesting machinery.

To address the lack of reliable discrete element method (DEM) parameters for wheat leveling using grain leveling robots, Wan Ken Mai 22 wheat grains were selected as the research material. Intrinsic and contact parameters – including static friction, coefficient of restitution, and rolling friction – between wheat grains, steel plates, and rubber surfaces were determined through physical experiments. Using the angle of repose as the response variable, Plackett–Burman, steepest ascent, and Box–Behnken experimental designs were employed to identify significant factors, determine optimal parameter ranges, and obtain optimized parameter combinations. The reliability of the calibrated parameters was verified using a t-test comparing simulation and experimental results. The results indicate that the static friction coefficient, coefficient of restitution, and rolling friction coefficient are 0.5023, 0.4700, and 0.1110 for wheat–steel; 0.622, 0.419, and 0.137 for wheat–rubber; and 0.53, 0.52, and 0.04 for wheat–wheat contacts, respectively. The simulated angles of repose (27.57°, 27.25°, and 26.83°) showed no significant difference from the experimental values (P = 0.5485 > 0.05). The calibrated DEM parameters provide a reliable basis for structural design, parameter optimization, and coupled simulation of tracked grain leveling robots.

In rice stubble fields with heavy clay soil and full straw retention, conventional tillage practices often fail to create suitable seedbed conditions for rapeseed blanket seedling transplanting. To address this limitation, a novel integrated seedbed preparation device was developed, incorporating reverse rotary tillage for straw crushing and soil fragmentation, midline ditch clearing with lateral soil displacement, and bed leveling combined with slit cutting. Key components, including the reverse rotary blade arrangement, ditch-cleaning shovel, and leveling–slitting roller, were theoretically designed and parametrically optimized. Taking grid bar spacing and soil-retaining soft curtain distance as experimental factors, and the soil fragmentation rate of the topsoil layer as the evaluation index, a full-factorial experiment was conducted. The optimal structural parameters were determined as a grid bar spacing of 75 mm and a soft curtain distance of 500 mm. Based on response surface methodology, field experiments were carried out using forward speed, rotary shaft speed, and leveling roller speed as factors, and soil fragmentation rate, straw incorporation rate, and surface flatness as evaluation indicators. The optimal operating parameters were identified as a forward speed of 0.8 m/s, a rotary blade speed of 243 r/min, and a leveling roller speed of 180 r/min. Under these conditions, the soil fragmentation rate reached 98.92%, the straw incorporation rate was 87.81%, and the surface flatness was 15.14 mm. The proposed device significantly improves seedbed quality in cohesive, straw-rich paddy fields, providing a reliable technical solution for the mechanized transplanting of rapeseed blanket seedlings.

To address the issues of low seedling extraction success rates and high susceptibility to damage caused by excessive adhesion between the root plug and the tray cell during mechanical transplanting of vegetable tray seedlings, this study designed a vibration-based loosening device according to the principle of inertial force. Using tomato seedlings as the research subject, the range of vibration frequency was determined through single-factor experiments. A three-factor, three-level factorial experiment was conducted, with plug moisture content, vibration frequency, and vibration duration as the experimental factors, and the reduction in adhesive force of the plug as the response indicator. The results of variance analysis showed that the established regression model fitted the actual data well. The order of influence of the factors on the response was: vibration frequency, plug moisture content, and vibration duration. Both the individual factors and their interactions had significant effects on the response indicator. Optimal operating parameters were determined through optimization: plug moisture content of 50%, vibration frequency of 35 Hz, and vibration duration of 60 s. Under these conditions, the reduction in adhesive force reached 1.01 N. The experimental results indicate that vibration-based loosening can significantly reduce the adhesive force between the plug and the tray cell, providing a reference for improving seedling extraction success rates and for designing the seedling-picking mechanisms of transplanters.

The rheological behavior of paddy soil plays a critical role in determining traction resistance, trafficability, and the operational performance of agricultural machinery. This study quantified the effects of moisture content on the steady-state and dynamic rheological properties of remolded paddy soil using a rotational rheometer. Soil samples were prepared at four moisture contents (23%, 26%, 29%, and 32%) and tested under steady shear and oscillatory loading conditions. Steady shear tests (0.1-100 s-1) revealed pronounced shear-thinning behavior, which was well described by a power-law model (n < 1). Within this shear rate range, the apparent viscosity decreased from 11.57-1600 Pa·s at 23% moisture content to 1.343-238.7 Pa·s at 32%. Amplitude sweep tests indicated a transition from solid-like to liquid-like behavior, with the yield strain increasing approximately linearly with moisture content, while the yield modulus decreased. The loss factor increased with strain following a power-law relationship, and the fitted exponent decreased from 0.449 to 0.336 as moisture content increased. Frequency sweep tests identified a crossover frequency of approximately 40 Hz, at which the dominant response shifted from viscous- to elastic-dominated behavior under the test conditions. These results provide quantitative parameters and critical thresholds for understanding the structural stability of paddy soil under cyclic loading, and offer guidance for the optimization of running gear design and anti-slip/anti-sinkage strategies in paddy-field machinery.

Currently, transplanting claws may cause damage to seedling stems or roots during the seedling extraction process. In this study, a dynamic simulation model of the seedling extraction process using transplanting claws was established and analyzed. Foam thickness and width along with the clamping torque were selected as experimental factors, while stem stress and deformation were used as evaluation indicators in a Box–Behnken response surface design. Based on variance analysis of the regression model, the significance of each factor and their interactions was determined, and corresponding regression equations were established. Subsequently, a multi-objective optimization of the regression models was performed using the NSGA-II algorithm, and optimal solutions were identified based on the Pareto frontier. The results show that the optimal parameter combination is obtained at a foam thickness of 6 mm, a foam width of 6.4 mm, and a clamping torque of 0.4 N·m. Under these conditions, the stem stress is 3.1893 MPa and the deformation is 0.8024 mm, which are in close agreement with the predicted values. These findings demonstrate that the combination of the NSGA-II algorithm and the Box–Behnken response surface method is effective for optimizing transplanting claw parameters. However, as this study is based solely on simulation, experimental validation using actual tomato seedlings is required before practical application, which will be addressed in future work.

To meet the agronomic requirements for multiple-seed-per-hill sowing of millet, broomcorn millet, and rapeseed under plastic-film mulching in Northwest China, an air-suction precision seed metering device was designed and evaluated through theoretical analysis and bench testing. The device integrates seed agitation, suction, staged cleaning, conveying, unloading, and placement, and is equipped with ten hill-forming units with a hill spacing of 16 cm. The airflow field and seed motion during operation were theoretically analyzed, and seed trajectories were captured using a 300 fps high-speed imaging system at an operating speed of 3 km/h. By adjusting the inlet negative pressure and suction-hole diameter, the preliminary stable operating ranges were determined. The results showed that the observed processes of seed agitation, adsorption, cleaning, unloading, and detachment were consistent with the proposed seed-motion model. The stable negative-pressure ranges were 0.61–3.54 kPa for millet, 0.83–4.15 kPa for broomcorn millet, and 0.74–3.59 kPa for rapeseed, while the corresponding suction-hole diameter ranges were 0.6–1.2 mm, 0.8–1.4 mm, and 0.7–1.2 mm, respectively. In 1000-hill bench tests, the qualified rates reached 90.6%, 92.7%, and 91.6% for millet, broomcorn millet, and rapeseed, respectively. These results demonstrate the feasibility of the proposed device and provide a basis for further optimization and field validation.

The study aimed to investigate the performance status of technologies developed for tuber crops in order to identify technological gaps and guide improvement, participatory evaluation, scale-up, and distribution. The method used in this study to review the performance status of technologies was based on a secondary data collection approach. The review focused on literature published between 2000 and 2025. The scope of this review is deliberately limited to technologies developed and tested for major tuber crops in Ethiopia, specifically enset, cassava, potato, carrot, and turmeric. Studies were prioritized according to the availability of quantitative performance indicators or evaluation criteria such as capacity, efficiency, loss percentage, and power source. A systematic review was conducted on nine categories of mechanization technologies for tuber crops, namely decorticators, pulverizers, fermenters, washers, polishers, boilers, peelers, graders, and diggers. Decorticators showed processing capacities ranging from 150 to 900 kg h?¹ with efficiencies of 70–88%, mechanical losses of 5–12%, and operating speeds of 400–1,200 rpm. Pulverizers had capacities of 100–800 kg h?¹, efficiencies of 65–90%, product losses of 6–15%, and rotor speeds of 600–1,500 rpm. Polishers operated at capacities of 120–700 kg h?¹ with efficiencies of 68–85%, losses of 6–14%, and speeds of 500–1,100 rpm. Mechanical peelers achieved capacities of 100–1,000 kg h?¹, peeling efficiencies of 65–88%, peel losses of 8–18%, and operating speeds of 350–1,000 rpm. Overall, the reviewed technologies reported general processing capacities ranging from 50 to 1,200 kg h?¹, operational efficiencies of 60–92%, mechanical damage and product losses of 3–18%, and machine operating speeds of 300–1,500 rpm. The review findings indicated that less than 15% of national postharvest research efforts have focused on root crops, while more than 70% have concentrated on grain crops. Nevertheless, recent progress has been observed in enset processing technologies, where performance efficiencies above 85% and capacities up to 800 kg h?¹ have been achieved. The results demonstrate that additional research, development, and investment are required to increase the availability and adoption of improved tuber-crop processing technologies. Based on a review, the effective technologies identified in this study are the corm grater, enset decorticator, turmeric slicer, cassava slicer, and carrot grader, which are recommended for innovative interventions, minor improvements, participatory evaluation, scale-up, and distribution for end-users. The review suggested that future efforts should prioritize user-centered design, gender based, socio-economic feasibility, cost reduction, renewable energy integration, and rigorous multi-location field validation to convert prototypes into widely adopted and sustainable solutions

To address the low speed and limited accuracy of tomato maturity detection in complex greenhouse environments characterized by dense distribution, overlap, and occlusion, this study proposes YOLOv11n-SDS, an improved algorithm based on YOLOv11n. The key enhancements include: (1) a Spatial Pyramid Depthwise Separable Convolution (SPD-Conv) module; (2) the integration of a Deformable Large-Kernel Attention (DLKA) mechanism into the backbone C3K2 module; and (3) a Semantic and Detail Injection (SDI) module replacing the Concat operation in the neck network. These improvements enhance the detection of small and low-resolution targets, as well as occluded fruits under challenging lighting and background conditions. Experimental results show that YOLOv11n-SDS improves mAP@0.5 by 2.4%, recall by 1.3%, and precision by 3.2%, while maintaining a low computational cost of 9.1 GFLOPs. Compared with existing models, including RT-DETR, Faster R-CNN, SSD, and other YOLO variants, the proposed model achieves a superior balance between accuracy, efficiency, and practical applicability. Furthermore, the model was deployed and validated on a mobile robotic platform in a greenhouse environment, enabling real-time tomato maturity detection and 3D target localization. These results demonstrate its strong potential for practical applications, such as ripeness monitoring and harvesting-oriented perception.

This paper systematically reviews the current status, key issues, and future directions of melon harvesting technologies. First, it outlines the industrial value of melons and the efficiency challenges associated with manual harvesting, emphasizing the necessity of developing mechanized harvesting technologies. Second, it summarizes major techniques for melon maturity detection, including non-destructive methods based on spectral, aromatic, and acoustic characteristics, while analyzing the progress in machine vision and artificial intelligence applications for fruit recognition and localization. Furthermore, the study compares the development history and typical models of melon harvesting machinery domestically and internationally, noting that partial commercialization has been achieved abroad, whereas domestic efforts remain focused on prototype design and experimentation. Finally, the paper identifies existing challenges, such as the lack of commercialized maturity detection technologies, low automation levels in harvesting machinery, and insufficient integration of intelligent systems. To address these issues, recommendations are proposed, including promoting multi-technology integration, advancing the development of harvesting robots, and creating specialized equipment tailored for precision cultivation, thereby facilitating the evolution of melon harvesting technologies toward higher efficiency and intelligence.

Efficient monitoring of soil salinity is of significant importance for the utilization and ecological restoration of saline–alkali soils. This study investigates typical saline–alkali soils by employing hyperspectral imaging and machine vision technologies to develop a quantitative prediction model for soil salinity. First, spectral and image data of soil samples were acquired. Subsequently, spectral preprocessing was performed using Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Moving Average smoothing (MA). Characteristic spectral bands were extracted using the Competitive Adaptive Reweighted Sampling (CARS), Variable Combination Population Analysis (VCPA), Iteratively Retaining Informative Variables (IRIV), and a combined VCPA–IRIV approach. Finally, the selected spectral features were fused with image features to establish a Support Vector Regression (SVR) model. The results demonstrated that, compared with single-source data, the SVR model based on feature-level data fusion significantly improved the prediction accuracy of soil salinity. Among the tested models, the SNV + VCPA–IRIV + SVR combination achieved the best performance (Rc² = 0.9889, RMSEC = 0.4790, Rp² = 0.9569, RMSEP = 1.0484, RPD = 3.4423). The results indicate that feature-level data fusion combining hyperspectral and machine vision data can effectively predict soil salinity in saline–alkali soils, while improving model stability compared to single-data-source approaches.

This paper presents a structural analysis of a combined cultivator working tool designed to integrate primary soil cutting and loosening with secondary soil fragmentation within a single pass. A three-dimensional model of the working tool was developed using specialized engineering software, and a numerical analysis based on the finite element method was performed. The structural assessment was carried out by applying a uniformly distributed surface load of up to 0.2 MPa, corresponding to typical contact pressures encountered in moderately compacted soils. The analyses were conducted for different working depths and for three tine geometric profiles - circular, square, and rhomboidal. Structural integrity was evaluated using the von Mises yield criterion, while the factor of safety (FOS) was adopted as the primary indicator of strength reserve. The results show that, for all analysed configurations, the equivalent von Mises stresses remain below the yield strength of normalized AISI 4340 steel, with FOS values ranging from 1.2 to 5.3. Tine geometry was found to have a significant influence on stress distribution and structural response. The rhomboidal profile exhibits the most favourable performance, characterized by lower and more uniformly distributed stresses and reduced draft resistance. The results confirm that the proposed combined cultivator working tool represents a promising solution for modern tillage machinery aimed at reducing field passes and improving soil cultivation quality.

To provide fundamental data for the design and experimental evaluation of pneumatic precision seed metering devices for small-seed crops, the material characteristics of millet, broomcorn millet, and rapeseed were systematically investigated in this study. The moisture content, thousand-seed weight, density, principal dimensions, equivalent diameter, and angle of repose of the three seed types were determined through physical measurements. The Poisson’s ratios of millet, broomcorn millet, and rapeseed were measured using a universal testing machine and were found to be 0.27, 0.33, and 0.29, respectively. In addition, the static and dynamic friction coefficients between each seed type and acrylic, stainless steel, and resin surfaces were obtained. The elastic moduli of millet, broomcorn millet, and rapeseed were measured using a texture analyzer as 351.76, 589.14, and 133.37 MPa, respectively, which were closely related to seed density and structural characteristics. To further clarify the influence of seed–surface collision behavior on seed motion trajectories during the seed metering process, a high-speed imaging test system was established to determine the coefficients of restitution of seeds with moisture contents of 15%, 20%, and 25% after collision with different material surfaces. The results showed that the coefficient of restitution was significantly affected by seed moisture content and decreased markedly with increasing moisture content. Among the three seed types, the coefficient of restitution decreased in the order of broomcorn millet, millet, and rapeseed. For each seed type, the coefficient of restitution decreased sequentially for collisions with acrylic, stainless steel, resin, and the corresponding seed surface. These results provide important data support for the structural design and simulation analysis of pneumatic precision seed metering devices.

The stability limits of weld overlay deposition processes on worn surfaces of steel cylindrical parts from agricultural machinery were investigated. The microstructure of the deposited coatings was analyzed, and the main structural constituents were identified. The microhardness distribution across the coating depth was determined. Furthermore, the influence of key technological parameters on selected mechanical properties of the weld metal was examined. The obtained results enable the determination of optimal deposition regimes for the restoration of worn parts, with the aim of improving their wear resistance and operational reliability.

In response to the difficulties and unsatisfactory performance of existing trenching machines in forming circular trenches for fertilization in citrus orchards, a novel ring-shaped trenching machine designed for around-tree operation was developed. The overall structure and working principle of the machine are presented, and the key components—including the drive system, trenching unit, lifting mechanism of the trenching head, and transmission reducer—were designed and theoretically analyzed. Stability analysis of the machine shows that the limiting overturning angle and slip angle are 53.9° and 34.8°, respectively, indicating that the machine meets the operational requirements of citrus orchard conditions. Considering the soil characteristics of citrus orchards, the optimal working parameters were determined through EDEM simulation: a trench depth of 250 mm, cutter head rotational speed of 190 r/min, and travel speed of 0.565 km/h. Field performance tests conducted on a prototype showed that the average radius of the circular trench was 138 cm, and the trench depth stability coefficient reached 91.8%. The operational performance of the machine meets the agronomic requirements for circular trench fertilization in citrus orchards and complies with the relevant standards for orchard trenching and fertilization equipment.

To optimize the pelleting formulation of Leymus chinensis seeds for mechanized precision sowing, single-factor and three-factor, three-level orthogonal experiments were conducted using CMC as binder and diatomite and talcum powder as fillers. An integrated evaluation system covering pellet uniformity, seed-bearing rate, compressive strength, cracking time, germination rate, and emergence rate was established. Results showed that CMC concentration, filler ratio, and seed-to-powder ratio all affected pelleting performance. The optimal tested formulation was 1.25% CMC, an 8:2 filler ratio, and a 1:4 seed-to-powder ratio, providing a basis for pelleting and mechanized sowing of small forage seeds.

To reduce mechanical damage to wheat grains during planting, harvesting, transportation, storage, and processing, the mechanical properties of ten wheat varieties were investigated. The grain compression process was simulated and analyzed using ANSYS software. Moisture content was selected as a single factor to evaluate its effect on grain mechanical properties. In addition, the chemical composition of the grains, including moisture, starch, protein, fat, and fiber content, was analyzed, and grey relational analysis was conducted to evaluate the relationship between mechanical properties and chemical components. The results showed that the crushing load ranged from 68.09 to 170.51 N, the elastic modulus from 23.88 to 158.46 MPa, and the hardness from 30.60 to 57.86 N/mm. The average moisture, starch, protein, fat, and fiber contents were 13.26%, 56.72%, 12.94%, 2.54%, and 3.81%, respectively. The grey relational analysis indicated that fiber and protein exhibited the strongest correlations with the mechanical properties of wheat grains. The correlation values between crushing load and chemical components ranged from 0.5994 to 0.6888, those for elastic modulus ranged from 0.5877 to 0.7127, and those for hardness ranged from 0.5964 to 0.7192. These findings provide a theoretical basis for the design and optimization of agricultural machinery for wheat harvesting, handling, drying, and processing, as well as for wheat breeding.

To address the challenges of strong nonlinearity, time-varying parameters, and significant external disturbances in wet clutch pressure control during tractor powershift operation, this study proposes an adaptive pressure control method based on Full-Form Dynamic Linearization Model-Free Adaptive Control (FFDL-MFAC). Sobol global sensitivity analysis is employed to identify the key controller parameters, and an Improved Mayfly Algorithm (IMA) is introduced to achieve global optimization. The controller performance is evaluated through co-simulation and hardware-in-the-loop (HIL) testing. The co-simulation results show that, compared with conventional PID control, the FFDL-MFAC controller reduces the rise and fall times in square-wave tracking from 2.63 s and 1.27 s to 0.72 s and 0.52 s, respectively, achieving fully monotonic responses without overshoot or undershoot. In sinusoidal tracking, the maximum pressure error decreases from 0.036 MPa to 0.021 MPa, while the response delay is reduced from 0.07 s to less than 0.005 s. HIL-based clutch pressure control experiments further verify the effectiveness of the optimized controller when implemented on real hardware. Compared with the PID controller, which exhibits a 9.44% overshoot, a settling time of 3.12 s, and a steady-state fluctuation of 0.40 MPa, the FFDL-MFAC controller shows no noticeable overshoot, shortens the settling time to 1.24 s, and maintains minimal steady-state fluctuation. These results confirm the effectiveness and superiority of the proposed method for wet clutch pressure regulation and provide a solid foundation for high-performance control in tractor powershift transmissions.

To address grain breakage and high losses associated with elevated grain moisture during corn harvesting in hilly regions, this study analyzes the threshing process and proposes a flexible threshing device composed of flexible arc-shaped bar teeth and a round steel bar concave screen. Discrete element simulations using EDEM were conducted to evaluate the contact forces between corn ears and the flexible threshing device, in comparison with a conventional rigid device. The aim was to verify the performance of the proposed design. Field comparative tests were subsequently performed to assess the operational performance of both flexible and rigid threshing devices. The results show that, compared with the rigid device, the flexible device reduces kernel breakage by 3.25 percentage points and kernel loss by 2.88 percentage points. These findings provide a useful reference for the design and application of corn kernel harvesters in hilly regions.

As the front-end component of the harvesting header in corn harvesters, the smooth feeding of stalks during the operation of the stalk dividing mechanism plays a crucial role in reducing the harvester’s operating losses. In this study, the mechanical properties of stalks under the action of stalk division were systematically analyzed, and a kinematic model of the stalk-stalk dividing mechanism was established. The adaptability characteristics between the layout form of the stalk dividing mechanism and single-plant stalk feeding were clarified through experimental statistics and theoretical analysis. Based on the key influencing parameters, an orthogonal test scheme was designed and field validation was conducted. A regression prediction model and a response surface model between the feeding success rate and the parameters were constructed. Validation tests were performed using the optimized parameter combination, and the results showed that the single-plant stalk feeding success rate reached 97.65%±0.08%, with the operating loss in the stalk division stage meeting the expected requirements. This study provides a theoretical basis and technical support for the parametric design and layout optimization of the stalk dividing mechanism.

To address the challenges of nonlinearity, significant time delay, and limited control accuracy in conventional water–fertilizer irrigation systems, this study develops a modular control architecture and proposes a fuzzy PID controller optimized using an Improved Whale Migration Algorithm (IWMA). A hardware platform and a dynamic electrical conductivity (EC) model are established, and real-time closed-loop control is implemented via a programmable logic controller (PLC). Simulation and bench-scale experimental results demonstrate that, compared with conventional PID, fuzzy PID, and PSO-optimized fuzzy PID controllers, the proposed IWMA–fuzzy PID strategy significantly improves dynamic performance. Specifically, the peak time is reduced by 15.2%–56.5%, the settling time by 10.0%–53.0%, and the maximum overshoot is reduced by 0.8%–8.1%. In addition, the proposed method achieves higher steady-state accuracy, faster dynamic response, and enhanced robustness. These results demonstrate the effectiveness and practical applicability of the proposed control strategy in intelligent irrigation systems.

This study systematically investigates the key stage of tobacco stalk harvesting within China’s fully mechanized tobacco production system, evaluating mainstream technical approaches and representative equipment worldwide. It identifies that progress in mechanized stalk harvesting is hindered by several challenges, including stratified leaf collection, repeated field operations, and the constraints imposed by ridge-based cultivation systems. The study clarifies the core operational processes (stalk extraction, conveyance, fragmentation, and collection) and summarizes the structural characteristics and recent developments of key domestic components. A comparative analysis of equipment performance and applicable field conditions is also presented. Finally, development recommendations are proposed in accordance with the trend toward integrated mechanization and agronomy, as well as increasing system intelligence, providing a reference for future technological innovation and practical application.

To improve the accuracy and stability of discrete element method (DEM) parameter calibration for soybean seeds under sowing conditions, a soybean seed DEM model was established in EDEM based on the intrinsic mechanical properties of seeds at sowing-stage moisture content. Free-fall collision, inclined-plane sliding, and inclined-plane rolling tests were conducted to determine the initial ranges of the seed–material contact parameters. The experimentally measured angle of repose (23.02°) was selected as the calibration index because it reflects the bulk flowability and inter-particle interaction behavior of soybean seeds. A steepest ascent test was first used to identify the sensitive inter-particle contact parameters, after which response surface methodology (RSM) and a GA-BP-GA framework were employed for optimization. The optimal parameter combination consisted of a coefficient of restitution of 0.27, a static friction coefficient of 0.23, and a rolling friction coefficient of 0.056, yielding a simulated angle of repose of 22.35° with a relative error of 2.91%, lower than that obtained by RSM alone (4.39%). Bench tests further confirmed the reliability of the calibrated model and parameters.

In crop breeding experiments, numerous breeding plots are established, requiring unmanned vehicles to autonomously navigate to designated locations based on breeding material identifiers, for phenotypic data collection. Currently, the identification of navigation target points largely relies on manual measurement. To address this limitation, this study proposes a path planning method for phenotyping vehicles in breeding fields, integrating RGB imagery acquired by unmanned aerial vehicles (UAVs) with an improved A* algorithm. The method begins with UAV image mosaicking and geometric correction. Orthophotos and DSMs are then generated. Regions of interest (ROIs) are then extracted and rectified based on affine transformation principles. The colour space of each ROI is converted from RGB to HSV. Rough locations of breeding plots are identified using an energy function method combined with a Savitzky–Golay filter, and precise plot boundaries are subsequently extracted through further application of the energy function method. This approach achieves an average Intersection over Union (IoU) of 95.17% for individual plots, with maximum and minimum rates of 99.8% and 87.8%, respectively. Navigable areas between breeding plots are derived from the DSM. A theoretical roll angle calculation method is developed by simulating the motion of an unmanned vehicle within the field, allowing traversability weights across these areas to be assessed. The A* algorithm is improved by incorporating jump point search (JPS) and diagonal distance heuristics. The improved algorithm then performs path planning by integrating these traversability weights. Simulation results show that the improved algorithm reduces travel distance by 32.12% and planning time by 72.69% compared with the conventional A* algorithm. Field trials in maize breeding plots further validate that the proposed method, based on DSM and the improved A* algorithm, effectively guides unmanned vehicles to avoid highly uneven terrain and select optimal travel paths.

To promote the application of the discrete element method (DEM) in the research and development of soybean production equipment, reduce development costs, and improve testing efficiency, the contact parameters of soybean seeds in a discrete element environment were calibrated. A discrete element model of soybean seeds was established in EDEM software using a multi-sphere aggregation approach. The coefficient of restitution between soybean seeds and a Q235 steel plate was calibrated to 0.54 using a collision rebound test. The static friction coefficient between soybean seeds and the Q235 steel plate was calibrated to 0.34 through an inclined-plane sliding test, whereas the rolling friction coefficient was calibrated to 0.01 using an inclined-plane rolling test. The angle of repose of soybean seeds was determined to be 31 ° using the steel-box extraction method. Based on a quadratic regression orthogonal rotational combination design, a regression model describing the relationship between the soybean angle of repose and inter-particle contact parameters was established. The model was optimized through analysis of variance, and the optimal combination of inter-particle contact parameters was determined as follows: coefficient of restitution between seeds of 0.65, static friction coefficient between seeds of 0.55, and rolling friction coefficient between seeds of 0.04. Under these parameter conditions, the relative error of the angle of repose between the simulation and bench tests was 0.32%, demonstrating the accuracy of the calibrated contact parameters and the feasibility of the simulation method. The obtained results can provide a reference for selecting soybean seed contact parameters in discrete element simulations.

This study presents a comprehensive investigation of soil penetration resistance and drilling force requirements across major agricultural soil types in Romania. Field measurements were conducted at multiple locations in the western, southern, and northern regions of the country. Using static cone penetrometry and a mechanized drilling system equipped with a 60 mm diameter drill bit, penetration resistance profiles were characterized to a depth of 30 cm, and the corresponding power requirements for soil sampling operations were quantified. The results revealed significant differences in penetration resistance among soil types (p < 0.05), with values ranging from 35 to 2387 kPa. Strong correlations were identified between cone penetration resistance (qc) and drilling torque requirements. The average power demand ranged from 297 to 1861 W, depending on soil type and moisture conditions. These findings provide practical data for equipment sizing and power requirement estimation in precision agriculture applications and contribute to sustainable soil resource management under Romanian pedoclimatic conditions.

To overcome the inefficiency and lack of system coordination caused by the separation of plug seedling sorting and replanting processes, this study designed and developed an intelligent integrated operation system that combines both sorting and replanting functions. The system aims to enhance the overall operational synergy and automation level of plug seedling management. It integrates modules for seedling grading, grasping parameter generation, and transplanting execution, thereby achieving autonomous identification, intelligent grasping, precise replanting, and efficient collection and reuse of weak seedlings. In the grading module, a comparative analysis of YOLO series models was performed, and YOLOv11 was selected for accurate identification of robust and weak seedlings. For the grasping strategy, a lightweight grasping pose parameter prediction network (LRGN) was introduced to generate optimal grasping angles and widths, effectively minimizing physical damage to the seedlings. Experimental results indicated that, for trays with a 4×8 cavity configuration, the recognition accuracy reached 96.0%, sorting success rate 96.67%, replanting success rate 96.0%, and leaf damage rate 2.15%. For trays with a 5×10 configuration, the recognition accuracy was 96.33%, sorting success rate 95.83%, replanting success rate 94.67%, and leaf damage rate 2.88%. The proposed system provides reliable technical support and a practical reference for advancing the intelligent and precise operation of plug seedling cultivation equipment.

In a real-life breeding environment, fine-grained pig behavior detection is of great significance for health assessment, welfare monitoring, and intelligent management. However, issues such as dense target distribution, severe occlusion, subtle inter-class differences, class imbalance, and limited computing resources make it difficult to achieve both detection accuracy and computational efficiency. To address these problems, this paper proposes a lightweight pig behavior detection model WFE-YOLO based on YOLOv11, which is used to identify five typical behaviors: standing, lying down, eating, drinking, and chewing. This method conducts collaborative optimization at three levels: training sample distribution, feature representation, and detection head design. Specifically, a weighted sampling strategy is adopted to enhance the learning sufficiency of low-frequency behaviors; a lightweight gated feature extraction module is introduced to improve the fine-grained representation ability; an efficient detection head is designed to reduce structural redundancy and computational overhead. Experimental results show that on the data set of this paper, the Precision, Recall, and mAP@50 of WFE-YOLO reach 0.8154, 0.7803, and 0.8233 respectively; compared with YOLOv11n, the parameter size is reduced from 2.58M to 1.96M, GFLOPs is reduced from 6.3G to 4.4G, and FPS reaches 520.43. Under the experimental settings adopted in this paper, compared with several mainstream YOLO models, WFE-YOLO demonstrates a better balance between detection performance and model complexity, especially in low-frequency and easily confused behaviors such as Drink and Bite, and has a greater advantage. These results indicate that WFE-YOLO provides a lightweight, application-oriented solution for pig behavior monitoring in complex breeding environments, with strong potential for deployment on edge devices

To address the low efficiency and high labor intensity of manual seedling feeding in semi-automatic transplanters, this study designed a novel circular guide rail type automatic pick-and-place device for the 2ZB-2B transplanter. This novel mechanism adopts a circular guide rail to achieve whole-row seedling picking and ring-distributed planting, solving the problems of structural complexity and low efficiency in existing devices. Through single-factor and response surface methodology optimization, the optimal operating parameters were determined. Under these conditions, the pick-and-place success rate reached 92.29%, and the field transplanting success rate achieved 90.28%, meeting operational requirements. This design provides a practical foundation for the development of fully automatic transplanters.

To address the issues of high damage rates and low success rates in seedling picking due to adhesion between seedlings and pots in the picking mechanism of an automatic transplanter, this study proposes a pre-separation method for pot seedlings based on airflow impact. Unlike the traditional jacking or vibrating-assisted separation method, this method achieves pre-separation through non-contact airflow impact and minimizes mechanical damage to the root-soil composite. Based on this method, a seedling pre-separation mechanism was designed and integrated into the seedling picking mechanism. By establishing a mechanical model of the airflow impact force, the influence of the nozzle aperture and air pressure on the separation effect was revealed. Parameter optimization and functional verification were completed using 50-day-old tomato seedlings. Experimental results showed that the pre-separation success rate of pot seedlings was 93.3% under the optimal combination of a 5 mm nozzle aperture and 125 kPa air pressure. After integration into the seedling pick-up mechanism, the pick-up success rate was 94.4%, with a damage rate of only 1.4%. This study confirmed that air-blowing pre-separation technology can effectively eliminate adhesion between pot seedlings and their pots, significantly reducing damage to seedlings and providing a feasible technical solution for developing a low-damage, automatic transplanter seedling pick-up mechanism.

This study presents a comprehensive investigation of soil penetration resistance and drilling force requirements across major agricultural soil types in Romania. Field measurements were conducted at multiple locations in the western, southern, and northern regions of the country. Using static cone penetrometry and a mechanized drilling system equipped with a 60 mm diameter drill bit, penetration resistance profiles were characterized to a depth of 30 cm, and the corresponding power requirements for soil sampling operations were quantified. The results revealed significant differences in penetration resistance among soil types (p < 0.05), with values ranging from 35 to 2387 kPa. Strong correlations were identified between cone penetration resistance (qc) and drilling torque requirements. The average power demand ranged from 297 to 1861 W, depending on soil type and moisture conditions. These findings provide practical data for equipment sizing and power requirement estimation in precision agriculture applications and contribute to sustainable soil resource management under Romanian pedoclimatic conditions.

The recovery of residual film in the plough layer is one of the key bottlenecks that the cotton industry in Xinjiang urgently needs to overcome. The absence of an accurate soil-residual film coupled EDEM model restricts the analysis of the working mechanism and structural optimization of the soil-film separation device in full-feed residual film recovery machines. This study focuses a 20-year continuous cotton field in Korla, Xinjiang, and constructs a soil-residual film coupled EDEM model. The parameters for soil models at various tillage depths were calibrated by employing a steepest ascent approach followed by a Box-Behnken experimental design, with the angle of repose serving as the response metric. The discrepancy between the simulated and experimentally measured angles of repose was less than 0.22%. The EDEM model parameters for residual film in the plough layer were calibrated based on tensile tests using tensile force and elongation as metrics. The calibration achieved errors of 0.16% in tensile force and 0.934% in elongation compared to experimental measurements. Incorporating the distribution patterns of residual film, a coupled EDEM model of the soil-residual film mixture was developed. This model aims to provide a simulation reference for designing the soil lifting and film-soil separation mechanisms of a full-feed plough layer residual film recovery machine.

To address the challenges posed by severe canopy obstruction in orchards and complex terrain—conditions under which single-GNSS systems frequently lose signal lock, as well as the susceptibility of single-sensor systems to dynamic interference—this paper proposes a multi-source fusion positioning framework based on an adaptive extended Kalman filter (AEKF), integrating GNSS-RTK and 3D LiDAR. To overcome the issue of sparse and discontinuous point cloud features in agricultural environments, a ground segmentation method based on a concentric zone model combined with an improved RANSAC algorithm is developed. This approach enables high-frequency and accurate extraction of orchard row geometric features under complex conditions, including muddy ruts and dynamic human interference, thereby establishing reliable observational constraints for local relative pose estimation. An adaptive observation noise covariance adjustment mechanism based on signal confidence is further proposed. By continuously monitoring RTK quality indicators and accuracy metrics in real time, the system dynamically suppresses unreliable state updates during periods of GNSS signal degradation and seamlessly switches to an error compensation mode based on lateral and heading constraints derived from 3D LiDAR. This effectively mitigates cumulative drift associated with dead reckoning. Experimental results demonstrate that, under challenging conditions involving intermittent canopy gaps and dynamic occlusions, the proposed system achieves a root mean square error (RMSE) of 0.042 m for lateral positioning over the entire trajectory, while the heading RMSE is maintained within 1.85°. The proposed approach effectively addresses the problem of intermittent localization loss in complex orchard environments, providing a robust state estimation framework that enables agricultural robots to operate without reliance on prior mapping, while supporting high-precision global path planning and real-time local obstacle avoidance.

To address the challenges posed by severe canopy obstruction in orchards and complex terrain—conditions under which single-GNSS systems frequently lose signal lock, as well as the susceptibility of single-sensor systems to dynamic interference—this paper proposes a multi-source fusion positioning framework based on an adaptive extended Kalman filter (AEKF), integrating GNSS-RTK and 3D LiDAR. To overcome the issue of sparse and discontinuous point cloud features in agricultural environments, a ground segmentation method based on a concentric zone model combined with an improved RANSAC algorithm is developed. This approach enables high-frequency and accurate extraction of orchard row geometric features under complex conditions, including muddy ruts and dynamic human interference, thereby establishing reliable observational constraints for local relative pose estimation. An adaptive observation noise covariance adjustment mechanism based on signal confidence is further proposed. By continuously monitoring RTK quality indicators and accuracy metrics in real time, the system dynamically suppresses unreliable state updates during periods of GNSS signal degradation and seamlessly switches to an error compensation mode based on lateral and heading constraints derived from 3D LiDAR. This effectively mitigates cumulative drift associated with dead reckoning. Experimental results demonstrate that, under challenging conditions involving intermittent canopy gaps and dynamic occlusions, the proposed system achieves a root mean square error (RMSE) of 0.042 m for lateral positioning over the entire trajectory, while the heading RMSE is maintained within 1.85°. The proposed approach effectively addresses the problem of intermittent localization loss in complex orchard environments, providing a robust state estimation framework that enables agricultural robots to operate without reliance on prior mapping, while supporting high-precision global path planning and real-time local obstacle avoidance.

Aging apple orchards in China are characterized by frequent pest and disease outbreaks, low suitability for mechanized operations, and an imbalance between production inputs and economic returns. In addition, orchard renovation is associated with high labor intensity, a lack of standardized operational procedures, and insufficient specialized equipment, which collectively constrain the green and high-quality development of the apple industry. This study aims to systematically review the technologies and equipment for the renovation of aging apple orchards and to establish a comprehensive technical framework to improve operational efficiency and standardization. Based on literature published between 1995 and 2025, a systematic and structured literature review was conducted with a focus on orchard renovation technologies and mechanized equipment. The results indicate that mechanized renovation technologies can improve operational efficiency by approximately 30%–60% while reducing labor demand by more than 40%. In addition, equipment systems significantly enhance process continuity and standardization. However, limitations remain in terms of adaptability to complex terrain, insufficient integration of technologies, and high operational costs. In conclusion, future development should prioritize intelligent, integrated, and adaptable equipment to support efficient and sustainable orchard renovation. This study provides a systematic reference for optimizing renovation practices and promoting the modernization and high-quality development of the apple industry.

The shear mechanical properties of corn stubble are fundamental to the design of high-efficiency crushing and stubble management equipment. However, the combined effects of moisture content and loading speed on these properties remain insufficiently characterized. In this study, maize root stubble (cv. ‘Heyu 23’) samples were collected and subjected to shear tests using a DDL 300 electronic universal testing machine under three moisture content levels (50–80%) and four loading speeds (5–50 mm/min), with 10 replicates per condition (total n = 120). The coefficient of variation (CV) ranged from 17.6% to 42.3%, indicating moderate to high variability depending on the test conditions. The shear stress exhibited a nonlinear, moisture-dependent response. The maximum shear stress (2.17 MPa) occurred at high moisture content (70–80%) and a medium loading speed (20 mm/min), while the minimum value (1.26 MPa) was observed at moderate moisture content (60–70%) and a low loading speed (10 mm/min). A critical moisture threshold of approximately 65% (fiber saturation point) was identified, below which brittle behavior dominates, and above which viscoelastic and lubricating effects prevail. These findings provide quantitative support for the design and operational parameter optimization of maize root stubble crushing equipment, particularly for cutting–kneading combined mechanisms and bionic stubble cutters.

Permanent grasslands represent one of the most important components of Romania’s agricultural and ecological landscape. Their productivity is influenced by climatic gradients, soil properties, and topographic variation. This study evaluates dry matter production across vegetation zones, and investigates the role of altitude, soil group, and soil pH in explaining productivity differences. A total of 83 sampling sites were distributed proportionally across Romanian vegetation zones. Aboveground biomass was harvested using standardized 1 m² quadrat sampling. Soil samples were collected from 0-10 cm and 10-20 cm depths for classification and pH determination. The adjusted model was composed of 68 samples after excluding underrepresented soil categories. Simple regression analysis showed that altitude significantly influences dry matter production (R²=0.091, p=0.009). The ANOVA analysis model which included soil group and soil pH explained 37.3 % of total variance (R²=0.373, p=0.002). While the soil group did not present a statistically significant independent main effect, interaction patterns suggested that the impact of altitude differs among certain soil types. These findings conclude that altitude is a consistent factor in grassland productivity, while soil characteristics impact productivity responses rather than acting independently.

To elucidate the inhibition mechanisms of sludge-grease anaerobic co-digestion (ACoD) and the microbial community succession patterns, this study systematically investigated the process through an enzyme addition strategy. Results demonstrated that compared with sludge mono-digestion, sludge-grease ACoD significantly enhanced methane yield, with the 1.5% mixed fatty acids group achieving a cumulative methane yield of (666.40±30.71) mL/g VS, representing a 179.4% increase over the control group. An addition of 3% unsaturated long-chain fatty acids (LCFAs) was identified as the inhibitory threshold for anaerobic digestion (AD). Both lipase and PECI (Peroxisomal 3,2-trans-enoyl-CoA isomerase) enzyme addition restored methanogenic activity, with the 1% PECI enzyme treatment group achieving a cumulative methane yield of up to 1627.76 mL/g VS, exhibiting the most effective mitigation. However, the addition of both enzymes showed limited improvement in reducing the lag phase of AD. Microbial community analysis revealed that low concentrations of lipase enriched Synergistetes, thereby facilitating the recovery of methanogenic functions, whereas high concentrations suppressed Synergistetes activity. Low concentrations of PECI enzyme promoted Bacteroidetes to dominate the late-stage degradation, while high concentrations maintained stable Synergistetes metabolic activity, thereby effectively circumventing LCFAs toxicity and acid accumulation risks. These findings provide a theoretical basis and technical reference for the engineering application of AD treating lipid-containing organic wastes.

Soil nitrogen content plays a critical role in fruit tree growth and fruit quality. To achieve high-precision estimation of the three-dimensional spatial distribution of soil nitrogen in peach orchards, this study proposes a hybrid model integrating a Backpropagation (BP) neural network with Regression Kriging (RK). The model is designed to overcome the limitations of traditional single-method approaches when addressing complex spatial nonlinear problems. Using soil data from peach orchards in Fujian Province as the research subject, the predictive performance of five models—BP neural network, Ordinary Kriging (OK), Regression Kriging (RK), Co-Kriging (CK), and the proposed RK-BP hybrid model—was systematically compared. The results indicate that the RK-BP hybrid model outperforms all baseline models, achieving the highest coefficient of determination (R² = 0.97) and the lowest root mean square error (RMSE = 7.0). Compared with the BPNN and RK models, the proposed model improved prediction accuracy by 74.17% and 34.95%, and enhanced precision by 77.29% and 77.46%, respectively. The RK-BP model successfully integrates nonlinear relationship modeling with spatial structural analysis, achieving complementary advantages. This study confirms the effectiveness and great potential of hybrid modeling frameworks combining machine learning and geostatistics for 3D digital soil mapping, offering significant theoretical value and promising prospects for precision agriculture.

Accurate detection of multiple behaviors in group-housed pigs was important for precision livestock farming and intelligent farm management. This study proposed RAFE-DETR, an improved detector based on RT-DETR, for recognizing standing, lying, feeding, drinking, and fighting in overhead surveillance images. RFAConv was embedded into RepViT blocks to construct the RFA-RepViT backbone for stronger local feature extraction. The original intra-scale interaction module was replaced with BiFormer to improve contextual modeling. The neck was redesigned with ASF-CSA to enhance adaptive multi-scale fusion, and Focaler-Shape-IoU was introduced to refine box regression. Experiments were conducted on a five-class dataset reconstructed from public surveillance videos. The proposed model achieved 93.9% precision, 92.7% recall, and 94.2% mean average precision at an intersection-over-union threshold of 0.5. Compared with RT-DETR-L, these values increased by 1.4, 2.8, and 3.0 percentage points, respectively. At the same time, the number of parameters decreased from 32.0 M to 21.9 M, and GFLOPs decreased from 103.5 to 77.0. Supplementary experiments on a second public dataset supported the robustness of the method. Deployment on Jetson Orin NX Super reached 13.8 and 19.1 frames per second under PyTorch and TensorRT, respectively, indicating good edge-deployment potential.

Addressing the issue of high cleaning loss rates encountered during actual combine harvester operations, this study designed a detection device specifically for monitoring cleaning losses. Initially, a three-dimensional model of wheat grains was established using Blender software. Subsequently, the impact processes of wheat grains and straw falling from different heights onto a sensitive plate were simulated using EDEM discrete element analysis software, from which contact force variation curves and motion trajectories were obtained. The results indicated a significant difference in the impact forces of the two material types on the sensitive plate, enabling material identification and loss rate calculation through signal acquisition. Based on these findings, a detection device comprising a mechanical structure and a control system was developed. An ESP32 microcontroller was employed to read data from piezoelectric ceramic vibration sensors. After processing the data with a Kalman filter, material classification thresholds were determined based on the principles of normal distribution. Preliminary experimental parameters were established through a three-factor, three-level experiment, and subsequently optimized using response surface methodology. The experimental results demonstrated that optimal threshold differentiation and the highest accuracy in loss rate calculation were achieved under the following conditions: a sensitive plate installation height of 550 mm, an inclination angle of 40°, and a conveyor belt speed of 8 m/min. Bench tests verified that the overall error of the device was less than 3%, with recognition rates exceeding 97% for both wheat grains and straw.

Downy mildew and powdery mildew pose significant threats to greenhouse cucumber production; however, accurate lesion detection on edge devices remains challenging due to the small size, high density, and frequent occlusion of lesions. This study proposes HSLG-YOLO, a lightweight detector based on YOLOv11n, which integrates CAA-HGNet, MPE-FPN, and TPL-Head for greenhouse cucumber lesion detection and edge deployment. Experimental results on a self-collected dataset show that the model achieves a precision of 93.58%, recall of 91.88%, mAP@0.5 of 96.49%, and mAP@0.5:0.95 of 67.89%. The model size is reduced from 5.3 MB to 4.6 MB, and it achieves 9.777 FPS on the Jetson Orin Nano platform, enabling near-real-time greenhouse disease monitoring.

This study is aimed at addressing the issues of poor conveying stability, seed damage, and high energy consumption in pneumatic seeders by systematically analyzing the impact of pipeline geometry on seed motion characteristics, integrating the coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) simulations with bench tests. The findings demonstrate that a 45 mm pipeline diameter provides the best overall conveying performance, decreasing system pressure loss by 18.8% relative to a 40 mm diameter and increasing system pressure loss by 2.2% relative to a 50 mm diameter. The first straight section of 0.4 meters was found to provide the optimal balance between seed conveying velocity and collision protection during turns. The implementation of an arc-shaped turning structure with a 100 mm radius decreased the seed supply time to 9.48 seconds. This structure also demonstrated notably superior seed trajectory stability, improving by 26.2%, and enhanced flow field uniformity compared to polyline turn structures. This research gives important parametric guidance and theoretical support for the low-damage, high-efficiency design of pneumatic precision seeding systems.

To address the problem of grain residue commonly occurring during the operation of plot wheat breeding harvesters, the CFD–DEM coupling method was employed to investigate the airflow distribution characteristics and material conveying behavior within a pneumatic conveying device. Fan rotational speed, feeding rate, and the curvature radius of the lower conveying pipe were selected as experimental factors for single-factor and response surface experiments. The results showed that all selected factors significantly affected the conveying performance and residue rate. Specifically, the residue rate decreased with increasing fan rotational speed, increased with increasing feeding rate, and decreased as the curvature radius of the lower pipe increased. The response surface analysis indicated that the optimal operating parameters were a fan rotational speed of 2400 r/min, a feeding rate of 0.4 kg/s, and a lower pipe curvature radius of 420 mm, under which the minimum residue rate of 0.021% was achieved.

This study developed an analytical procedure for estimating the mass of carrot foliage residues remaining on the crowns of carrot root crops after cleaning with flexible blades. The proposed model relates the mass removed during a single blade contact to the residence time of the root crown within the cleaning zone, the average effective number of blade contacts, and the number of plants per metre of row. The calculations were performed using representative parameters of carrot root crown geometry, residue height, residue layer density, blade width, blade radius, post-impact contact duration, and plant spacing. A response surface was generated for practical ranges of cleaner forward speed and blade rotational frequency and subsequently approximated using a second-order regression model. The results showed that the mass of remaining residues decreases nonlinearly with increasing blade rotational frequency and increases with higher forward speed because the residence time within the cleaning zone becomes shorter. The most intensive cleaning was achieved by combining lower forward speed with increased blade rotational frequency; therefore, increasing the rotational frequency is advisable only together with an appropriate limitation of forward speed. The developed model provides a practical basis for selecting the operating parameters of a flexible-blade cleaner and explains why flexible blades can compensate more effectively for reduced contact time than technologies based solely on passive or single-contact residue removal.

Aiming at the problems of poor seed distribution uniformity and low sowing accuracy in traditional sowing operations, this paper studies and develops a variable-rate monitoring and control technology system based on an unmanned tractor-towed seeding machine. The system adopts a distributed controller architecture, including a cooperative control ECU, a seed-metering monitoring and control ECU, and a sowing depth monitoring and control ECU, achieving multi-source information interaction and cooperative decision-making through a CAN bus. In terms of hardware, the seed-metering devices electric drive scheme has been improved by replacing the original split-type chain transmission scheme with an integrated torque servo motor, enhancing the system integration and transmission efficiency. Field test results show that the optimized electric drive seed-metering device, at forward speeds of 4 km/h, 8 km/h, and 12 km/h, outperforms the traditional ground wheel-driven method in terms of seed spacing qualification index, standard deviation, and coefficient of variation. Especially under the medium-speed condition of 8 km/h, the performance is optimal, with the coefficient of variation as low as 6.45%. The system demonstrates good robustness and adaptability in complex field environments, providing reliable technical support for achieving precise, efficient, and intelligent sowing operations.

The relevance of the study stems from the need to substantiate the traction-adhesion and traction-speed characteristics of a new type of mobile power unit designed to perform transport and technological operations involving the aggregation of agricultural machinery coupled with tractors of traction classes 1.4–2. The aim of the work was to experimentally determine the influence of mass and transmission operating modes on the traction force, the drive wheel slip coefficient and the speed of the multifunctional mobile power unit “Avtotraktor”. Experimental studies of the machine’s traction characteristics on an asphalt concrete surface were conducted for two MEZ mass variants – without additional load and with installed ballast – using statistical methods for data analysis. Based on the research results, regression relationships were established between the slip coefficient and speed of movement as a function of traction force for various transmission operating modes. It was found that ballasting the “Avtotraktor” significantly reduces slip across the entire range of traction forces and enhances the efficiency of the machine’s traction potential. Rational ranges for the use of first and second gears have been determined depending on the magnitude of the tractive effort. It has been experimentally confirmed that, under ballast conditions and with the correct selection of transmission operating modes, the “Avtotraktor” meets the requirements for mobile power units of tractive classes 1.4–2. The results obtained can be used in the further improvement of the design of “Avtotraktor”-type mobile power units, as well as to justify their operating modes during transport and technological operations in agricultural production.

The growing interest in functional food products in recent years has encouraged producers and researchers to develop new preservation techniques and processing methods that improve product quality. Vacuum impregnation is one such method, representing a non-thermal and non-destructive technology based on the hydrodynamic mechanism. Recently, it has been increasingly applied in the production of high-value food products through the incorporation of bioactive compounds into the food matrix. This study investigates the influence of vacuum impregnation treatment on the chemical composition of apple slices impregnated with apple juice and aronia berry juice, followed by convective dehydration. The results revealed superior values for the aronia-impregnated samples, characterized by increased contents of mineral compounds, dietary fiber, vitamin C, polyphenols, and anthocyanins compared with the apple juice-impregnated variant, which exhibited a less pronounced effect. The performed analyses confirmed that vacuum impregnation significantly influenced the chemical profile of dehydrated apples and demonstrated that this technique can be successfully used as a strategy for obtaining optimized functional food products.

To address the low mechanization level, poor adaptability, and insufficient sowing accuracy of potato planters in the hilly areas of Sichuan, a high-precision intelligent potato planter based on an electric chassis was developed. The integrated design ensures power matching between the electric chassis and the seeding system, while photoelectric and radar sensors enable real-time seed-metering monitoring and missing-seed compensation. A central composite design was used to optimize travel speed and seed-metering linear speed, with qualified spacing rate as the evaluation index. Results revealed a significant interaction between the two parameters. The optimal combination, determined via regression analysis, was 0.36 m/s (travel speed) and 0.23 m/s (seed-metering linear speed), yielding a qualified spacing rate of 98%. This study validates the feasibility and application potential of the electric chassis planter for hilly regions, offering a technical reference for the electrification and intelligent development of potato planting equipment.

Accurate individual identification is essential for precision donkey farming, but conventional methods based on manual records or physical tags are labor-intensive, inefficient, and vulnerable to tag loss or damage. To address these limitations, this study proposes MFW-YOLO11, a non-contact donkey face localization and individual identification model based on an improved YOLO11 framework. A total of 6,531 valid donkey face images were collected from a real farm environment and used to construct an individual identity dataset under diverse conditions, including different illumination, poses, occlusions, and backgrounds. In the proposed network, MANet is introduced into the backbone and head to strengthen fine-grained identity-related features, such as the eyes, nasal bridge, muzzle region, facial contour, and coat texture. A MANet-FasterCGLU composite module is further designed to adaptively filter effective facial responses and suppress interference from railings, donkey bodies, troughs, and complex backgrounds. In addition, a weighted feature union module is embedded in the neck to enhance the adaptive fusion of shallow texture details and deep semantic information. Experimental results show that MFW-YOLO11 achieves a precision of 90.7%, recall of 79.8%, mAP50 of 88.0%, mAP50–95 of 74.4%, FPS of 68.93, and GFLOPs of 6.3. Compared with the original YOLO11, the proposed model improves precision, recall, mAP50, and mAP50–95 by 6.5, 8.8, 6.2, and 6.4 percentage points, respectively, while maintaining real-time inference performance. These results indicate that MFW-YOLO11 provides an effective and practical solution for non-contact donkey individual identification in precision livestock management.

Aging apple orchards in China are characterized by frequent pest and disease outbreaks, low suitability for mechanized operations, and an imbalance between production inputs and economic returns. In addition, orchard renovation is associated with high labor intensity, a lack of standardized operational procedures, and insufficient specialized equipment, which collectively constrain the green and high-quality development of the apple industry. This study aims to systematically review the technologies and equipment for the renovation of aging apple orchards and to establish a comprehensive technical framework to improve operational efficiency and standardization. Based on literature published between 1995 and 2025, a systematic and structured literature review was conducted with a focus on orchard renovation technologies and mechanized equipment. The results indicate that mechanized renovation technologies can improve operational efficiency by approximately 30%–60% while reducing labor demand by more than 40%. In addition, equipment systems significantly enhance process continuity and standardization. However, limitations remain in terms of adaptability to complex terrain, insufficient integration of technologies, and high operational costs. In conclusion, future development should prioritize intelligent, integrated, and adaptable equipment to support efficient and sustainable orchard renovation. This study provides a systematic reference for optimizing renovation practices and promoting the modernization and high-quality development of the apple industry.

To investigate the distribution characteristics of the threshed mixture, threshing and separation experiments were conducted using a self-developed longitudinal axial-flow threshing device with adjustable threshing force, with the adjustable threshing force plates set to the closed mode. The variation patterns of the threshed mixture, grains, stalks, and light impurities along both the axial and radial directions of the threshing drum were obtained experimentally. Based on the experimental data, distribution models of the threshed and separated mixture along the axial and radial directions of the drum were established. The results show that the mass of the threshed mixture beneath the drum accounts for 91.99% of the total feed rate, while the discharged material at the drum outlet accounts for 8.01%. Within the threshed mixture beneath the drum, wheat grains, stalks, and light impurities account for 61.48%, 26.22%, and 12.30% of the total mass, respectively. Based on these findings, the structure of the threshing device was optimized. In the axial range of 1020 ~ 1700 mm along the threshing drum, the grain crushing rate was reduced from 1.16 ~ 12.5% to 1.05 ~ 7.5%. These results provide a theoretical basis and technical reference for the structural design and parameter optimization of threshing, separation, and cleaning systems, contributing to reduced energy consumption and improved operational performance.

Sea buckthorn (Hippophae rhamnoides) is an important tree species for ecological restoration and the specialty forest-fruit industry in northern China. Its fruits possess high nutritional and economic value; however, harvesting operations still rely predominantly on manual labor because of the small fruit size, short pedicels, dense fruit clustering, thorny branches, and thin skin that is highly susceptible to mechanical damage. These limitations result in low harvesting efficiency and constitute a major bottleneck restricting large-scale industrial development. To systematically clarify these challenges and support the development of effective mechanized solutions, this review summarizes the physical and mechanical properties of sea buckthorn fruits and the agronomic requirements associated with mechanized harvesting. Furthermore, from the perspective of technological evolution, the study analyses the operating principles, research progress, and applicability of the main harvesting methods, including vibration-based harvesting, pneumatic harvesting, pruning-based harvesting, and whole branch-fruit harvesting systems, together with subsequent fruit detachment, cleaning, and sorting operations. The reviewed studies indicate that international research has primarily focused on cultivars suitable for mechanized harvesting, leading to technological approaches centered either on direct field harvesting or on post-harvest processing after whole branch-fruit collection. In China, significant progress has been achieved in optimizing vibration parameters for fruit detachment, developing low-damage cutting and conveying mechanisms, and improving freezing-assisted fruit detachment, cleaning, and sorting technologies. Nevertheless, several major challenges remain, including the limited availability of cultivars and planting systems suitable for mechanization, insufficient integration between agronomic practices and machinery design, and the relatively low level of intelligence and automation of harvesting equipment. Future research should therefore focus on the breeding of mechanization-oriented cultivars, the establishment of standardized cultivation systems, the development of low-damage and high-efficiency harvesting components, and the advancement of integrated intelligent harvesting equipment, thereby supporting technological innovation and the industrial application of mechanized sea buckthorn harvesting in China.

Sea buckthorn (Hippophae rhamnoides) is an important tree species for ecological restoration and the specialty forest-fruit industry in northern China. Its fruits possess high nutritional and economic value; however, harvesting operations still rely predominantly on manual labor because of the small fruit size, short pedicels, dense fruit clustering, thorny branches, and thin skin that is highly susceptible to mechanical damage. These limitations result in low harvesting efficiency and constitute a major bottleneck restricting large-scale industrial development. To systematically clarify these challenges and support the development of effective mechanized solutions, this review summarizes the physical and mechanical properties of sea buckthorn fruits and the agronomic requirements associated with mechanized harvesting. Furthermore, from the perspective of technological evolution, the study analyses the operating principles, research progress, and applicability of the main harvesting methods, including vibration-based harvesting, pneumatic harvesting, pruning-based harvesting, and whole branch-fruit harvesting systems, together with subsequent fruit detachment, cleaning, and sorting operations. The reviewed studies indicate that international research has primarily focused on cultivars suitable for mechanized harvesting, leading to technological approaches centered either on direct field harvesting or on post-harvest processing after whole branch-fruit collection. In China, significant progress has been achieved in optimizing vibration parameters for fruit detachment, developing low-damage cutting and conveying mechanisms, and improving freezing-assisted fruit detachment, cleaning, and sorting technologies. Nevertheless, several major challenges remain, including the limited availability of cultivars and planting systems suitable for mechanization, insufficient integration between agronomic practices and machinery design, and the relatively low level of intelligence and automation of harvesting equipment. Future research should therefore focus on the breeding of mechanization-oriented cultivars, the establishment of standardized cultivation systems, the development of low-damage and high-efficiency harvesting components, and the advancement of integrated intelligent harvesting equipment, thereby supporting technological innovation and the industrial application of mechanized sea buckthorn harvesting in China.