This study proposes an artificial intelligence-based method for the automatic identification of natural groups of mechanical behaviour in rose hip seeds (Rosa canina). The method combines compression testing with unsupervised machine learning techniques, namely Principal Component Analysis (PCA) and K-means clustering, while creep tests are analysed separately to assess the time-dependent mechanical behaviour of the seeds. This approach enables the identification of distinct mechanical patterns and the objective classification of samples according to their response to mechanical loading. The unsupervised multivariate analysis was performed using mechanical features extracted from compression tests conducted on 25 individual rose hip seeds (n = 25), with each individual compression test generating a force–displacement dataset comprising 934 experimental data points. Each cluster was analysed separately in terms of the experimentally determined mechanical parameters. For each group, the mean values of maximum compression force (Fmax), displacement at maximum force (xmax), deformation energy, and initial stiffness were calculated. The results revealed the existence of two main categories of mechanical behaviour. The first group was characterized by an average maximum force of approximately 141 N and a displacement at maximum force of 0.73 mm, indicating seeds with high mechanical strength. The second group exhibited an average maximum force of approximately 75 N and a displacement at maximum force of 0.85 mm, corresponding to seeds that were more deformable and less resistant to compression. The seeds were tested at a moisture content of 2.79% (wet basis), determined thermo-gravimetrically. To validate cluster separation and reduce the dimensionality of the dataset, Principal Component Analysis (PCA) was applied. The quality of clustering was assessed using the silhouette index, which indicated a satisfactory separation of samples according to their mechanical response. PCA revealed that the main differences between clusters were associated with maximum force, absorbed energy, and initial stiffness values, confirming the existence of distinct categories of mechanical behaviour within the analysed seed batch.

Efficient classification of tobacco coated seeds is critical for seed quality assurance in the tobacco industry, yet the nearly identical appearance of coated seeds renders manual sorting labor-intensive and unreliable. Existing automated solutions either lack the precision required for distinguishing sub-millimeter size differences or are too computationally demanding for deployment on edge devices in seed processing facilities. To bridge this gap, this study proposes a lightweight classification model, EMC-YOLO, based on an improved YOLOv10n. This model optimizes YOLOv10n in three aspects: first, replacing the original backbone network with EfficientViT, balancing low computational requirements and global feature extraction capabilities; second, introducing an improved multi-scale spatial pyramid attention (MSPA) into the neck network, utilizing parallel dilated convolution to optimize feature fusion; third, employing the CARAFE operator for upsampling to enhance the edge restoration effect of small targets. Experiments show that the precision, recall, and mAP@0.5 of EMC-YOLO reach 95.12%, 91.20%, and 93.01%, respectively, representing improvements of 2.12, 1.20, and 1.01 percentage points over the original model. Simultaneously, the model reduces the number of parameters by 71%, computational load by 75%, and weight volume by 68.1%. This model achieves high-performance detection with low resource consumption, providing a technical reference for automated seed quality inspection. Validation on a designed seed detection platform under simulated production conditions yielded a coating qualification rate of 95.50%, meeting the 95% industrial benchmark, and confirming the practical feasibility of the proposed method for automated quality inspection in tobacco seed processing lines.

Aeroponic cultivation has emerged as a promising technology for improving potato minitubers production under controlled conditions. This study evaluated the effects of three cultivation systems (aeroponics, hydroponics, and conventional cultivation) and four potato genotypes (Azaria, Brasovia, Cosiana and Cezarina) on plant growth and minitubers production. Plant height, number of minitubers per plant and minitubers weight per plant were assessed. Analysis of variance reveals a statistically significant effect of the cultivation method on plant height at one month (p < 0.01) and a highly significant effect on plant height at two months, as well as on the number and weight of minitubers per plant (p < 0.001). The variety has a statistically significant effect on plant height at one month (p < 0.01) and a highly significant effect on the other parameters analyzed (p < 0.001). The interaction between cultivation method and variety is insignificant for plant height at one month (p > 0.05), but becomes highly significant for plant height at two months and for production traits (p < 0.001), indicating a differentiated response of the varieties to the conditions specific to each cultivation method. Hydroponic cultivation stimulated early plant growth one month after transplanting, whereas aeroponic cultivation resulted in the greatest plant height two months after transplanting. The aeroponic system also produced the highest number of minitubers (37.26 per plant) and the greatest minitubers weight (357.11 g per plant), significantly exceeding both hydroponic and conventional systems. Among the evaluated genotypes, Cosiana, Brasovia and Azaria showed superior minitubers production compared with control cultivar Cezarina. The results demonstrate the high potential of aeroponic cultivation for efficient potato minitubers production and support the implementation of advanced soilless technologies.

To enable automatic and precise flower transplanting, a soil pickup and separation device was designed for substrate filling in greenhouse flower transplanting operations. The soil-conveying mechanism of the soil pickup auger was analyzed through theoretical analysis and discrete element simulation. Taking the soil pickup quantity and its coefficient of variation as the evaluation indices, the effects of auger rotational speed, soil penetration depth, and pitch were investigated and optimized using Box-Behnken response surface methodology. The simulation results showed that the optimal operating parameters were an auger rotational speed of 115.7 rpm, a soil penetration depth of 39.28 mm, and a pitch of 149.26 mm. Based on the optimized parameters, a prototype was fabricated and field tests were conducted. The measured soil pickup quantity was 4.13 kg, and the corresponding coefficient of variation was 1.44%, with relative errors of 3.05% and 4.00%, respectively, compared with the simulation results. The validation results demonstrated that the device operated stably and could achieve accurate and efficient substrate filling.

Temporary grasslands are recognized both for their ecological functions, such as nutrient cycling, soil quality, and also for their potential role within crop rotation frameworks. While many studies have shown the agronomic and environmental benefits of permanent grasslands, fewer have analysed the impact of temporary grassland phases as precursors, influencing coming crops in terms of system stability or yield. Results are often mixed or contradictory in the academic literature, due to methodological limitations or insufficient statistical power. The current long-term study (2008-2014) investigates the role of grassland as a precursor to suitable arable crops (potatoes, spring wheat, silage maize and forage turnips), on a rotation-based empiric design, focusing on temporal variability, yield fluctuations and the broader agronomical context in which these changes occur. Using multi-year data, we look at crop yield performance, and discuss results offering suggestions for future research. To account for structural yield differences among crops, yields were standardized within crop categories, allowing for a unified analytical framework. Results from factorial ANOVA models indicate that grassland phases contribute to yield stabilization rather than a uniform increase in production of other crops, with outcomes influenced by crop type and stationary area conditions. Our results suggest that grasslands have the potential to enhance system resilience and are valuable for sustainable crop production under increased environmental uncertainty.

To prevent mechanical damage to tree trunks during cultivation of orchard inter-row and near-trunk areas, agrotechnical requirements prescribe leaving a certain uncultivated soil area around each trunk as a plant protection zone. When the working unit is periodically displaced relative to the tree-row axis by forced motion, an additional untreated soil area remains outside the plant protection zone. Its size is influenced by several factors, including the diameters of the rotary tiller and tree trunk, the lengths of the swing arm carrying the tiller and the sensing probe that actuates the hydraulic system, the tractor travel speed, and the piston-rod velocities of the hydraulic cylinder. Among these factors, the present study considers the principal kinematic factor: the influence of the backward motion of the hydraulic-cylinder piston rod toward the inter-row space and its forward motion toward the tree row, which provide and regulate the transverse displacement of the tiller relative to the tree-row axis, on the size of the untreated soil area. Using a grapho-analytical method for drive mechanism analysis, the equations of motion of the vertical-axis rotary tiller during tree-trunk bypass were derived for the backward motion of the hydraulic-cylinder piston toward the inter-row space and the forward motion toward the tree row. The resulting motion trajectories are cycloids. To characterize them, the concept of a bypass kinematic parameter was introduced: ??=????????/, representing the ratio of the circumferential velocity of the tiller’s transverse-radial motion to the tractor forward velocity. The size, shape, and position of the untreated soil area outside the plant protection zone relative to the tree trunk are determined by the value of the bypass kinematic parameter, which, for a given tractor speed, depends on the backward and forward velocities of the hydraulic-cylinder piston rod. It was established that, for given structural and kinematic parameters of the tillage machine, regulating the volume of oil supplied to the hydraulic cylinder makes it possible to minimize the untreated soil area outside the plant protection zone around the tree trunk.

To address the low quality of mechanized wheat seeding in rice-stubble fields in the middle and lower reaches of the Yangtze River, caused by high soil moisture content and heavy straw coverage, a press-groove uniform seeding machine was designed. Key mechanisms for rotary tillage, precision seedbed preparation, depth-controlled furrow formation, and banded soil covering were developed, and the overall transmission system and blade-roller arrangement were designed to create a wheat seeder suitable for high-moisture rice-stubble fields. Field tests showed that the vegetation coverage rate after operation was 94.2%, the qualified rate of seeding depth was 98.0%, and the coefficient of variation of seeding uniformity was 18.1%. The machine effectively reduced seed skips and exposed seeds during mechanized seeding in high-moisture fields.

To improve the seed-filling performance of pneumatic precision seed metering devices for small-seeded crops, a U-shaped seed agitation plate and a combined-type agitation plate were developed. The agitation performance of cylindrical, U-shaped, and combined-type agitation plates was evaluated using the average and maximum velocities of the seed population as quantitative indicators. A three-factor, three-level simulation experiment based on the Discrete Element Method (DEM) was conducted, with agitation plate type, rotational speed, and knob height as the main factors. The optimal parameter combinations were determined as follows: for millet, a combined-type agitation plate with a rotational speed of 24.85 r/min and a knob height of 8.36 mm; for broomcorn millet, 24.17 r/min and 7.96 mm; and for rapeseed, 22.93 r/min and 8.57 mm. Bench experiments were carried out to validate the optimized combined-type agitation plate. The seed-filling rates reached 99.3%, 100%, and 99.7% for millet, broomcorn millet, and rapeseed, respectively. The results indicate that the combined-type agitation plate significantly improves seed mobility and filling performance, providing an effective solution for precision seeding of small-seeded crops.

To enhance the seeding accuracy of multi-seed-per-hole operations for small-seed crops, a combined experimental-CFD optimization method for an air-suction seed metering plate was developed. Single-factor tests established that the suitable suction-hole diameter was 0.4-0.6 times the equivalent seed diameter, and threshold-based negative-pressure tests were used to define the acceptable suction-hole negative-pressure range for millet, broomcorn millet, and rapeseed. CFD pressure-tracing analysis was then performed to quantify the mapping between suction-hole pressure and air-chamber inlet negative pressure. On this basis, a three-factor, three-level optimization test was conducted using inlet negative pressure, suction-hole diameter, and suction-hole thickness as variables, with the qualification rate of suction-hole pressure as the response. The optimized parameter combinations were 2.51 kPa, 0.88 mm, and 0.31 mm for millet; 2.97 kPa, 1.12 mm, and 0.35 mm for broomcorn millet; and 2.35 kPa, 0.93 mm, and 0.32 mm for rapeseed. Bench validation showed seed-filling rates of 93%, 97%, and 96%, respectively. The results demonstrate that the proposed method can effectively reduce empirical trial workload and provide a practical basis for the design of pneumatic precision seed metering plates for small-seed crops.

To address the challenges of labor-intensive and inefficient manual harvesting of Lycium barbarum L. grown on a double-layer trellis, this study proposes a human–robot collaborative system. The system consists of a hand-held high-frequency rocker-oscillator harvester and an electric receiving cart. Mechanical property measurements of fruits and branches revealed detachment forces of 1.2 to 1.8 N for mature fruits and 2.5 to 3.0 N for immature fruits, with an average bending elastic modulus of 548.9 MPa for fruit-bearing branches, providing a basis for selective harvesting. A Box–Behnken design was employed to optimize vibration frequency, harvesting duration, and inter-rod spacing, and response surface analysis identified the optimal parameters as 18 Hz, 4.5 s, and 26 mm. Next, a central composite design optimized the receiving cloth tilt angle and radius to maximize collection efficiency, defined as the ratio of fruits collected by the cart to the total number of detached fruits. Field validation demonstrated a mature fruit harvesting rate of 96.2%, a mis-harvesting rate of 4.3%, a damage rate of 3.7%, and a collection efficiency of 96.1%, with relative errors below 0.5% compared to model predictions. These results confirm the system’s effectiveness and provide technical support for standardized, low-damage mechanized fruit harvesting of Lycium barbarum L..

The power take-off (PTO) shaft of a tractor is subjected to complex and variable random loads during field operations, and the accuracy of the resulting load spectrum directly affects the reliability of fatigue life prediction. To address the limitations of traditional parametric extrapolation methods, particularly their inadequate fitting performance, a non-parametric extrapolation method based on DBSCAN clustering is proposed. Field test validation demonstrates that the proposed method effectively captures the multi-modal distribution characteristics of the load. The extrapolated results show good agreement with the measured data in terms of cycle count distribution and pseudo-damage indicators. Compared with the fixed-bandwidth method, the proposed approach increases the coefficient of determination by 3.401% and reduces the mean squared error (MSE) by 39.154%, while yielding pseudo-damage values closer to 1. These findings provide a novel technical approach for the construction of load spectra under complex operating conditions of agricultural machinery.

In addressing the high labor costs and low operational efficiency of greenhouse tomato harvesting and separate packaging workflows, this study develops an integrated tomato harvesting robotic system embedded with RGB-D machine vision, 5-degree-of-freedom manipulator and vertical heat-sealing net bag packaging mechanism. The YOLOv8s lightweight detection model trained on self-built multi-light greenhouse tomato dataset (1260 annotated images covering unobstructed, semi-occluded and heavily occluded fruits) is adopted to identify ripe tomatoes with a recognition accuracy of 95.2%, and ImageJ software is introduced to conduct secondary maturity screening via RGB chromatographic analysis. A* global path planning combined with TEB local trajectory optimization realizes autonomous obstacle avoidance navigation of the wheeled mobile platform, while RRT-Connect bidirectional random tree algorithm is applied for obstacle-free grasping trajectory planning inside dense tomato canopies. A total of 120 valid cyclic tests are carried out in simulated greenhouse environment to verify the full-chain automation including fruit detection, in-situ picking and instant bagging. Experimental results show that the average single-fruit processing cycle is 12.1 s, with a picking success rate of 90.8% and bagging success rate of 98.3%. Compared with skilled manual picking and packaging, the overall working efficiency is improved by approximately 30%. This system firstly realizes continuous integrated harvesting and commercial packaging operation for greenhouse tomatoes, providing a feasible technical solution for full-process intelligent protected agriculture.

This paper presents a theoretical framework for estimating the gripping force of a three-degree-of-freedom robotic arm mounted on a remotely operated underwater vehicle, intended for sampling operations in intensive aquaculture systems. Using the static relationship based on the Jacobian transpose, the proposed method reconstructs the force and moment vectors acting at the end-effector from the estimated joint torques. The gripping force is determined by projecting the Cartesian force onto the normal directions of a two-finger gripper. Transmission efficiency and joint friction are incorporated into the model through the definition of an effective torque. The influence of hydrodynamic forces is treated as an external disturbance during quasi-static manipulation. The internal consistency of the model is evaluated through a numerical validation based on representative design parameters for compact underwater manipulators. The analysis indicates that a gripping force of approximately 150–200 N satisfies the friction-based stability condition for typical sampling loads of approximately 100 N. Furthermore, the resulting joint torques remain within the realistic operating limits of actuators commonly used in ROV-mounted manipulators. The findings confirm the dimensional consistency and physical plausibility of the proposed estimation framework, which may be applied to assisted underwater force estimation in aquaculture applications.

To promote the mechanization of the tobacco industry, a self-propelled tobacco stem harvester was designed in this study. To improve harvesting efficiency under varying terrain and planting conditions, the machine integrates key functional components, including a chain-clamp conveyor and a loosening shovel with optimized structural parameters. The performance of these core components was analysed using 3D modelling and finite element simulations conducted in ANSYS. Field experiment optimization indicated that the optimal operating parameters were a clamping chain speed of 0.7 m/s, a forward travel speed of 0.4 m/s, and a clamping chain gap of 12 mm. Experimental results showed that the harvester achieved a leakage rate of 3% and a breakage rate of 7%. Overall, the machine demonstrated reliable performance and satisfied practical operational requirements. This study provides an effective technical solution for the mechanization of tobacco stem harvesting, contributing to reduced production costs and the advancement of agricultural modernization.

To address the issue of uneven fracture during the processing of Astragalus slices, this study first establishes a discrete element Bonding model for Astragalus roots, filling the gap in the calibration of its bonding parameters. Through uniaxial compression tests and multiple optimization experiments, the optimal parameters were determined, with simulation errors below 1.6% and validation errors for the fracture rate = 12.7%, providing theoretical support for the development of processing equipment for Astragalus slices.

To address the problems of low bale-forming rate and loose compaction during the mechanized recovery of residual plastic film in farmland, a three-stage residual film baling device was developed. Based on the discrete element method (DEM), a flexible thin-shell model of the residual film was established, and dynamic simulation and parameter optimization of the baling process were carried out using Rocky DEM software. Combined with single-factor experiments and response surface methodology, the effects of baling chamber inclination angle, belt type, belt linear speed, and upper belt inclination angle on bale integrity and compaction were analyzed. The results showed that the bale-forming rate of the three-stage device reached 99.13%, and the bale density reached 83.75 kg/m³ under the optimal conditions: baling chamber inclination angle of 30°, upper belt inclination angle of 30°, belt linear speed of 2.3 m/s, and the use of a rough-surface belt. Field validation tests showed that the average density of the residual film bales was 91.4 kg/m³, with a prediction error of only 2.23 kg/m³ compared with the simulation results. The device operated stably and reliably. This study presents a high-efficiency residual film baling device with improved bale-forming rate and compaction performance through structural optimization and parameter tuning, providing technical support for efficient recovery and pollution control of residual plastic film in farmland.

Agricultural power networks accelerate contact wear and reduce the accuracy of conventional loop resistance testing. This study presents an APP-based wireless three-phase synchronous measurement system integrating UC3854-driven multi-stage power factor correction (0–100 A, 82–83.17% efficiency, PF 0.99) and a supercapacitor pulse module (16 V trigger, 230 V charge). The system achieves 0.01 µO resolution and >10,000 MO insulation resistance. Field validation on 110 kV GIS reduced wiring and testing times by 67% (=1% deviation), while 500 kV GIL measurements improved efficiency by 80%. The platform enables high-current stability, strong interference immunity, and digitalized operation.

Traditional uniform fertilization in rice paddy fields is associated with low fertilizer-use efficiency and severe agricultural non-point source pollution. Existing variable-rate fertilization equipment commonly presents limitations such as reliance on single-source prescription information and insufficient control precision. To address these issues, this study uses an impeller-type fertilizer applicator as the working platform and, considering the characteristics of cold-region rice cultivation in Heilongjiang Province, constructs an integrated ternary nitrogen-phosphorus-potassium fertilization prescription map. A mapping model between fertilizer application rate and motor speed is established, and a fuzzy PID controller optimized using an improved whale optimization algorithm (IWOA) is designed. Fertilizer-discharge calibration tests and field trials were conducted. The results showed that the coefficient of determination (R²) between fertilizer discharge rate and motor speed reached 0.9912. The IWOA-fuzzy PID algorithm achieved a step-response overshoot of only 3.3% and a settling time of 1.55 s, while the relative error of field fertilization was controlled within ±5%.

To address the limitations of computational resources and the stringent real-time stability requirements in post-harvest online grading of fresh tea leaves, this study proposes a lightweight object detection method oriented toward efficient deployment. The YOLOv11n network was selected as the baseline model. A StarNet backbone was introduced to enhance nonlinear inter-channel interactions while reducing the complexity of feature extraction. In addition, a DySample dynamic upsampling module was employed to predict content-adaptive sampling locations, thereby improving multi-scale feature reconstruction under lightweight constraints. Furthermore, a wavelet-based pooling structure was designed to perform structure-aware frequency-domain decomposition, preserving critical edge and texture information while reducing redundant computation. The Inner-MPDIoU loss function was also adopted to calculate overlap consistency within a compact core region, thereby improving bounding-box localization stability for fine-grained structures. Based on these integrated improvements, the lightweight YOLOv11-SDWI detection model was developed. Experimental results demonstrated that the proposed model achieved an accuracy of 89.5%, with only 4.9 GFLOPs and 1.89 M parameters. To further verify its engineering applicability, the algorithm was encapsulated into a complete visual software system specifically designed for fresh tea leaf sorting. The developed system provides a functional interface for real-time detection, effectively bridging theoretical algorithm design and practical agricultural applications, and establishing a solid software foundation for future deployment on physical sorting equipment.

This study aimed to evaluate the performance of a vacuum seed-metering unit (VSMU) developed for sowing vegetable seeds in trays under different vacuum pressures. Laboratory experiments were conducted using three seed types with markedly different physical and geometric properties: pepper, Armenian cucumber, and okra. Metering performance was evaluated in terms of the accuracy, miss, and multiple indices. The results showed that seed physical and geometric properties affected the optimal vacuum pressure, which was 1.52, 7.5, and 8.5 kPa for pepper, Armenian cucumber, and okra seeds, respectively. The corresponding accuracy indices were 93.54%, 91.25%, and 96.04%. Seeds with a relatively large mass, such as okra seeds, or low sphericity, such as Armenian cucumber seeds, required higher vacuum pressures than the flat, approximately circular pepper seeds. The developed VSMU therefore demonstrated its ability to handle seeds with diverse physical and geometric characteristics and achieve accurate seed metering.

Effective cooling is crucial for maintaining suitable thermal environments in greenhouses during hot summers. This study developed a coupled numerical model to investigate the cooling performance of a wet curtain system in a multi-span plastic greenhouse in Weifang, China. Relative deviations were less than 5.88% for wet curtain outlet temperature and below 7% for average greenhouse temperature. Results indicated that the wet curtain reduced the average greenhouse temperature by 2.33 ~ 4.20 ?, with the maximum reduction of 4.20 ? occurring at 12:00. The cooling capacity increased with rising solar radiation in the morning and then diminished in the afternoon. The cooled air entered as a floor-level jet, accumulated near the crop zone, and subsequently rose due to buoyancy, forming two large recirculation vortices that enhanced vertical mixing and improved temperature uniformity. Consequently, the average indoor air velocity increased by 8.38% ~ 22.40% compared to ventilation without the wet curtain. Parametric analysis revealed that the average greenhouse temperature decreased nonlinearly with increasing inlet velocity, and the marginal cooling benefit dropped significantly when the inlet velocity exceeded 0.75 m/s. An optimal inlet velocity range of 0.75 ~ 1.0 m/s is recommended. The model accurately captured the thermal performance and airflow patterns of the greenhouse with wet curtain cooling.

Efficient and flexible agricultural image annotation is crucial for intelligent crop monitoring in smart agriculture, yet conventional detection models are limited by fixed class labels and require extensive manual annotations. This study presents a zero-shot annotation framework that integrates OWLv2, Googles second-generation open-vocabulary vision model, with large language models (e.g., GPT-3.5, DeepSeek V1) to enable multilingual, natural language-driven fruit recognition in smart agriculture. A user-friendly interface was developed to support individual or batch image annotation with adjustable sensitivity to meet diverse field requirements. Experimental evaluations demonstrated the frameworks strong generalizability and semantic understanding capabilities, allowing recognition of unseen fruit categories and attributes such as ripeness or color. The system significantly reduces annotation time and labor costs, while enhancing accessibility through natural language interaction. To ensure a robust evaluation of generalizability, a cross-domain protocol was employed using a novel dataset from 2025. Results showed that OWLv2 achieved an F1-score of 0.80 and an mAP of 0.8301, significantly outperforming the pre-trained YOLO11 (F1: 0.74, mAP: 0.60) in zero-shot scenarios. OWLv2 exhibited superior flexibility and required no task-specific dataset retraining, although its computational demands remain higher than lightweight models like YOLO11. Notably, while the LLM (DeepSeek) introduced a total one-time API latency of 598.3 ms (called only once for processing multiple images). the actual core computational latency of OWLv2 was only 257.7 ms per image. Despite a total processing time of 891.9 ms (including visualization output), the framework demonstrates superior recall (0.9080) and semantic flexibility without retraining. These results verify the enormous application potential of OWLv2 and similar zero-shot models in agriculture, providing scalable solutions for automated annotation, real-time monitoring, and large-scale data collection.

Addressing the issues of severe resource wastage, low automation levels, and insufficient control precision in traditional orchard water and fertiliser management, this paper proposes a multi-dimensional, collaborative integrated water and fertiliser management system for orchards. This system combines IoT sensing technology, wireless communication technology, cloud platform computing, and intelligent control algorithms. Through a layered architecture design, this system innovatively enhances the perception layer, transmission layer, data layer, and application layer. It integrates diverse communication protocols such as ZigBee, LoRa and NB-IoT, combines sensors for irrigation, fertilisation, and other aspects, and merges intelligent algorithms including PID closed-loop control and fuzzy neural networks. This enables precise water-fertiliser ratio formulation, remote real-time monitoring, and adaptive regulation. Field validation at a demonstration orchard in Guangdong demonstrated water savings of 30%–65%, fertiliser reductions of 40%–50%, and crop yield increases of 20%–135%. Research findings indicate that the deep integration of IoT and cloud platforms significantly enhances water and fertiliser utilisation efficiency in orchards while reducing labour costs. This provides technical support for standardised orchard cultivation, aligning with the demands for precision and intelligent development in modern agriculture.

Driven by global agricultural mechanization and green low-carbon transformation, hybrid tractors have become a key focus in agricultural machinery competition due to fuel economy and operational adaptability. Using literature review and comparative analysis, this paper summarizes global and domestic technological development and characteristics of typical models. It analyzes three core power topological structures, compares energy configurations and management strategies, clarifies applicable scenarios and pros and cons of different routes, and points out bottlenecks in power coupling, energy storage and control, as well as industrial dilemmas like high costs and poor supporting facilities.

During mechanized harvesting operations, including excavation, root–soil separation, collection, and transportation, cassava roots are susceptible to multiple mechanical loads such as shear, compression, and impact, which easily induce mechanical damage and seriously deteriorate the processing quality and storage performance of cassava roots. To reveal the mechanical variation characteristics and damage thresholds of cassava roots under primary mechanical damage modes during harvesting and transportation, this study took harvest-stage cassava roots as the research object and conducted systematic mechanical property tests. Three typical mechanical tests, including compression, shear, and bending tests, were performed to explore the mechanical response rules of cassava roots under typical loads, obtain key mechanical parameters and damage thresholds, and clarify the failure characteristics and mechanical mechanisms under different loading modes. The research results clarify the internal relationship between mechanical properties and mechanical damage of cassava roots at the harvesting stage. This study provides reliable basic data and theoretical support for the parameter optimization of key working components, operational parameter matching of specialized cassava harvesters, and the optimal selection of tool edge angles for cassava slicing machinery, and possesses practical engineering significance for reducing mechanical harvesting damage and improving the post-harvest commodity rate and processing performance of cassava roots.

To explore the operational performance of a ridge roller under two different working modes, namely, linear arrangement (LA) and interleaved arrangement (IA) of adjacent ridging shovels, firstly, theoretical analysis was conducted to conclude that the operational resistance of IA is greater than that of LA. Secondly, a discrete element simulation method was employed to establish a discrete element simulation model. The extracted data verified the conclusions of the theoretical analysis. Qualitative analysis was conducted based on the formed ridge profile, indicating that LA has a better ridge forming effect than IA. Finally, field experiments were conducted, with the operating speed of the ridge roller (1.5 km/h, 3 km/h, 4.5 km/h) and different installation distances between adjacent ridging shovels (0, 240 mm, 480 mm) as experimental factors. The average operational resistance, the distance between adjacent ridge tops, and the height difference at the bottom of the ridge furrow were used as indicators for a full-factor experiment. It was concluded that under the same conditions, LA has the best ridge forming effect but requires the greatest resistance, while IA can effectively reduce resistance but has a poorer ridge forming effect. This study provides a theoretical basis and guidance for tillage and soil preparation operations.

To overcome YOLOv11s limitations in complex field environments, this paper proposed SDD-YOLOv11n, a lightweight real-time detector for soybean diseases. The model reconstructed the backbone using GhostConv to minimize redundancy and integrates a C3k2_Star module to enhance small lesion detection against background noise. Additionally, a Detect Efficient (DE) head further compressed the architecture. Experimental results verified the models efficiency, achieving a parameter count of 1.88 M and a weight size of 3.9 MB—reductions of 27.3% and 25% compared to YOLOv11n, respectively. Furthermore, the model maintained high detection performance with a Mean Average Precision (mAP50) of 75.6% and an F1-score of 69.3%, demonstrating its effectiveness in balancing architectural efficiency and accuracy in complex field environments.

To achieve precise prevention and control of rubber roll wear in huskers, this study established a complete experimental platform integrating mechanical transmission, feeding control, and visual acquisition units, and subsequently developed a real-time monitoring system for paddy flow instability based on machine vision. An image acquisition system composed of a CMOS camera and a customized light source was built to construct a dedicated dataset for paddy flow states. Based on the lightweight YOLOv8n detection model, Python programming was adopted in the PyCharm environment with the Ultralytics library integrated, realizing real-time recognition and quantitative analysis of paddy flow states. The results demonstrated that the system realized a real-time detection efficiency of 14 FPS on a local workstation, and the YOLOv8n model achieved a recognition accuracy of 90.8% in terms of mean Average Precision at IoU threshold 0.5 (mAP@0.5) for sparse and overlapping grain states. The system could effectively capture key abnormal states, including inclined paddy grains entering the rolling zone, sparse paddy flow lasting more than 5 seconds, and overlapping paddy flow density exceeding 10 grains/cm². This study transformed the mechanical characteristics of paddy flow instability into pixel-level quantitative indicators and established an integrated visual monitoring paradigm of "perception-analysis-decision", providing effective technical support for the intelligent management and control of huskers.

The efficiency of agricultural operations depends on the length of non-working passes in the headlands. Therefore, the aim is to minimize both turn length and headland width. Various methods are used to construct turn trajectories, allowing them to be compared under specific conditions and enabling the most efficient movement pattern to be selected. This article examines closed fishtail-shaped turns composed of arcs with a given radius and straight-line segments. Analytical relationships were derived to determine the headland width and the length of closed fishtail-shaped turns for different travel directions of a machine-tractor unit equipped with a mounted reversible plough in an irregularly shaped field. It was found that, at a positive angle between the travel direction of the unit and the field boundary, movement of the unit across the field from right to left results in shorter closed fishtail-shaped turns and requires a narrower headland than movement from left to right.

To investigate the effects of moisture content on the compressive mechanical properties of sweet potato roots, this study used three sweet potato varieties (Zishu, Hongyao, and Xiguahong) cultivated in the Tianjin region as experimental materials. Experiments were conducted between October 8, 2025, and January 8, 2026, in a laboratory environment maintained at 20°C ± 2°C and 50% ± 5% relative humidity. The test employed a moisture content measurement method based on storage duration, measuring moisture content every 10 days. Simultaneously, compressive mechanical property tests were performed using an INSTRON 3344 series universal material testing machine under conditions of a 10 mm/min loading speed and a maximum pressure of 1500 N. The maximum rupture force, maximum deformation, and elastic modulus of sweet potatoes at different moisture contents were determined. Data analysis was conducted using Design-Expert 13 software. The results indicated that the variation patterns of moisture content were similar across the different sweet potato varieties. The moisture content decreased rapidly during the initial storage period, showed a brief increase in the middle stage, and then continued to decline at a slower rate in the later stage. Moisture content significantly affected the compressive mechanical properties of sweet potatoes. As the moisture content decreased, the elastic modulus and maximum rupture force increased, while the maximum deformation decreased.

Corn seedling and weed detection during the 2–5-leaf stage is essential for precision weeding, but similar morphology, field background interference, and the accuracy-efficiency trade-off limit the practical use of lightweight detectors. To address these problems, this study proposes Corn-Weed-ENAS, an improved YOLOv8-based detection model with joint structure and loss-function search. A differentiated ENAS search space was constructed for the backbone, neck, detection head, and loss function to automatically identify a compact architecture suitable for corn seedling-weed detection. A field image dataset of 2–5-leaf corn seedlings and weeds were collected in Heilongjiang Province, and comparative and ablation experiments were conducted against mainstream detectors. The proposed model achieved 97.3% mAP@0.5 with 2.5M parameters and 6.7 GFLOPs, showing a favorable balance between detection accuracy and lightweight deployment. To further verify its agricultural engineering applicability, an indoor simulated operation test was conducted on a Jetson Orin Nano Super platform using 20 pots of corn seedlings and weeds and a moving test vehicle at 1.2 m/s. In the virtual spraying signal verification, YOLOv8n produced five missed detections and one false detection, whereas Corn-Weed-ENAS produced three missed detections and no false detection. These results indicate that Corn-Weed-ENAS can provide more reliable weed localization and virtual trigger outputs for selective spraying decision support.

An alternative approach to the utilization of lake sapropel is its mixing with available organic sorbents, such as chopped cereal straw. The application of such mixtures in strips into the soil ensures the formation of a moisture-holding nutrient layer during crop cultivation. To form these strips, an experimental prototype of a single-row machine was developed. Under laboratory conditions, the torque required to drive the machine and the mixture mass flow rate were investigated. The variable parameters included the rotational speed of the metering beater shaft , the blade inclination angle , the width of the hopper outlet opening , and the opening position of the metering beater housing gate . To obtain mathematical models of the machine drive torque and the mixture mass flow rate in the form of regression equations, a statistical design of experiments method for a four-factor experiment based on the Box–Behnken design was applied. Analysis of the obtained regression equations showed that, in order to reduce the energy consumption of the machine drive, it is advisable to maintain the beater rotational speed within the range of n = 550–600 rpm, the blade inclination angle at ? = 45 - 500 , and the maximum opening of the metering beater housing gate ? = 0.06 m. A nomogram was developed for regulating the mixture feeding rate of the machine by changing the width of the hopper gate opening ? . It was also established that, at the maximum mass flow rate of Q 3.5 = kg/s, the torque required for the machine drive ? ranged from 6.9 to 7.2 N·m.

As the pig farming industry evolves towards intelligence and large-scale operations, multi-object tracking technology plays a pivotal role in enhancing farming efficiency and optimizing health management. Addressing the issues of trajectory interruption and jitter caused by frequent occlusion of targets, significant scale variations, rapid motion, and sudden turns in large-scale breeding scenarios, an improved ByteTrack method is presented. This method employs YOLOv8 as the detector and enhanced the ByteTrack framework. By incorporating an Exponential Moving Average (EMA) trajectory smoothing module, it optimizes the trajectory jitter issues encountered by traditional Kalman filtering in fast-moving or sudden turning scenarios, resulting in smoother and more continuous tracking trajectories. Experimental results on a real-world livestock farm video dataset demonstrate that the proposed method achieves improvements across several key evaluation metrics. Specifically, the Multi-Object Tracking Accuracy (MOTA) increased by 1.61%, Identification F1 Score (IDF1) improved by 2.55%, and the Recall rose by 9.68%. The results demonstrate that the proposed method can effectively track pigs in complex farming environments with frequent occlusions, large scale variations, and abrupt motion, providing reliable underlying data support for downstream applications, such as abnormal behavior identification and automated physical activity statistics in intelligent pig farming systems.

Current research on shrub stubble cutting machines primarily focuses on mechanical structure design and operational performance, whereas shrub localization and cutting status still rely on manual judgment. To address this issue, an intelligent desert shrub stubble cutting machine integrating structural design with vision-assisted operation was developed. The overall structure and key component parameters were determined through theoretical analysis, and the disc cutting process was validated via Workbench simulation. The YOLOv5s model incorporating a Focal-CIoU loss function was adopted to enhance shrub detection under complex background conditions. Image processing and binocular vision were employed to obtain the three-dimensional coordinates of shrub roots. Experimental results demonstrate that the cutting device meets the operational requirements; the improved model achieves an accuracy of 91.9%, a recall of 96.5%, and a mAP of 98.3%; and the maximum relative errors for root depth and height measurements are 3.19% and 6.51%, respectively, satisfying the detection and localization requirements for shrub stubble cutting.

This study proposes Smooth-Planner, a robust gradient-based local trajectory planning method for non-holonomic orchard robots operating in narrow row environments. The method employs a B-spline–based optimization framework that explicitly considers kinematic feasibility and collision avoidance without relying on dense global maps. Field experiments conducted in a standardized orchard demonstrate stable and repeatable performance, with lateral deviations effectively constrained and a minimum safety clearance of 0.90 m consistently maintained from crop rows. The results validate the practical safety and reliability of the proposed approach for autonomous agricultural operations.

To reduce miss-seeding and improve the stability of single-clove pickup in garlic seed metering devices, a spoon-clamp type garlic seed metering device was designed. The proposed mechanism combined spoon-type seed pickup with spring-assisted clamping, so garlic cloves with irregular surfaces could be picked up, carried, cleaned, and discharged more stably. Based on the geometric characteristics of three garlic varieties, the key parameters of the seed plate, seed pickup spoon, and opening-closing mechanism were determined by theoretical analysis. A three-dimensional model of the seed metering device and garlic-clove models were established using the discrete element method. Single-factor simulations were then performed to analyze the effects of seed plate speed and seed pickup spoon diameter on the qualified seeding index, reseeding index, and miss-seeding index. The suitable simulated operating range was 1–7 r/min for seed plate speed and 25–35 mm for seed pickup spoon diameter. Bench tests showed that the suitable parameter combination for “Acheng Purple Garlic” and “Nong’an Purple Garlic” was a seed plate speed of 4 r/min and a seed pickup spoon diameter of 32 mm. For “Zhaozhou White Garlic”, the suitable combination was 5.5 r/min and 28 mm. These results showed that the spoon-clamp mechanism was feasible for improving single-clove garlic metering stability under controlled bench-test conditions

With the development of society and the advancement of science and technology, the object detection technology driven by Artificial Intelligence is also constantly innovating. As an important task in the field of agricultural Computer Vision, fruit detection in real environments faces many challenges. This paper reviews and analyzes the latest breakthroughs and representative studies in this field. Based on the existing research, the existing fruit detection algorithms are roughly divided into 5 categories: (1) the traditional fruit detection algorithm based on manual features; (2) the fruit detection algorithm based on two stages; (3) the fruit detection algorithm based on one stage; (4) the fruit detection algorithm based on anchor-free frame, and (5) the fruit detection algorithm based on transfer learning. This paper also discusses various application scenarios, such as multi-object detection, complex backgrounds, and edge computing. It also summarizes the classic and cutting-edge technical methods at the current time point, providing valuable insights for fruit detection in agricultural production.

Automatic orientation and conveying are critical to enhancing the operational efficiency of postharvest cabbage commercial processing (e.g., trimming), reducing labor input costs, and boosting commodity added value. To address this demand, this study presents the optimal design of an automatic orientation and conveying device for postharvest cabbages based on conical treads, leveraging the moment of inertia principle. Through dynamic modeling and analysis, the key structural parameters governing orientation performance and their feasible ranges were quantified. Furthermore, the orientation reliability of the proposed structure was validated via ADAMS-based motion simulation. A prototype of the postharvest cabbage orientation and conveying device was fabricated, and parameter optimization experiments were conducted to determine the optimal operating conditions. The results indicate that the device achieves superior operational performance when the orientation roller angle is set to 20°, the axial clearance is 60 mm, and the conveying chain linear speed is 300 mm/s. Under these optimal parameters, the orientation success rate of postharvest cabbages reaches 98.67 ± 1.05%, with an orientation angle deviation of 5.18 ± 0.39°. This design remarkably improves the orientation and conveying precision of postharvest cabbages, laying a solid theoretical foundation and providing technical support for subsequent commercial processing operations such as root cutting.

Panax notoginseng is a high-value medicinal rhizome crop widely cultivated in the hilly and mountainous regions of southwest China, where traditional manual harvesting remains labor-intensive and inefficient. To address these challenges, this study focuses on the design, kinematic analysis, optimization, and validation of a separation conveyor device for a self-propelled Panax notoginseng combine harvester. The separation conveyor was developed based on the agronomic characteristics of Panax notoginseng and the mechanical requirements of soil-rhizome separation. A comprehensive methodology integrating theoretical modeling, kinematic and mechanical analysis, coupled discrete element-multibody dynamics (DEM-MBD) simulations using EDEM and RecurDyn, and laboratory experiments was employed. Key operational parameters, including conveyor speed, vibration frequency, and lifting angle, were systematically evaluated through quadratic regression and orthogonal experimental design. Simulation results indicated that effective soil separation could be achieved within 2.25 s, with fine soil particles separating earlier than larger aggregates. Laboratory tests identified optimal parameters of 0.8 m·s?¹ conveyor speed, 1.5 Hz vibration frequency, and a 20° lifting angle. Field validation experiments confirmed the reliability of the optimized design, achieving average separation and conveying rates of 96.87% and 96.42%, respectively, with a low rhizome damage rate of 1.88%. The results demonstrate that the proposed separation conveyor provides an effective solution for mechanized harvesting of Panax notoginseng and offers valuable reference for similar rhizome crop harvesters.

Detasseling the female parent plants is a critical operation in hybrid corn seed production. This article systematically reviews global developments in mechanized detasseling technology for corn seed production. First, it provides an in-depth analysis of two key technologies supporting mechanized detasseling: the biomechanical characteristics of corn tassels and intelligent tassel-detection technologies based on machine vision and photoelectric sensing. Second, the current development of mechanized detasseling equipment is reviewed, and the technical parameters and field performance of representative domestic and international machines are compared in detail. The comparison identifies four major constraints limiting the wider adoption of this technology in China: an insufficient single-pass detasseling rate, poor coordination between machinery and agronomic practices, a high crop-damage rate, and low acceptance among farmers. Finally, three targeted optimization strategies are proposed: developing intelligent and precision detasseling equipment, promoting closer integration of agricultural machinery and agronomy, and strengthening field demonstration and extension activities. This review aims to provide a theoretical basis and practical reference for improving mechanized detasseling processes and supporting the development of more effective detasseling equipment.

Manual weeders reduce labour requirements compared with hand weeding but remain time-consuming and physically demanding in paddy cultivation. A rotary power weeder powered by a 4.5 hp single-cylinder, air-cooled engine and equipped with three rotary units was developed for direct-seeded paddy and evaluated under field conditions. The machine was tested at three forward speeds and compared with manual weeding using a khurpi. Weeding efficiency decreased with increasing forward speed, whereas plant damage and power requirement increased. The highest grain yield and benefit–cost ratio were obtained at 1.27 km h?¹, which was identified as the optimum operating speed. Field evaluation demonstrated that the developed machine reduced labour requirements, provided effective weed control, and improved the operational efficiency of mechanical weed management in direct-seeded paddy cultivation.

To address the high power consumption, limited load adaptability, and inadequate parameter matching of conventional axial-flow threshing devices, power-consumption tests were conducted on an axial-flow threshing device with adjustable threshing intensity using rice as the test material. The tests examined different threshing-intensity modes, separating-screen types, and operating parameters. The effects of these factors on no-load power consumption, effective power consumption, total power consumption, and mechanical efficiency were analyzed. The results showed that total power consumption was minimized when the threshing-intensity adjustment plates were fully open and the rectangular-hole separating screen was used. Total power consumption increased with increasing drum speed, deflector angle, and feed rate, but decreased with increasing concave clearance. Mechanical efficiency initially remained stable and then decreased as drum speed increased, whereas it increased with increasing deflector angle and feed rate. The specific threshing power consumption, expressed per unit feed rate, ranged from 4.4 to 5.2 kW/(kg/s). It increased with increasing drum speed, deflector angle, and feed rate, and decreased with increasing concave clearance. This study provides a reference for reducing power consumption and optimizing parameter matching in axial-flow threshing devices with adjustable threshing intensity.

This study proposes a real-time method for detecting combine-harvester feed quantity based on multi-source information fusion and error correction, with the aim of addressing control delays caused by information lag. Field tests were conducted to obtain key parameters, including feeding-auger torque and threshing-drum torque, determine their relational weights, and establish a prediction model. The model achieved a maximum relative error of 9.64% and a mean relative error of 4.55%, meeting the requirements of practical field operation.

The soybean-maize strip intercropping mode has been widely popularized, yet existing plant protection machinery fails to dynamically adjust pesticide application according to crop zones, suffers from poor pesticide application accuracy, and is prone to causing crop damage. Aiming at the above problems, this study proposed a precise strip-zoning pesticide application method for soybean and maize and established an automatic strip-zoning pesticide application control system based on machine vision. Perspective transformation was adopted to correct image viewing angles; after subsequent processing via excess green binarization and opening-closing operations, a dynamic ROI (Region of Interest) positioning and recognition algorithm for crop rows based on the peak value method was applied. The least square method was used to extract crop row lines, the offset was calculated according to the geometric relationship of the centerlines of soybean and maize zones, and an automatic strip-zoning offset compensation model was constructed to achieve precise strip-zoning. Field experiment results show that: under conditions with little shadow and stable light, the recognition algorithm achieved the optimal performance with an accuracy of up to 92%. At operating speeds of 2–5 km/h, the automatic strip-zoning pesticide application control system had a mean strip-zoning deviation of =3.16 cm, with the proportion of deviations within 5 cm reaching 84% (up to 94%). Under wind speeds of 1.4–2.9 m/s, the maximum droplet drift deposition amount of the closed anti-drift device was 9.41 droplets per cm², delivering favorable strip-zoning pesticide application performance, which provides data support and a reference for field precision plant protection operations under the strip intercropping mode.

Mechanised furrowing and fertiliser application is an effective engineering solution for improving operational efficiency and reducing labour intensity in hilly and mountainous orchards. In this study, a systematic optimisation methodology for a disc-type furrowing tool is proposed by integrating discrete element method (DEM) simulations using EDEM with quadratic orthogonal regression experiments and response surface analysis. Key structural parameters, including bending radius, bending angle, and alpha angle, were selected as design variables, and their individual and interactive effects on furrowing power consumption were investigated. The results of the response surface analysis indicate that appropriate structural optimisation can significantly reduce trenching power consumption. The optimal structural parameters were determined as follows: bending radius of 36.79 mm, bending angle of 126.94°, and alpha angle of 43.65°. Under these conditions, the predicted power consumption was 0.85 kW, representing a reduction of 8.88% compared with the original tool design. Soil bin experiments were conducted to validate the DEM simulation results, and the experimental trends showed good agreement with the simulation predictions, demonstrating the reliability of the proposed optimisation approach. This study provides a practical reference for the engineering design and performance improvement of soil-engaging tools.

In greenhouse cultivation of cherry tomatoes, the accurate identification of pollination status is a key prerequisite for achieving automated robotic pollination. As the site of direct interaction during pollination, changes in the colour and morphology of the stigma most accurately indicate whether pollination has been successful. However, due to the minuscule size of the stigma and the extremely subtle visual differences among the three states—unpollinated, pollinated, and fruiting—combined with interference from fluctuating lighting conditions and complex backgrounds in greenhouse environments, traditional methods struggle to provide high-precision visual perception data for pollination robots. To address this issue, this paper proposes an improved model, YOLO11-RCSP, which focuses on the stigma as the core recognition object to achieve automatic classification of the three pollination states: introducing a novel attention mechanism, RFA (Receptive Field Attention), adding an enhancement module (CPA-Enhancer) to the original model, and incorporating SAConv switchable dilated convolutions. In addition, the loss function is improved using the Powerful-IoU method (adaptive penalty factor and gradient adjustment function based on anchor box quality). The precision rate of the improved model reached 88.40%, the recall rate reached 84.60%, and the average precision was 90.80% when the IoU was 0.50. The average precision ranged from 0.50 to 0.95, reaching 63.20%. These metrics represent improvements of 5.20%, 3.30%, 4.40%, and 5.30%, respectively, compared to the original YOLO11 network model, demonstrating stronger feature extraction capabilities and robustness. Ablation experiments further confirm that the attention mechanism and multi-scale optimization have a synergistic effect on enhancing the performance of stigma pollination status recognition. This research will be applied to pistil-level pollination status recognition tasks, helping to advance the development and application of pollination robots, and providing critical technical support for the development of automated pollination equipment in protected agriculture.

Accurate detection of grape diseases in controlled facility environments is of significant importance. In this study, a novel detection model, PWE-YOLO, is proposed based on YOLO v11. In this model, the C3k2 modules in the Neck network are replaced with C3k2-PConv modules, which maintain high-precision feature extraction while reducing model parameters and memory consumption. Furthermore, a WaveletPool module is introduced to replace all Conv modules in the model, except for the 0th and 1st layers, enhancing feature representation and detection accuracy for grape diseases while further reducing model parameters and floating-point operations. EMA attention modules are incorporated at the 17th and 24th layers to improve small-object detection capabilities, thereby increasing overall detection precision. Experimental results demonstrate that PWE-YOLO achieves a precision of 85.0%, recall of 83.6%, and mAP of 86.9%, with 1.96 × 106 parameters and 5.1 × 10? floating-point operations. Compared with the original YOLO v11 model, precision, recall, and mAP increase by 4.5, 1.4, and 1.9 percentage points, respectively, while parameters and floating-point operations decrease by 24.03% and 10.05%. Relative to YOLO v8, v9, v10, v12, and v13, precision improves by 2.2%, 5.3%, 3.3%, 2.2%, and 2.5%, recall by 6.0%, 2.8%, 5.2%, 8.8% and 3.8%, and mAP by 1.9%, 1.8%, 2.8%, 5.0%, and 2.3%, respectively, with corresponding reductions in model parameters and floating-point operations. These results indicate that PWE-YOLO not only provides high-accuracy detection for grape diseases but also reduces computational complexity, achieving a lightweight architecture and faster detection speed, making it suitable for deployment in resource-constrained target detection scenarios.

To address the challenges of low transplanting efficiency, poor planting accuracy, and high labor intensity associated with manual Panax notoginseng transplanting in the hilly and mountainous regions of Yunnan, as well as the limited adaptability of existing transplanting equipment to narrow ridged fields and steep, complex terrain, this study developed a self-propelled, fully electric tracked chassis tailored to the agronomic requirements of Panax notoginseng transplanting. The study included the overall structural design, selection and matching of the power battery and drive transmission system, and development of a dimensionless ratio-coefficient control model relating planting spacing to travel and transplanting speeds under the constraint of the manual seedling-feeding speed. An integrated electronic control system was developed to provide remote-controlled travel, speed-synchronized transplanting control, and straight-line deviation correction. The stability and dynamic performance of the chassis were analyzed theoretically and subsequently validated through field tests under multiple operating conditions. Theoretical results showed that the chassis had a maximum lateral roll angle of 39.19°, a maximum traction force of 7,524 N, and sufficient power reserve for operation on slopes of up to 25°. Field tests demonstrated that, after correction using the speed-adjustment coefficient, the straight-line deviation over a travel distance of 30 m was limited to within 1.1%, exceeding the requirements of the relevant national standard. The measured pivot-turning offset was approximately 191 mm, in close agreement with the theoretical value. Key performance indicators, including slope-climbing stability, ridge-travel stability, and motor thermal stability, all met the applicable national standards. The developed chassis effectively improves the adaptability of transplanting equipment for Panax notoginseng production in hilly and mountainous regions and significantly enhances the automation level and planting accuracy of transplanting operations. This study provides theoretical and technical support for the intelligent development of transplanting equipment for rhizomatous medicinal plants.

To address the challenges of low transplanting efficiency, poor planting accuracy, and high labor intensity associated with manual Panax notoginseng transplanting in the hilly and mountainous regions of Yunnan, as well as the limited adaptability of existing transplanting equipment to narrow ridged fields and steep, complex terrain, this study developed a self-propelled, fully electric tracked chassis tailored to the agronomic requirements of Panax notoginseng transplanting. The study included the overall structural design, selection and matching of the power battery and drive transmission system, and development of a dimensionless ratio-coefficient control model relating planting spacing to travel and transplanting speeds under the constraint of the manual seedling-feeding speed. An integrated electronic control system was developed to provide remote-controlled travel, speed-synchronized transplanting control, and straight-line deviation correction. The stability and dynamic performance of the chassis were analyzed theoretically and subsequently validated through field tests under multiple operating conditions. Theoretical results showed that the chassis had a maximum lateral roll angle of 39.19°, a maximum traction force of 7,524 N, and sufficient power reserve for operation on slopes of up to 25°. Field tests demonstrated that, after correction using the speed-adjustment coefficient, the straight-line deviation over a travel distance of 30 m was limited to within 1.1%, exceeding the requirements of the relevant national standard. The measured pivot-turning offset was approximately 191 mm, in close agreement with the theoretical value. Key performance indicators, including slope-climbing stability, ridge-travel stability, and motor thermal stability, all met the applicable national standards. The developed chassis effectively improves the adaptability of transplanting equipment for Panax notoginseng production in hilly and mountainous regions and significantly enhances the automation level and planting accuracy of transplanting operations. This study provides theoretical and technical support for the intelligent development of transplanting equipment for rhizomatous medicinal plants.

To address the contradiction between obstacle avoidance safety and irrigation operation quality in 3D path planning of fixed-wing UAVs for mountainous agricultural crop irrigation, this paper proposes a multi-strategy improved dung beetle optimization (MDBO) algorithm. First, safe turning radius and pitch angle constraints are embedded into the optimization objective to restrict flight attitude, ensure flight stability under complex mountain terrain, reduce collision risk, and maintain consistent spray deposition. Second, four improvement strategies, including Bernoulli chaotic mapping initialization, golden sine position update mechanism, dynamic adaptive weight, and adaptive Gaussian-Cauchy hybrid mutation perturbation, are integrated into the basic DBO algorithm to enhance global search capability, local exploitation accuracy and convergence performance. Comparative tests on 9 benchmark functions show that MDBO outperforms the basic DBO in convergence speed and solution stability. Furthermore, three irrigation performance indicators (coverage rate, overlap ratio and coverage uniformity) are constructed for posterior evaluation of spray distribution quality. Simulation experiments under three mountainous terrain scenarios verify that MDBO achieves better path accuracy, convergence performance and irrigation effect than traditional algorithms, and can meet the efficient irrigation path requirements of mountainous agricultural crops.

Smartphones have become a convenient tool in agriculture due to their mobility, affordability, and capacity to enhance more efficient crop monitoring, including MATAG variety identification. Detecting MATAG hybrid varieties remains challenging because their morphological traits are difficult to differentiate macroscopically, but reflectance values can be generated using a spectral signature graph. This review aims to explore the development of an intelligent identification system for the MATAG variety based on spectral signature analysis using a smartphone device. It discusses characterisation of MATAG hybrids, spectral signature differences between each coconut species, the development of spectral signature libraries, and the potential of smartphone-spectral signature-based applications compared with manual inspection and laboratory analysis. It also highlights the mechanisms of MATAG variety detection based on spectral signature analysis using a smartphone and practical considerations for farmers. Case studies and testimonials from farmers on how smartphone devices have revolutionised crop monitoring practices were also discussed, together with the potential benefits of using this technology for the agricultural industry. It also elaborates on the opportunities and challenges of detecting MATAG variety using smartphone-spectral approaches, which will be a useful resource for app developers to solve the problems in the field. Overall, this review provides a comprehensive overview of smartphone-spectral signature integration for MATAG detection that eventually helps farmers in the plantation.

To overcome the problems of low germination rate and irregular emergence caused by dense surface structure, poor water permeability, and dormancy of forage seeds, this study screened the optimal plasma generation mode suitable for continuous treatment of forage seeds under atmospheric pressure and evaluated its efficacy and mechanism. Through a comparative analysis of the uniformity and stability of corona, spark, gliding arc, and dielectric barrier discharge (DBD), the results showed that DBD can generate large-area, low-temperature, and uniform plasma in atmospheric air. Taking Leymus chinensis seeds as the research object, the effects of different treatment voltages (0–16 kV) on the glume microstructure and the electrical conductivity of the leachate were investigated. Scanning electron microscopy (SEM) observations indicated that DBD treatment with appropriate parameters caused significant etching on the seed glume surface, forming micropores and fissures, and removing impurities. The electrical conductivity measurements further revealed a “dosage effect”: moderate voltage helped maintain the integrity of the membrane system, whereas excessive voltage led to severe damage to the glume and leakage of intracellular contents. The study confirmed that atmospheric pressure DBD plasma, through physical etching and surface modification, can effectively improve the water absorption channels and physiological activity of Leymus chinensis seeds, and an optimal parameter window exists. These findings provide a technical basis for the development of continuous plasma seed treatment equipment.

Target recognition and localization of picking robots are very important in orchard environments. This paper analyzed the You Only Look Once version 8 normal (YOLOv8n) method in deep learning for citrus picking robots, introduced FasterNet, efficient multi-scale attention (EMA) mechanism, and Wise Intersection over Union (WIoU) loss function, developed an improved YOLOv8n method, and collected a citrus fruit dataset to analyze the recognition and positioning effects of the proposed methods. The selected FasterNet, EMA, and WIoU were all superior to the other types compared, and the improved YOLOv8n method achieved an accuracy of 0.893, a recall rate of 0.894, and a mean average percentage (mAP) of 0.895 for citrus recognition, outperforming other recognition methods. In terms of citrus localization, the average errors on X, Y, and Z axes were 3.56 mm, 3.54 mm, and 4.74 mm, respectively. The findings demonstrate the reliability of the proposed model, and it can be applied to actual citrus picking robots.

This study presents a particle-scale coupled CFD–DEM numerical approach for elucidating aerodynamic sorting mechanisms and optimizing process parameters. Non-spherical particle models were developed to represent the geometric and physical differences between shells and seed-bearing particles. The Stokes number, which characterizes the balance between aerodynamic drag and particle inertia, was identified as the primary determinant of sorting performance. Under the optimized conditions, a primary airflow velocity of 8.4 m/s, a secondary airflow velocity of 7.85 m/s, and a feed mass flow rate of 80 g/s, the system achieved a shell purity of 95.3% and a recovery rate of 58.2% when processing feedstock with an initial shell content of 5%. The results further revealed an inherent trade-off between purity and recovery. These findings provide a theoretical and numerical basis for the design and optimization of aerodynamic sorting equipment.

Automatic transplanting of plug seedlings is critical for improving agricultural efficiency. However, existing systems often suffer from functional fragmentation and use rigid end-effectors prone to damaging seedlings. This study presents a fully integrated automatic transplanting system that uniquely combines multi-tray storage and conveying, precise step-moving, manipulator-based picking and placing with a novel flexible end-effector, and automatic tray collection. In experiments using pepper seedlings, conducted over repeated trials (n=5) with 576 seedlings per trial, the integrated system achieved a picking success rate of 93.24% and a damage rate of 1.43%, with a maximum conveying positioning deviation of 1.79 mm. Compared with previously reported transplanting systems, the proposed system achieves a superior success rate while delivering a substantially lower seedling damage rate. This research provides a practical, highly integrated solution for small-to-medium scale nurseries.

To address the high labour intensity, low operating efficiency, and safety risks associated with loading and unloading flue-cured tobacco leaves in bulk-curing barns, a tobacco-clip loading and unloading device suitable for the confined barn environment was developed. The device integrates a powered platform, a two-stage scissor-lift mechanism, and an online weighing and conveying system. Mechanical modeling and finite element analysis were conducted to verify the structural strength and overturning stability of the lifting mechanism under a rated load of 350 kg and a maximum lifting height of 3 m. The resulting overturning stability coefficient was approximately 2.9. Within an inclination range of 0°–25°, the mass-measurement error of the conveying system did not exceed 0.40%. Field performance tests showed that the maximum conveying rate reached one tobacco clip every 2.7 s. The loading time for a single bulk-curing barn was reduced from 4 h to 1.5 h, while the required labour force decreased from four workers to two. Overall operating efficiency increased by 4.33 times, and the mean time between failures met the requirements for continuous operation during the tobacco-curing season. The developed device enables efficient, low-cost, and reliable loading and unloading of flue-cured tobacco leaves and provides technical support for improving the quality and efficiency of flue-cured tobacco production.

In the process of agricultural intelligence, precise detection of plant organs serves as the foundation for core tasks such as crop phenotyping analysis and yield prediction. However, in complex field environments, small targets such as citrus flowers and shoots face challenges including scale variation, background interference, and dense occlusion, which severely impact detection accuracy. This study improves the YOLOv10 model by introducing the BAM (Bottleneck Attention Module) attention mechanism and GIoU (Generalized Intersection over Union) loss function, constructing a YOLOv10s-BAM-GIoU model suitable for citrus flower, fruit, and shoot recognition. The BAM attention mechanism enhances the models feature extraction capability for small target organs under complex backgrounds through parallel channel and spatial attention branches; the GIoU loss function improves the localization accuracy of densely occluded targets by optimizing the geometric alignment between predicted and ground-truth boxes. Validation experiments were conducted on a self-constructed dataset. The experimental results show that the improved YOLOv10s achieves significant advantages in comprehensive detection accuracy, with an mAP50 of 89.1%, representing an improvement of 2.9%~9.5% over the original YOLOv10s and other comparative models. In fine-grained category detection, the model achieves mAP50 of 91.2%, 83.6%, and 92.5% for shoots, flowers, and fruits, respectively. Furthermore, while maintaining high detection accuracy, the model achieves a detection speed of 23.6 ms per frame, meeting real-time detection requirements. The research results demonstrate that the improved YOLOv10s model integrating the BAM attention mechanism and GIoU loss function achieves an optimal balance between accuracy and speed in citrus organ detection tasks, providing a preferred solution for field real-time detection systems.

To address the low control precision caused by the high nonlinearity and pure time-delay of hydraulic pipelines in variable-rate soybean foliar fertilization, this study developed an automatic variable-rate control system for boom sprayers. Based on Fluent dynamic mesh simulations, the throttling characteristics of the electric control valve were revealed, and a second-order transfer function model with pure time-delay was accurately identified for the actuator. To overcome the reliance of conventional fuzzy controllers on expert experience, an improved Sparrow Search Algorithm-optimized Fuzzy PID (ISSA-Fuzzy PID) control strategy was proposed. By integrating optimized population initialization and adaptive global search, this improvement mechanism effectively avoids premature convergence during dynamic parameter tuning. Simulation results indicate that the proposed algorithm compresses the rise time to 0.35 s, limits the overshoot to within 20%, and achieves steady-state convergence within 1.0 s. Bench tests verified that the average detection errors for system flow rate and pressure were merely 2.14% and 2.34%, respectively. Under the working pressure of 250–350 kPa, dynamic pressure regulations were completed stably within 1.9 s without significant overshoot, exhibiting improved regulation precision under high-flow conditions. This research effectively overcomes the lag bottleneck of actuating mechanisms, providing an integrated control solution with high dynamic response and steady-state precision for variable-rate applications. Practically, this optimized system ensures rapid dosage correction and pressure stability, providing technical support for improving the operational timeliness and fertilizer-use efficiency of field spraying operations.

To address the limited maneuverability and terrain adaptability of conventional liquid-fertilizer injection equipment in hilly and small-plot farmland, a compact self-propelled injector integrating a narrow-track four-wheel-drive chassis, an angle-adjustable injection unit, and a crank-slider penetration mechanism was developed. Unlike large tractor-mounted or fixed-orientation systems, the proposed machine integrates inter-row movement, posture adjustment, needle penetration, and timed point injection on a lightweight platform. The discharge characteristics of the injection needle were investigated using computational fluid dynamics, and a three-factor, three-level Box–Behnken design was employed to optimize injection duration, inlet pressure, and outlet-hole number, with fertilizer volume per injection as the response. Response surface optimization identified an injection duration of 1 s, an inlet pressure of 0.45 MPa, and six outlet holes as the preferred parameter combination. Field validation yielded a measured fertilizer volume of 675.4 mL per injection, compared with the predicted value of 700 mL, corresponding to a relative difference of 3.51%. The results demonstrate the feasibility of controllable deep point placement under the tested conditions and provide a lightweight equipment solution for liquid-fertilizer application in hilly and small-plot farmland.

Irregular branches in agricultural picking environments require rapid collision-free path planning and smooth manipulator motion. This study developed an improved RRT algorithm guided by grid numbering, termed Op-timized-RRT, based on a unified indexed-set representation of numbered primary and backup free-grid regions. Within this framework, dynamic region adjustment, target biasing, node occupancy, and local burst expansion jointly guide the search, while greedy pruning and quintic polynomial interpolation are applied to post-process the resulting path. Unlike geometric-region-based methods that generate coordinate samples within an up-dated sampling window, Optimized-RRT selects a valid grid number from the active set and maps it directly to the corresponding state. Compared with RRT, GB-RRT*, and Informed-RRT*, the complete algorithm reduced planning time, the number of iterations, and the final number of path nodes by 40.61%–94.32%, 57.51%–88.66%, and 89.29%–94.59%, respectively, in two-dimensional scenarios, and by 88.95%–98.92%, 79.26%–94.67%, and 67.31%–90.83%, respectively, in three-dimensional scenarios. The reduction in the number of nodes resulted from the combined effects of guided search and path pruning. Manipulator simulations and a demonstration on a single experimental platform confirmed the executability of the generated trajectories under the tested conditions.

Control of greenhouse microclimate requires the interpretation of several environmental and substrate-related parameters, including air temperature, relative humidity, light intensity, carbon dioxide concentration and soil moisture. This paper compares three machine learning models, Random Forest, Extreme Gradient Boosting (XGBoost) and a multilayer perceptron (MLP), for 30-minute-ahead prediction of irrigation, ventilation and shading states in a greenhouse. The models were trained using data collected from a wireless sensor network (WSN) with four sensor nodes installed in the protected cultivation area. After preprocessing, scaling, error removal, temporal synchronization and resampling at 10-minute intervals, a structured dataset of 7063 records was obtained. Individual sensor readings, spatially aggregated variables, time-related features, rolling averages and short-term differences were used to describe both the current and recent evolution of the greenhouse environment. The dataset was split chronologically into training, validation and test subsets, corresponding to 70%, 15% and 15% of the records, respectively, while preserving the temporal order of the measurements. The best average performance was obtained by XGBoost, with a mean F1-score of 0.9237, followed by Random Forest with 0.9045 and MLP with 0.5862. Random Forest achieved the best results for irrigation and ventilation control, with F1-scores of 0.9630 and 0.9611, respectively, while XGBoost achieved the best result for shading control, with an F1-score of 0.8571. XGBoost also achieved the highest mean accuracy, 0.9506, and mean balanced accuracy, 0.8904. The results indicate that tree-based ensemble models are more suitable than the tested MLP architecture for predictive greenhouse control based on multi-sensor WSN data. The proposed framework supports the selection of a final control model according to prediction performance, stability and interpretability.

Accurate and efficient rice-pest detection is essential for field scouting, early warning and precision control. This study presents a lightweight YOLOv11n-based framework for accurate edge deployment. It integrates a Multi-cognitive Visual Adapter (Mona), a Detail-Preserving Contextual Fusion module (DPCF) and Wise-IoU-based non-maximum suppression (Wise-IoU-NMS). Mona improves multi-scale feature representation with limited overhead; DPCF preserves fine-grained pest details during contextual fusion; and Wise-IoU-NMS stabilizes localization of overlapping and densely distributed targets. On a public dataset of 5,212 images from six near-balanced rice-pest classes, divided 8:1:1 for training, validation and testing, the proposed model achieved 95.4% mAP@50, 94.8% precision and 94.7% recall with 2.65 M parameters and 6.5 GFLOPs. The F1-confidence curve identified 0.476 as the alarm threshold. Deployment on Jetson Nano using ONNX and frame-level logging demonstrated a practical workflow for real-time monitoring and selective spraying.

To address the deterioration in the seed-metering performance of mechanical seed-metering devices caused by vibration during field operation, a conical deflector-type seed meter was selected as the research object. A vibration-system model of the seed meter under field operating conditions was established, and the principal operating parameters affecting seed-metering performance were determined. The seed-metering process under vibration was then simulated using the discrete element method to investigate changes in the working angle of the seed population and its dynamic characteristics. In addition, vibration bench tests were conducted to evaluate the effects of vibration on seed-metering performance. The main results were as follows: (1) seed-filling efficiency, seed-clearing efficiency, and horizontal seed-throwing displacement were significantly affected by operating speed, vibration acceleration, and vibration frequency; (2) the adverse effect of vibration on seed-metering performance increased with increasing vibration acceleration but decreased with increasing operating speed or vibration frequency; and (3) when the vibration acceleration was below 0.63 g and the vibration frequency exceeded 4.28 Hz, the qualified-seed rate was greater than 95%, and the coefficient of variation of seed spacing was less than 20%. This study clarified the effects of vibration parameters on seed-metering performance and identified appropriate operating ranges, thereby providing theoretical support for the vibration-resistant design and optimization of mechanical seed meters.

Chinese solar greenhouses with earthen walls can provide a suitable environment for crop production during winter. In this study, air and soil temperatures inside solar greenhouses in Shanxi Province, China, were monitored from January 1 to March 31, 2024. The results showed that, during nighttime, vertical air-temperature variation among the measurement points was greater than horizontal variation. Zones with approximately equal soil temperatures were also identified among the measurement points. The onset of the increase in weighted mean soil temperature occurred 4 h later than that of the weighted mean air temperature, whereas the onset of the decrease in weighted mean soil temperature occurred 5 h later. These findings provide a data-based foundation for regulating the thermal environment inside Chinese solar greenhouses with earthen walls.

This study aims to investigate how sustainable agriculture is represented in the digital public sphere by examining technology-driven agricultural systems through artificial intelligence–based text analytics. A dataset of 13,354 English posts collected from the X platform during January 2026 was used, with 10,782 posts retained after preprocessing. The methodology integrates text mining, TF-IDF keyword extraction, BERT-based sentiment analysis, and LDA topic modelling. The optimal number of LDA topics was determined using coherence score evaluation to ensure statistical robustness and thematic interpretability. This study integrates text mining, BERT-based sentiment analysis, and LDA topic modelling within a unified artificial intelligence framework to provide a comprehensive analysis of sustainable agriculture discourse on social media. The results show predominantly neutral discourse (65.27%), reflecting informational content, while positive discourse (33.17%) highlights smart farming and innovation. Negative discourse (1.56%) primarily addresses structural challenges. Three dominant themes emerged: climate-oriented sustainability, community-based practices, and technology-driven agriculture, emphasizing the role of digital technologies in shaping sustainable agricultural systems. The findings provide practical insights for policymakers, agricultural stakeholders, and researchers by supporting evidence-based communication strategies and technology-oriented sustainability policies.

Conventional deep-burial devices typically employ a split structure consisting of a furrowing shovel and a soil-covering plate. During field operation, vibrations can cause relative displacement between these components, resulting in unstable soil coverage. Such devices may also exhibit structural redundancy, insufficient tillage depth, and high energy consumption. In this study, theoretical analysis and numerical simulation based on mechanical and soil-dynamics models were conducted to elucidate the soil–tool interaction mechanism of a direct-injection winged shovel and determine its key structural parameters. Using an EDEM-based soil–tool interaction model, a single-factor Box–Behnken experimental design was conducted with penetration angle, operating speed, and wing depth as the experimental factors and soil coverage rate and tillage resistance as the response variables. The optimal parameter combination consisted of a shovel-tip penetration angle of 25°, a wing depth of 150 mm, and an operating speed of 1.4 m·s?¹. Under these conditions, the direct-injection winged shovel achieved low tillage resistance, a high soil coverage rate, stable operation, and consistent furrow width. Field tests showed that all measured performance indicators met the requirements of the applicable Chinese national standards. The developed shovel exhibited stable and consistent performance during deep straw incorporation, thereby effectively improving operational quality.

Furrow opening is a critical operation in the transplantation of Codonopsis pilosula using the seedlings-with-exposed-heads-and-film-covered-bodies (HEBCF) method. The considerable resistance generated during furrow opening results in high power consumption, making resistance reduction an important research objective. Although furrow openers have been widely studied, this study focused on the furrow opener of a self-propelled HEBCF transplanter for C. pilosula. The interaction mechanism between the furrow opener and the soil was investigated through force analysis and the development of models describing soil transport patterns and particle velocities. A discrete element simulation model of the furrow-opening process was established using EDEM to evaluate the effects of the main structural parameters on furrow-opening resistance. A three-factor five-level Box–Behnken response surface experiment was conducted, and regression models for furrow-opening resistance and soil-particle velocity were developed based on the simulation results. The structural parameters were then optimized. The optimal parameter combination consisted of a chisel-tip angle of 65°, a clearance angle of 10°, and an oblique angle of 90°. Under these conditions, furrow-opening resistance was reduced by 30.82%, while the agronomic requirements of a furrow depth of 10 cm and a furrow width of 50 cm were satisfied. These results provide a technical basis for the development and optimization of self-propelled HEBCF transplanters for C. pilosula.

The seed metering device was identified as the core actuating component of precision seeding equipment, directly determining seeding accuracy, seed spacing uniformity, seedling emergence rate, and crop yield. This review systematically examined the technological evolution and research status of mechanical-type and pneumatic-type precision seed metering devices. The identified limitations of mechanical-type devices included low seed filling efficiency, poor seed clearing performance, insufficient operational stability at high speeds, and significant interference from field vibrations. Pneumatic-type devices were found to face technical bottlenecks including difficulties in precision seeding of small-sized seeds, high air pressure loss, poor airtightness, and susceptibility to hole clogging. Recent advances in structural innovation, mechanism analysis, parameter optimization, and performance improvement were summarized. A comparative analysis was conducted on the technical advantages and applicable scenarios of the two mainstream seed metering device types. Future development trends were envisioned towards high-speed and high-efficiency operation, universal adaptability, intelligent monitoring, and low-damage energy saving, providing a reference for the optimal design and technological upgrade of precision seeding equipment.

To address the impact of changes in dairy cow posture on intelligent body condition scoring, this study proposes a dairy cow body condition scoring model (ME-PointNeXt). Based on PointNeXt, this model incorporates a dynamic graph convolutional module at the global feature level; furthermore, a selective sequence block is added to the encoder’s upper layers, improving the SA-FP architecture and enhancing the model’s overall understanding of the structure. Using this model to segment the hindquarters of dairy cows, principal component analysis is performed via eigenvalue decomposition of the local neighbourhood covariance matrix. This enables the determination of edge point normal vectors and the calculation of surface curvature, thereby enabling the scoring of the cows’ body condition. Experimental results show that the ME-PointNeXt model achieved an overall accuracy, mean precision, mean recall, and mean F1 score of 91.43%, 84.33%, 87.92%, and 90.74%, respectively, on the test set. Compared with PointNeXt, the ME-PointNeXt model achieved improvements of 2.72, 3.65, 4.13, and 4.19 percentage points in overall accuracy, average precision, average recall, and average F1 score, respectively. This study has significantly improved the efficiency of dairy cow body condition scoring, ensuring accuracy whilst reducing the workload, and can provide technical support for non-contact dairy cow body condition scoring.

To address the problems of unstable posture, end crushing, and poor release during conventional press-in sand barrier laying caused by the large diameter, rigid internodes, pronounced nodes, and heterogeneous structure of corn stalks, a smooth composite insertion pin consisting of a front steel pin and a rear sleeve was designed. Based on the structural characteristics of the corn stalk rind, pith, and nodes, a layered discrete element model was established to analyze pith compression, sidewall friction, and local failure during axial insertion. The sleeve contact-segment diameter, steel pin diameter, and sleeve front transition angle were selected as experimental factors, and insertion force stiffness was adopted as the evaluation index. A Box-Behnken response surface design was employed for parameter optimization, followed by internodal validation, finite element analysis, nodal insertion tests, and release tests. The results showed that the sleeve contact-segment diameter had the greatest influence on insertion force stiffness, while the steel pin diameter and transition angle also had significant effects. The regression model exhibited a good fit, and the lack-of-fit was not significant. Considering machining feasibility and structural stability, the optimal structural parameters were a sleeve contact-segment diameter of 5.5 mm, a steel pin diameter of 2.0 mm, and a sleeve front transition angle of 160°, corresponding to a predicted insertion force stiffness of 1.15 N/mm. The validation results agreed well with the model predictions. Although the insertion force increased substantially in the nodal region, the optimized composite insertion pin successfully achieved axial insertion through the nodes and enabled smooth release in both the internodal and nodal regions.

This study calibrated key contact parameters for discrete element simulations of interactions among Panax notoginseng seedlings, lateritic soil, and 65Mn steel to address the lack of systematic multi-media parameter calibration methods. The researchers investigated three core contact interfaces using EDEM software, focusing on JKR surface energy, restitution coefficient, static friction coefficient, and rolling friction coefficient. They employed angle of repose tests, mixture collapse tests, and inclined plane rolling tests, combined with orthogonal design and response surface methodology, to establish correlation models between simulation and physical results. Through analysis of variance and multi-objective optimization, the team identified significant parameters and their optimal combinations for each interface. Validation showed that the optimized model exhibited relative errors below 7.5% across all tests, with prediction accuracy improved by over 30%. Field experiments further confirmed the models reliability, providing theoretical support for structural optimization of Panax notoginseng transplanters.

To address the low operating efficiency and uneven seeding associated with conventional seedling-production seeders, a cell-wheel seed-metering device was developed specifically for rapeseed blanket seedling production. The key components of the device were designed, and its seed-filling and seed-metering characteristics were analyzed. Using ‘Ningza 1838’ rapeseed as the test material, single-factor tests and multifactor response surface experiments were conducted. Seed-metering wheel rotational speed, seedling-tray conveying speed, and seed-box opening size were selected as the test factors, while the qualified seed-metering rate and missed-hill rate were used as the performance indicators. The results showed that the factors influencing the performance indicators, in descending order, were seed-box opening size, seedling-tray conveying speed, and seed-metering wheel rotational speed. The optimal parameter combination consisted of a seed-metering wheel rotational speed of 9.16 r·min?¹, a seedling-tray conveying speed of 0.45 m·s?¹, and a seed-box opening size of 22.80 mm. Under these conditions, the bench test yielded a qualified seed-metering rate of 94.41% and a missed-hill rate of 1.49%. These values were consistent with the predicted results, with deviations of less than 0.5 percentage points.

Research on electric tractors has expanded rapidly; however, previous reviews have generally examined energy sources, powertrains, intelligent control, and supporting infrastructure separately. This narrative review establishes an integrated technology-chain framework encompassing energy supply, electric drive systems, energy management, power take-off (PTO) and hitch control, autonomous operation, simulation, standardization, and industrial deployment. The available evidence was synthesized and compared in terms of system performance, advantages, limitations, and technological maturity. The results indicate that battery-electric systems can reduce energy consumption and life-cycle environmental impacts but remain constrained by limited operating endurance, high battery costs, thermal-safety risks, and insufficient charging capacity. Hybrid and fuel-cell powertrains offer greater adaptability to varying operating conditions but introduce additional system complexity. Future research should prioritize standardized duty cycles, integrated simulation frameworks, modular energy-replenishment systems, reliability testing, and interoperability standards.

With the increasing refinement of livestock feeding management, conventional manual feeding and record-keeping methods often fail to ensure reliable confirmation of individual animal arrival and accurate identity matching. This may result in ineffective feeding, feed misdelivery, missed feeding, and deviations from the prescribed feed amount. To address these limitations, an intelligent animal feeding system based on multisource information acquisition was developed. The system integrates camera-based monitoring with microphone-array sound-source localization to support target positioning and combines radio-frequency identification (RFID)-based identity verification with weight-based arrival confirmation to enable individualized feeding while minimizing animal–target mismatches. The system also collects body weight and environmental data to generate traceable individual records and supports data transmission and command delivery through an ESP8266 module using the Message Queuing Telemetry Transport (MQTT) protocol. A WeChat mini-program is integrated into the platform to provide remote control, feeding-plan configuration, and data visualization, as well as AI-assisted recognition and question-answering functions. Experimental results demonstrated that the proposed system successfully integrates intelligent feeding with multisource information acquisition. Under the combined audio–visual configuration, the target-positioning success rate reached 92.67%, while the combined RFID–weight configuration achieved a trigger accuracy of 93.33%. These results indicate that the system can support refined feeding management and improve process traceability.

Agricultural plastic mulch films have significantly increased crop yields but have also caused severe residual film pollution. This review summarizes the research progress in residual film recovery machinery, intelligent monitoring systems, film–impurity separation technologies, and resource utilization methods, while analyzing the advantages, limitations, and development trends of different technologies. Post-harvest recovery equipment has been widely applied due to its strong adaptability and broad application range, whereas seedling-stage and pre-sowing recovery technologies still require improvements in automation and environmental adaptability. Intelligent monitoring technologies can enhance the evaluation of recovery processes, but their application is restricted by complex field environments. Vibrating, pneumatic, and water-washing separation technologies exhibit advantages in processing capacity, film purity, and impurity removal, respectively. Recycled pelletizing and wood–plastic composites are important resource utilization pathways, but they still face challenges related to cost and performance. In the future, residual film management will develop toward intelligent, efficient, and high-value utilization.

Due to the anisotropy and small size of wheat roots, as well as the neglect of shear process effects on mechanical properties in existing wheat root-soil composite (WRSC) models, the establishment and parameter calibration of WRSC models remain inaccurate, hindering the precise analysis of the microscopic effects of furrow openers on WRSC during no-till sowing. This study aimed to develop an accurate and reliable WRSC model based on the discrete element method (DEM) using rapid particle filling and the Hertz-Mindlin with bonding contact model. Plackett-Burman tests identified particle shear modulus, inter-particle bonding radius, and bonding stiffness as significant factors affecting the ultimate shear force of wheat roots. Steepest ascent tests narrowed the optimal parameter ranges, and Box-Behnken test results were used to establish a quadratic regression model, leading to the optimal parameter combination: particle shear modulus of 7.96 MPa, inter-particle bonding stiffness of 4.13 GN/m³, and bonding radius of 0.32 mm. Actual and simulated shear tests on WRSC were conducted, and key parameters between wheat roots and soil particles were calibrated via steepest ascent tests with the ultimate shear force on the cutter as the target value: static friction coefficient of 0.8, bonding stiffness of 11 MN/m³, and critical stress of 11 MPa. Validation showed a relative error of 7.35% between simulated and actual shear resistance, indicating that the proposed WRSC model and parameters can effectively simulate field operations such as furrowing and rotary tillage.

A chunking strategy based on YOLOv12n has been proposed in this study to address challenges of young fruit detection for entire trees in dwarf and densely planted apple orchards, such as small young fruit volume, high similarity between fruit skin color and surrounding young leaves. This strategy divides an image into sub-images before feeding them into the network for training. Experimental results show that, compared with using only the YOLOv12n model for whole-image recognition, the chunking strategy achieved a precision of 85.57% and a recall of 62.54% in young fruit detection. In contrast, the YOLOv12n model alone, due to ineffective small-target capture and missing edge features, achieved a detection precision of less than 1% for young fruits, making effective detection nearly impossible. The introduction of the chunking strategy achieved a qualitative leap in young fruit detection performance. This strategy provides technical support for precise young fruit counting, subsequent accurate thinning, and growth dynamic monitoring in dwarf and densely planted orchards, and also offers a new implementation approach for small-target detection scenarios in orchards.

This study develops an artificial neural network (ANN) model to optimize the convective drying process of pear slices. Experimental data from drying at 50°C, 60°C, and 70°C were used to train an ANN with three hidden layers. The model predicted drying rate with high accuracy (98.823% validation fidelity), capturing the nonlinear relationship between moisture content, time, and drying rate. Results demonstrated that higher temperatures accelerated drying but required careful control to maintain quality. The ANN effectively identified optimal drying parameters, balancing energy efficiency and product quality preservation, providing a valuable tool for industrial drying process optimization.

A novel buckwheat dehulling device consisting of a rotating drum and a pressure deck with an elastic contact surface was developed to eliminate the need for grain size classification prior to dehulling. The geometry of the pressure deck was designed to match the shape of buckwheat grains, ensuring stable contact and effective dehulling across the commercial grain size range of 3–5 mm. The performance of the device was experimentally evaluated using a three-factor rotatable central composite design. The effects of drum rotational speed, working gap, and grain moisture content on the dehulling degree were investigated, and a quadratic regression model was established. The results showed that the maximum dehulling degree of 66.08% was achieved at a drum rotational speed of 846 rpm, a working gap of 4.2 mm, and a grain moisture content of 13.86%. The developed device demonstrates that optimizing the geometry and elastic properties of the pressure deck can significantly improve buckwheat dehulling efficiency while eliminating the need for prior grain size classification.

Wind-blown sand erosion threatens agricultural-vehicle lamp covers, yet the effects of wind speed and yaw angle have not been sufficiently quantified. A one-way coupled Eulerian–Lagrangian CFD model of a full-scale isolated polycarbonate (PC) lamp cover was combined with gas-jet erosion tests on flat PC specimens for trend-level comparison. Wind speeds of 10–30 m/s and yaw angles of -30° to 30° were investigated, with erosion predicted using the Oka model. Erosion was concentrated near the windward leading edge and downstream surface, whereas the maximum pressure remained closer to the stagnation region. The mean and maximum erosion rates followed power-law relationships with exponents of 2.70 and 2.31, respectively. From -30° to 30°, the mean erosion rate increased by 28.3%, 57.0%, and 13.9% at 10, 20, and 30 m/s, respectively. At 30 m/s, the maximum erosion rate increased by 55.8% from 0° to 30°. At 20 m/s, the erosion concentration factors were 13.31, 5.45, and 9.54 at -30°, 0°, and 30°, respectively. Over the same yaw-angle range, the experimental mass loss increased by 212.5%, 115.4%, and 88.9%, while the surface roughness increased by 23.9%, 18.9%, and 20.1%. After normalization, the numerical and experimental indicators exhibited consistent yaw-angle-dependent trends (R² = 0.9481, RMSE = 0.0822, MAE = 0.0671). Within the investigated conditions, the model successfully captured the relative erosion trends, and the combined indicators supported the assessment of localized erosion risk.

A novel buckwheat dehulling device consisting of a rotating drum and a pressure deck with an elastic contact surface was developed to eliminate the need for grain size classification prior to dehulling. The geometry of the pressure deck was designed to match the shape of buckwheat grains, ensuring stable contact and effective dehulling across the commercial grain size range of 3–5 mm. The performance of the device was experimentally evaluated using a three-factor rotatable central composite design. The effects of drum rotational speed, working gap, and grain moisture content on the dehulling degree were investigated, and a quadratic regression model was established. The results showed that the maximum dehulling degree of 66.08% was achieved at a drum rotational speed of 846 rpm, a working gap of 4.2 mm, and a grain moisture content of 13.86%. The developed device demonstrates that optimizing the geometry and elastic properties of the pressure deck can significantly improve buckwheat dehulling efficiency while eliminating the need for prior grain size classification.

To address the instability of trajectory tracking in unmanned orchard inspection vehicles caused by satellite navigation signal attenuation and track slippage, an integrated control method based on pose correction and an improved Stanley controller was proposed. First, a pose-correction algorithm based on the interacting multiple-model extended Kalman filter (IMM-EKF) was developed. By incorporating the nonlinear dynamic characteristics of tracked vehicles, the algorithm improved the robustness of pose estimation under varying motion states and measurement conditions. Second, an improved Stanley trajectory-tracking controller incorporating feedforward control and dynamic parameter adjustment was designed based on the corrected pose estimates. By accounting for path curvature in advance, the controller optimized the control commands and reduced lateral and heading errors. Simulations conducted using the Robot Operating System (ROS) platform and field tests performed in an actual vineyard were used to evaluate the proposed method. At a representative operating speed of 1.0 m/s, the mean lateral error during continuous multi-route trajectory tracking was 0.019 m, and the mean root-mean-square error (RMSE) was 0.021 m. These results demonstrate that the proposed method can improve trajectory-tracking accuracy, stability, and smoothness under the tested orchard conditions.

The number of main stem nodes in soybean is an important phenotypic trait for evaluating plant growth, varietal characteristics, and breeding potential. However, automatically detecting soybean main stem nodes under field conditions remains challenging because of plant occlusion, variations in illumination, and complex backgrounds, while manual counting is inefficient for large-scale phenotypic data acquisition. Therefore, this study proposes BGE-YOLOv11, a lightweight model for detecting soybean main stem nodes in complex field environments. Using an image dataset of soybean main stem nodes collected under field conditions, the model was developed by integrating a bidirectional feature pyramid network (BiFPN), Global–Local Self-Attention (GLSA), and EfficientHead into the YOLOv11n baseline to enhance multiscale feature representation and detection performance. The proposed model was evaluated in terms of precision, recall, mean average precision at an intersection over union threshold of 0.5 (mAP50), and model complexity. BGE-YOLOv11 achieved a precision of 94.9%, recall of 92.9%, and mAP50 of 97.6%, representing improvements of 3.9, 4.0, and 3.0 percentage points, respectively, over the YOLOv11n baseline. Moreover, the model contained only 1.87 million parameters, required 5.6 GFLOPs, had a memory footprint of 8.0 MB, and achieved an inference time of 3.0 ms. These results demonstrate that BGE-YOLOv11 improves detection performance while retaining a lightweight architecture. Nevertheless, its performance may be affected by severe occlusion and highly variable field conditions. Future research will focus on expanding the dataset and further improving the model’s generalization ability. This study provides an effective method for the automated counting of soybean main stem nodes and the analysis of phenotypic traits.

This study focuses on the development of a multifunctional agricultural robotic platform. Previous articles in this series established the need for such a platform based on the specific soil and climatic conditions of the Republic of Armenia. Tests of a laboratory prototype indicated the need for further investigation of the platform’s dynamic behavior, particularly during operation in orchards and agricultural fields on sloping terrain, which is common in Armenia. Particular attention was given to changes in platform inclination during turning maneuvers on slopes and to the yaw moment generated about the vertical axis in four-wheeled robotic platforms with independently controlled wheels. Based on a mathematical model of the chassis incorporating elastic elements (springs) and damping elements (shock absorbers), the differential equation governing the motion of the platform’s center of mass was formulated, together with moment equations about the transverse and longitudinal axes passing through the center of mass. Analysis of these equations yielded the differential equations governing the platform’s vertical oscillations. Expressions were subsequently derived for the displacement, velocity, acceleration, and jerk associated with the platform’s free oscillations. These equations can be used to evaluate ride smoothness and determine the spring stiffness and shock-absorber damping coefficient required to achieve the desired suspension performance.

A study was carried out to evaluate the performance of mobile and stationary fish smoking technologies. The experiment was conducted using a randomized block factorial design, with smoking temperature as a blocking factor. According to the investigation findings, at a smoking temperature of 90? and a smoking time of 4 hours, the maximum moisture content reduction of 81.4% was noted. In contrast, at a smoking temperature of 70 ? and a smoking time of 2 hours, the minimum moisture content reduction of 57.6% was noted for the electric fish smoker. Based on the test results of the electric fish smoker, at 90 ? of smoking temperature and 4 h of smoking time, the maximum drying rate of 0.69 kg h-1 was obtained. The findings verified that the maximum thermal efficiency of 69.8% was recorded at a smoking temperature of 90 ?, and a smoking time of 2 h for the electric fish smoker. Based on comparative evaluation results, the moisture content reduction, drying rate, fuel consumption and thermal efficiency for the fuelwood fish smoker were found to be 66.17%, 0.38 kg h-1, 4.8 kg kg-1 fish, and 37.9%, respectively. The study demonstrated that electric fish smoking technology significantly outperformed fuelwood smoking technology across all evaluated parameters. This study revealed that the electric fish smoking technology is strongly recommended for demonstration and scaling up to end users, particularly in areas where traditional fish smoking methods remain dominant.

Winter potato cultivation in paddy fields in southern China can improve land-use efficiency and increase farmers’ incomes. However, compared with those in northern China, paddy soils in southern China are generally heavy, cohesive, and compacted, resulting in poor mechanized potato-harvesting performance and restricting the development of the potato industry. To improve potato-soil separation efficiency under heavy clay soil conditions, a twin-roll soil-crushing device was installed ahead of a conventional chain-and-rod potato-soil separation device based on the characteristics of clayey soils in southern China. This configuration formed a two-stage separation process involving soil crushing followed by screening. The soil-crushing process was theoretically analyzed, a discrete element model of the soil-crushing and screening device was established, and the soil-crushing and potato–soil separation processes were numerically simulated. Response surface simulation experiments were conducted using rib height, number of ribs, and twin-roll rotational speed as the experimental factors, with soil-screening efficiency and the maximum force acting on the potato tubers as the response variables. The results showed that the twin-roll soil-crushing device effectively disturbed and crushed the soil during operation. Increasing the rib height and twin-roll rotational speed enhanced the soil-disturbance and crushing effects. The optimal structural and operating parameters of the twin-roll soil-crushing device were a rib height of 30 mm, three ribs, and a twin-roll rotational speed of 323.70 r/min. A simulation verification test conducted using these optimal parameters yielded a soil-screening efficiency of 72.80% and a maximum force acting on the potato tubers of 15.45 N. Field-test results showed that the potato harvester equipped with the soil-crushing device achieved a tuber damage rate of 0.285%, a skin-damage rate of 2.38%, and a tuber loss rate of 3.18%, thereby meeting the requirements of the applicable national standards. These findings provide a reference and technical support for the development of potato-harvesting machinery suitable for heavy clay soil conditions.

To investigate the concentration of molasses ethanol wastewater, a two-dimensional numerical model of falling-film evaporation over horizontal tubes was developed. The model used the Volume of Fluid (VOF) method to capture the liquid-gas interface and was coupled with the Lee phase-change model. The model was validated by comparing the predicted liquid-film thickness and average heat-transfer coefficient with experimental data reported in the literature. The validated model was then used to systematically investigate the effects of spray density (0.10 ~ 0.30 kg/(m·s)) and heat-transfer temperature difference (2 ~ 8 °C) on the falling-film flow and evaporation performance of molasses ethanol wastewater. The results showed that the liquid-film thickness initially decreased and subsequently increased along the circumferential direction, reaching a minimum within the angular range of 90° ~ 120°. Increasing the spray density produced a thicker liquid film; nevertheless, the average heat-transfer coefficient increased monotonically by 23.0% ~ 27.2%, whereas the mass-transfer rate decreased by 19.3% ~ 25.1%. Increasing the heat-transfer temperature difference increased both the average heat-transfer coefficient and the mass-transfer rate. When the temperature difference increased from 2 to 8 °C, the mass-transfer rate increased 2.84 ~ 3.07-fold, and its effect on evaporation performance was substantially greater than that of spray density. Therefore, where operating conditions permit, a lower spray density combined with a higher heat-transfer temperature difference is recommended for the falling-film evaporative concentration of molasses ethanol wastewater.

Aiming at autonomous weeding in narrow, complex orchards, this paper proposes an improved A* and DWA fusion path planning algorithm. Globally, a safety distance penalty in the A* cost function prevents paths from adhering to tree trunks. Locally, enhanced DWA modules, including dynamic obstacle angle thresholds, hierarchical lateral braking, and adaptive velocity window expansion, resolve trajectory oscillation and local minima. Validated in real orchards, the algorithm achieved a 0.029 m tracking deviation and over 95% dynamic obstacle avoidance success rate, providing robust and safe navigation for autonomous robots.

Egg appearance defect detection in conveyor-belt environments is challenged by irregular defect morphology, illumination variations, and the limited computational resources of intelligent sorting equipment, making it difficult to simultaneously achieve high detection accuracy and lightweight deployment. In this study, a lightweight detection model, termed YOLOv11n-DAL, is proposed to improve egg defect detection performance while reducing computational complexity. The proposed model integrates C3k2_DCNv4, ADown, and Detect_LSDECD modules into the YOLOv11n framework to enhance defect feature representation, improve effective information retention during downsampling, and reduce computational redundancy in the prediction stage. A conveyor-belt egg image dataset containing three categories, namely damaged, dirty, and good, was constructed. The dataset consisted of 1,730 original images, and the training set was expanded to 4,152 images through data augmentation. Comparative experiments and ablation studies were conducted to validate the effectiveness of the proposed model. The experimental results demonstrated that YOLOv11n-DAL achieved 95.2% Precision, 94.5% Recall, 94.8% F1-score, 96.7% mAP@0.5, and 95.8% mAP@0.5:0.95. Compared with the original YOLOv11n model, YOLOv11n-DAL reduced the number of parameters from 2.6 M to 1.7 M, GFLOPs from 6.3 to 4.8, and model size from 5.2 MB to 3.8 MB, while maintaining superior detection performance. The results indicate that YOLOv11n-DAL achieves an effective balance between detection accuracy and computational efficiency, providing a lightweight and practical solution for intelligent egg sorting systems in conveyor-belt environments.

To address the problems of multiple seeding, missed seeding, and insufficient operational stability commonly encountered during the operation of precision maize seed-metering devices, an inclined spoon-disc seed-metering device was designed in this study. The seed-metering process of maize kernels within the device was analysed, and the key components, including spoon wheel, partition plate, and seed-metering wheel, were structurally designed. Bench tests were conducted using a JPS-16 seed-metering device performance testing system. First, single-factor tests were performed to investigate the effects of metering-wheel rotational speed, overall inclination angle, and seed-layer height on the qualified index, multiple index, and miss index. Subsequently, a three-factor, five-level quadratic regression orthogonal rotational combination experiment was conducted using the metering-wheel rotational speed, overall inclination angle, and seed-layer height as the experimental factors. Optimization was performed with the objective of maximizing the qualified index. The optimal parameter combination was determined to be a seed-metering wheel rotational speed of 25 r·min?¹, an overall inclination angle of 27°, and a seed-layer height of 71.9 mm. Under these conditions, the model-predicted qualified index was 96.5%. In the verification tests, the average qualified index was 95.0%, with a relative error of 1.5% compared with the predicted value. The results can provide a reference for the structural design and operating-parameter optimization of inclined spoon-disc precision maize seed-metering devices.

Seedling replenishment during the operation of high-speed ride-on rice transplanters still relies heavily on manual labor, thereby limiting the level of operational automation. To address this limitation, an automatic seedling-loading system comprising a longitudinal conveying mechanism, a lateral drive mechanism, and a tracking compensation mechanism was developed. Based on an STM32 embedded platform, a control strategy for detecting seedling shortages and performing precise seedling-loading operations was developed, enabling automatic shortage detection, lateral positioning, and targeted seedling replenishment. The main operating parameters—conveyor belt speed, lateral offset distance, and fore-aft tilt angle—were first investigated individually to determine their appropriate ranges. Subsequently, a second-order, three-factor, five-level rotatable orthogonal experiment was conducted. Seedling-loading success rate was used as the evaluation index, and a multifactor regression model was established to analyze the interactions among the factors. Bench tests were conducted to optimize the system parameters and determine the optimal parameter combination. Subsequent field trials confirmed the operational reliability of the system, with an average seedling-loading success rate of 90.67% over five batches of tests. The proposed system shows considerable potential for replacing manual loading of mat-type rice seedlings and provides a practical solution for advancing the full-process automation of rice transplanting operations.

To address the lack of contact parameters for seed ginger and the insufficient accuracy of existing discrete element models used in simulations of seed ginger machinery, a discrete element modeling and contact parameter calibration method for irregularly shaped seed ginger was proposed. A discrete element model of seed ginger was constructed using 3D laser scanning and an adaptive multi-sphere filling method. The contact parameters between seed ginger and steel were calibrated using free-fall collision tests, inclined-plane tests, and combined inclined-plane–horizontal-plane tests. Using the angle of repose as the response variable, the optimal parameter combination was determined through steepest ascent tests and response surface methodology. The relative error between the simulated angle of repose and the value measured in the bench test was 2.24%. The results provide a modeling basis for the optimization and design of key components of seed ginger machinery and for investigating the dynamic interaction mechanisms among seed ginger particles.

The key challenge in Brassica napus combine harvesting is achieving a balance between threshing rate and impurity rate, both of which are strongly influenced by stem breakage. This study investigated the breakage characteristics and static mechanical properties of Brassica napus stems from the middle and lower reaches of the Yangtze River using axial tensile tests combined with digital image correlation (DIC). The results showed that the fiber layer area of rapeseed stems increased with stem diameter, and stem breaking force was significantly positively correlated with stem diameter. For stem diameters ranging from 7 to 16 mm, the breaking force ranged from 166.82 to 970.82 N and was described by the relationship Fb=42.064dx1-92.894. The tensile strength of rapeseed stems ranged from 25.1 to 64.6 MPa, and a quadratic response surface model relating tensile strength to stem diameter and moisture content was established. DIC measurements of strain in the radial and axial orthogonal directions showed that axial deformation was substantially smaller than radial deformation, with an average axial strain of ey=4.22±2.23×10-3 and an average radial strain of ex=3.84±2.17×10-2. These findings improve the understanding of the mechanical response of rapeseed stems under tensile loading and provide a theoretical basis for optimizing threshing devices for efficient, low-damage rapeseed harvesting.

To meet the maneuverability and terrain-adaptability requirements of agricultural machinery operating in hilly and mountainous areas, an electronically controlled tracked chassis was designed and developed. Based on a theoretical analysis of chassis terrain-crossing capability and stability, a multibody dynamic model was established using RecurDyn to simulate the dynamic response of the chassis under typical operating conditions. The chassis trajectory and variations in dynamic stability were analyzed. A physical prototype was then manufactured and tested under multiple operating conditions on actual road surfaces. The chassis achieved a maximum straight-line travel speed of 4 km/h on level ground, a maximum uphill and downhill travel speed of 2 km/h, a maximum climbing angle of 18°, a maximum trench-crossing width of 500 mm, and a turning radius not exceeding 675 mm. The simulation and experimental results were in close agreement, with a maximum relative error of 11%, demonstrating the predictive accuracy of the multibody dynamic model. The developed chassis exhibited good terrain-crossing capability and operational stability. This study provides a theoretical basis and technical support for the development and application of small agricultural machinery in hilly and mountainous areas.

Sustainable management of gray mold (Botrytis cinerea) in viticulture necessitates eco-friendly alternatives to synthetic fungicides to alleviate chemical residue risks and combat emerging pathogen resistance. This study evaluated the biocontrol potential of botanical extracts derived from Allium sativum, Lavandula angustifolia, Salvia officinalis, Cannabis sativa, and Melissa officinalis, obtained via an optimized hybrid ultrasound-assisted percolation process. The optimal extraction parameters were determined to be a particle size of 30 mm, a solid-to-liquid ratio of 1:30, a system pressure of 7 bar, and an ultrasound power of 120 W over a 120-minute extraction duration, achieving high polyphenolic yields while maintaining process energy efficiency. In vitro antifungal screening via the poisoned food technique revealed that A. sativum extract exhibited the most potent inhibition against B. cinerea (78.62% on day 2 and 39.84% on day 7 at a 50% v/v concentration), followed by L. angustifolia (69.62% on day 2). In vivo evaluations on Vitis vinifera L. corroborated a concentration-dependent reduction in disease incidence following post-inoculation application. However, a progressive temporal decline in antifungal efficacy was observed across all crude extracts, highlighting the critical need for microencapsulation strategies or condensed application intervals (3–5 days) under field conditions. Overall, hybrid ultrasound-assisted percolation represents a highly efficient green extraction platform for recovering bioactive compounds suitable for integration into sustainable vineyard crop protection regimes.

To address parameter optimization and fertilization uniformity of a fluted-wheel fertilizer metering device for granular organic fertilizer, a 3D-scanned fertilizer model was constructed, and an EDEM fertilization simulation model was established. The Poisson’s ratio and elastic modulus of the fertilizer were determined using a texture analyzer. Drop tests were conducted to determine the coefficients of restitution between fertilizer particles and between the fertilizer and PA, PC, and PLA. Inclined-plane rolling tests were conducted to determine the rolling friction coefficients between the fertilizer and PA, PC, and PLA. A cylinder-lifting test for measuring the angle of repose, combined with EDEM simulations and a Design-Expert experimental design, was used to determine the static and rolling friction coefficients between fertilizer particles, as well as the static friction coefficients between the fertilizer and PA, PC, and PLA. By combining the discrete element method (DEM) with response surface methodology, the effects of the parameters on fertilization uniformity were ranked as follows: fluted-wheel inscribed-circle diameter > fluted-wheel lead angle > number of grooves; fertilizer-metering shaft rotational speed > fertilizer-discharge flap angle > effective working length of the fluted wheel. The optimal parameter combination was determined to be a fluted-wheel inscribed-circle diameter of 37 mm, 7 grooves, a fluted-wheel lead angle of 63°, an effective working length of the fluted wheel of 31 mm, a fertilizer-metering shaft rotational speed of 28 rpm, and a fertilizer-discharge flap angle of -1°. Soil-bin tests using 3D-printed fluted wheels with different structural parameters produced results that were generally consistent with the simulation results, providing a reference for the DEM-based optimization and design of fluted-wheel fertilizer metering devices.

Illegal dumping in peri-urban and natural environments poses persistent challenges for environmental authorities, particularly when large areas must be inspected with limited personnel and budget. Reliable and scalable monitoring approaches are essential for practical environmental management. Unmanned Aerial Vehicles (UAVs) offer rapid spatial coverage, yet it remains unclear how complex the analysis must be to reliably detect waste sites in practice. This controlled pilot study evaluated the operational reliability of three UAV-based image analysis approaches (global statistical screening, classical change detection with colour filtering, and deep learning object recognition) under controlled field conditions. The field experiment used controlled contamination conditions so that the evaluation reflected practical detection reliability rather than algorithm benchmarking. The statistical method could not reliably identify localised waste, while change detection achieved moderate reliability (~72%; 29 of 40 evaluated acquisitions) but required reference imagery. The deep learning model achieved the highest site detection reliability (~93%; 37 of 40 evaluated acquisitions) and worked without baseline images. Its main advantage was improved object completeness rather than better identification of contaminated areas. These observations showed that monitoring workflow design matters more than increasing algorithmic complexity. A tiered strategy that combines broad screening with focused high-accuracy analysis can reduce inspection effort without compromising the results of inspection. The evaluated pilot framework supported the selection of monitoring approaches according to available resources, and the findings apply to the controlled, low-altitude field conditions (15 m above ground level, single test site) examined here rather than to fully validated multi-site operation. REZUMAT

Radicle length is an important phenotypic trait for evaluating the growth-promoting effects of plant growth-promoting rhizobacteria on cucumber seeds. However, conventional manual measurement is time-consuming, labor-intensive, and prone to subjective errors, while existing radicle phenotyping systems still suffer from insufficient throughput. To address these limitations, this study proposes an automatic radicle length estimation method for germinated cucumber seeds based on GermHRNet. First, a YOLOv8-p2s model was used to automatically detect seed instances in Petri dish images. Subsequently, a GermHRNet keypoint detection network was developed to accurately localize radicle keypoints. A high-resolution cross-scale feature enhancement module and a dynamic coordinate feature enhancement module were introduced to improve feature representation and keypoint localization accuracy for slender and curved radicles. Finally, radicle length was estimated using a polyline-based pixel distance accumulation strategy according to the detected keypoint coordinates. Experimental results showed that the proposed method achieved an average precision, average recall, and percentage of correct keypoints of 90.6%, 92.2%, and 88.2%, respectively. The mean absolute error, root mean square error, normalized mean absolute error, normalized root mean square error, and coefficient of determination for radicle length estimation were 0.51 mm, 0.61 mm, 6.06%, 7.25%, and 0.98, respectively. Under a CPU-only environment without GPU acceleration, the proposed method achieved a throughput of 3.38 × 104 seeds/h. The proposed method enables rapid and accurate radicle length acquisition without specialized equipment and provides an efficient technical solution for large-scale microbial strain screening and seed phenotyping.

Accurate discrete element method (DEM) parameters are essential for simulating key operations in jujube postharvest processing, such as pneumatic cleaning during mechanical harvesting. The fundamental physical properties of Yimeng Mountain jujubes were systematically measured. A jujube particle template was constructed using the multi-sphere method, and the Hertz–Mindlin (no slip) contact model was adopted. The jujube simulation model was generated by filling the particle template with multiple spheres of different diameters. The static friction coefficient between jujubes ranged from 0.41 to 0.69, while that between jujubes and a steel plate ranged from 0.40 to 0.54. The rolling friction coefficient between jujubes ranged from 0.16 to 0.30, whereas that between jujubes and a steel plate ranged from 0.11 to 0.24. The coefficient of restitution between Yimeng Mountain jujubes, determined using a high-speed camera, ranged from 0.41 to 0.54, while that between jujubes and a steel plate ranged from 0.46 to 0.61. A Plackett–Burman screening design was conducted, and the results showed that the Poisson’s ratio of jujube, the static friction coefficient between jujubes, and the static friction coefficient between jujubes and the steel plate were the three dominant factors. The steepest ascent method was then used to narrow the ranges of these significant parameters. Subsequently, a Box–Behnken design was employed to establish a quadratic regression model relating the angle of repose to the three significant factors. Using the experimentally measured angle of repose of 28.08° as the optimization target, the optimal simulation parameters were determined as follows: a Poisson’s ratio of 0.32, a jujube–jujube static friction coefficient of 0.52, and a jujube–steel static friction coefficient of 0.47. The relative error between the simulated and experimentally measured angles of repose was 2.17%, further confirming the accuracy and reliability of the calibrated simulation parameters.

Liquid sloshing in pesticide tanks is a major factor affecting the stability of plant-protection unmanned aerial vehicles (UAVs). A double-layer floating plate was developed to improve sloshing suppression. A series of experiments was conducted to measure the sloshing-induced force under horizontal sinusoidal displacement excitation. The results showed that the double-layer floating plate provided superior sloshing suppression over a wider range of liquid depths. An intermediate-sized floating plate also significantly reduced the sloshing-induced force at specific liquid depths, making it suitable for installation within the limited space available inside pesticide tanks.

To address the high penetration resistance and severe soil adhesion of tobacco planting devices operating in wet, clayey soil, a biomimetic planting device was designed using a Gaussian multi-peak function and characteristic curves derived from multiple bird beaks. The peak parameters, including height, position, and width, were optimized using discrete element method (DEM) simulations and response surface methodology. The resulting biomimetic profile reduced penetration resistance and soil adhesion, thereby improving tobacco transplanting performance. The optimized prototype exhibited a penetration resistance of 18.7 N and an adhered soil mass of 14.98 g, representing reductions of 60.47% and 40.56%, respectively, compared with the conventional prototype.

Agricultural tractors are repairable mechanical systems whose service records can provide insight into how failure mechanisms evolve over their operating life. This study analysed 200 failure cases recorded for 75 Zetor tractors. Failure causes were classified into six mechanism categories and compared across four engine operating-hour classes. The relationship between operating-hour class and failure mechanism was evaluated using contingency tables, chi-square tests, Cramer’s V, odds ratios, and correspondence analysis. Sealing-related failures predominated in the dataset, whereas material/manufacturing and electrical causes were more prevalent during the early stages of operation, and wear-, friction-, and fatigue-related mechanisms became more prominent at higher operating hours. The findings provide a basis for service planning and the assessment of tractor reliability.

The utilization of machine vision and image processing techniques enables the recognition of corn stalks in complex field conditions, thereby enhancing the efficiency and accuracy of corn stalk identification. Initially, images of corn stalks are captured using a camera, followed by Gaussian filtering to effectively reduce noise within the images. Subsequently, RGB and HSV color segmentation is applied to the corn stalk images to isolate the component V, which most distinctly differentiates the corn stalks from the background. Threshold segmentation is then conducted on component V, establishing a threshold range to identify and extract the corn stalks from the background. Morphological operations are employed to enhance the fullness of the image. Finally, grid division is performed, using the coverage area of the straw as a feature to assign density values to corn straw in various sub-regions, ultimately generating a distribution grade map of corn straw. The results indicate that when the straw coverage rate ranges from 25% to 80%, the discrepancy between the corn straw recognition rate obtained through threshold segmentation and the manually measured straw coverage rate is less than 5%, satisfying the requirements for subsequent real-time variable spraying of corn straw decomposition agents.

This review examines tobacco transplanting machinery, focusing on regional adaptation, automated seedling handling, planting mechanisms, coupled modeling, intelligent sensing, and field robustness. A narrative search covered English- and Chinese-language literature and standards through June 2026, selecting evidence by tobacco relevance or transferable similarity. High-throughput automation suits standardized production; hilly areas require compact, maneuverable, agronomically adaptable machines. Progress includes compliant seedling picking, buffered conveying, zero-relative-velocity planting, machine–soil–seedling modeling, machine vision, and digital twins. Constraints include seedling variability, adhesive soils, slope-induced disturbances, sensing degradation, and life-cycle cost. Future systems should integrate nursery standards, robust perception, decision-oriented digital twins, and modular electrification.

This study combined two complementary criteria for evaluating a spiral cleaner for carrot roots: the spatial distribution of separated soil and the occurrence of mechanical damage. A laboratory test rig with a V-shaped cleaning trough formed by cantilevered spiral springs and driven brushes equipped with elastic rods was used. Nantes carrot roots were tested at cleaner-shaft angular velocities of 27.2, 31.4, and 35.6 rad·s?¹ and in two entry orientations: tail-end-first and thicker-end-first. Separated soil was collected in three consecutive zones along the transport path. Mechanical damage was assessed immediately after cleaning and again after 24 h using samples of 100 roots. Zone I accounted for 38–48% of the separated soil, Zone II for 33–41%, and Zone III for 11–28%. Increasing the angular velocity shifted soil separation toward the inlet section. The proportion of undamaged roots decreased from 66% to 44% for the tail-end-first orientation and from 75% to 59% for the thicker-end-first orientation. The thicker-end-first orientation resulted in 9–15 percentage points more undamaged roots and reduced the combined proportion of cracks, broken tail ends, and impact bruises from 13–25% to 5–14%. Under the tested conditions, an angular velocity of 27.2 rad·s?¹ with the thicker-end-first orientation was identified as the preferred low-damage operating condition. The results provide a basis for further multifactor optimization incorporating residual adhering soil, throughput, and energy consumption as additional evaluation criteria.

Airborne dust generated during straw pickup can impair operating visibility and local air quality; however, the respective contributions of mechanical particle detachment and aerodynamic entrainment in flail-type pickup headers remain insufficiently quantified. This study combined discrete element method (DEM) and computational fluid dynamics (CFD) analyses with field measurements to investigate these two processes. A bonded corn root stubble–soil DEM model was established in EDEM, and the bond fracture rate was used to characterize the relative degree of particle detachment. The effects of ground clearance (30–90 mm), rotor speed (4100–5300 rpm), forward speed (0.5–1.5 m/s), plant spacing (200–300 mm), and row spacing (300–500 mm) were evaluated. CFD simulations were then used to characterize the time-averaged airflow field within the pickup-header housing. Increasing the ground clearance from 30 to 90 mm reduced the bond fracture rate by 44.5%, whereas increasing the rotor speed from 4100 to 5300 rpm increased it by 52.3%. By relating the DEM particle-detachment states to the CFD flow-field characteristics, three dust-generation pathways were identified: stubble fragmentation, root extraction, and direct entrainment of loose surface particles. Rotor speed affected both mechanical detachment and airflow entrainment at the header inlet. Field measurements showed that the selected low-dust operating condition, with a ground clearance of 75 mm, rotor speed of 4400 rpm, and forward speed of 0.75 m/s, reduced total suspended particulate (TSP), PM10, and PM2.5 concentrations by 62.2%, 58.4%, and 53.8%, respectively, compared with the dust-intensive condition. By linking particle detachment, airflow entrainment, and field measurements within a mechanism-based framework, this study clarifies the effects of operating parameters on dust generation and provides a basis for selecting low-dust operating parameters and improving pickup-header inlet design.

To address the problems of low manual pickup efficiency and severe soil accumulation in existing shovel-type pickup devices used for segmented potato harvesting, a composite pickup device consisting of a grid-type segmented pickup shovel and a flexible lifting wheel was designed. A potato-soil-machine discrete element simulation model was established using EDEM to optimize structural parameters, including grid spacing, and to investigate the effects of operating parameters, including forward speed, digging depth, and lifting-wheel speed, on pickup performance. The simulation results showed that grid spacing was negatively correlated with the loss rate and positively correlated with the damage rate. An optimized grid spacing of 25 mm reduced the damage rate to 2.21% and the loss rate to 3.70%, while effectively reducing potato jamming. As the forward speed and lifting-wheel speed increased, the loss rate decreased, whereas the damage rate increased. Digging depth had a nonlinear effect on the loss rate, which initially decreased and then increased, reaching its minimum at a digging depth of 100 mm, whereas the damage rate continuously decreased with increasing digging depth. Field tests yielded an average loss rate of 3.88% and an average damage rate of 2.24%, meeting the operational requirements for potato harvesting.

Post-mining substrates are spatially heterogeneous and require site-specific diagnosis before biomass cultivation. This study documents the establishment and three-year monitoring of a 4,500 m² field platform on a coal-mining waste dump in Petrila, Romania. Nine 500 m² plots were used: three fixed-crop reference plots (designated as controls in the original project records) and six plots under a balanced cyclic rotation of maize (Zea mays), soybean (Glycine max) and sweet sorghum (Sorghum bicolor). Amendment treatments formed part of the wider experimental layout but are not evaluated in the present manuscript. Baseline measurements at eight points in June 2023 included instrument-reported volumetric water content (VWC), temperature, penetration resistance, field pH and semi-quantitative N-P-K screening, complemented by preliminary potentially toxic element screening. Mean VWC was 26.6 ± 4.2% at 5 cm and 20.0 ± 4.3% at 15 cm; mean temperatures were 18.2 ± 1.3 and 16.7 ± 1.1°C. Mean penetration resistance across seven complete profiles was 1.05 ± 0.28 MPa and increased with depth. Field pH averaged 7.1 ± 0.1, and the available laboratory summary prioritised Cu and Ni for follow-up. All crops reached reproductive development. Preliminary operational harvest totals were 85, 263 and 487 kg in 2023, 2024 and 2025, respectively; these values are reported descriptively only and are not interpreted as crop yield because harvested area, crop-specific contributions and moisture basis were not documented consistently. The nine crop-year elemental records are likewise treated as single descriptive observations displaying apparent crop-year differences in the reported Cu, Ni, Zn and Pb values; no inferential comparison among crops or years and no conclusion on soil-to-plant transfer or bioaccumulation is made.

Precision agriculture requires advanced digital solutions capable of continuously monitoring environmental conditions and pest dynamics in orchard ecosystems. This paper presents the development, integration, and experimental validation of a digital system dedicated to orchard crop monitoring. The proposed system integrates a smart pheromone trap equipped with an HD camera, a meteorological monitoring station, an autonomous photovoltaic power supply system, and a wireless communication module for real-time data transmission. The experimental model was designed to support automatic pest detection and continuous acquisition of climatic parameters associated with pest and pathogen development. The monitoring architecture combines environmental sensing, image acquisition, local data processing, and cloud-based communication. Functional testing was conducted under controlled orchard conditions over a 30-day monitoring period. The obtained results demonstrated stable autonomous operation, reliable wireless communication, and accurate environmental monitoring. The average deviations compared with a reference meteorological station were within ±0.4 °C for air temperature, ±2.5% for relative humidity, and ±0.3 m/s for wind speed. The smart trap achieved pest identification accuracy higher than 90% for target species. The photovoltaic system ensured autonomous operation for more than 72 h without direct solar radiation. The developed digital platform represents a scalable solution for integrated pest management and precision orchard monitoring applications.

This study compares energy and exergy performance of two vapour-compression refrigeration systems designed to provide simultaneous refrigeration and freezing at two distinct temperature levels for food-industry applications. The simulation results indicated that R1234yf provided the best overall performance for both configurations. The exergy analysis showed that the throttling processes in the first configuration had the fastest increase in exergy destruction with the variation of ambient temperature. The structural improvement of the second configuration led to an increase in overall system performance, confirming the importance of refrigerant selection and structural optimisation of the refrigeration system. For R1234yf, the energy coefficient of performance increased from 2.69 to 2.97 at an ambient temperature of 35 °C and from 1.98 to 2.24 at 45 °C when configuration (b) was used instead of configuration (a).

The quality inspection of sorghum grains after drying is critical for ensuring storage stability and processing quality. However, traditional manual inspection methods are inefficient, time-consuming, and prone to subjective biases. To address these challenges, this study proposes an improved YOLOv8s model, named YOLOv8s-GBD, for accurate sorghum grain classification and detection. The proposed model integrates three key enhancements. First, the DFC Attention-based bottleneck structure from GhostNetV2 is incorporated into the C2f module to enhance global context awareness and improve detection accuracy. Second, a bi-directional feature pyramid network (BiFPN) is introduced to optimize multi-scale feature fusion. Third, a dynamic head framework based on attention mechanisms is adopted to strengthen feature representation, further boosting the model’s accuracy and robustness. Experimental results demonstrate that the YOLOv8-GBD model achieves 95.2% precision, 98.0% recall, 98.7% mAP1, 97.5% mAP2, and 96.6% F1-score. Compared to the original YOLOv8s model, these metrics show improvements of 3.1%, 1.7%, 1.2%, 1.0%, and 2.4%, respectively. Furthermore, the YOLOv8-GBD model outperforms other YOLO series models in sorghum grain classification and detection tasks. In conclusion, the YOLOv8s-GBD model meets the requirements for high accuracy in sorghum grain quality inspection, offering a robust solution for practical applications.

The seedling stage is a relatively short but critical period in the maize growth cycle, during which rapid weeding, pesticide application, and fertilization are required. Crop row centreline extraction technology based on computer vision can assist agricultural machinery in performing automated field operations, thereby improving work efficiency. Therefore, improving the accuracy of crop row centreline extraction imposes higher requirements on existing models and algorithms. In this study, a model named Synergistic Attention YOLO (SA-YOLO) was proposed. The feature extraction capability for crop rows was enhanced by introducing the spatial and channel synergistic attention module and designing the A2C2f module integrated with switchable atrous convolution. In addition, a crop topological skeleton feature point extraction strategy and a collaborative fitting method combining principal component analysis and least squares was integrated to accurately extract crop row centrelines from the skeleton feature points of crop pixels within the segmented region of interest. Comparative experiments showed that the SA-YOLO model achieved competitive performance, with an average precision at IoU threshold 0.5 (AP50) of 99.1%, and a mask Intersection over Union (IoUmask) of 83.8%, while maintaining a lightweight architecture with only 3.72 M parameters. The mean angle error (??????????) of crop row centerline extraction was 0.590°, and the mean normalized lateral error (??????????????????) was 0.357%. This study provides a new approach for exploring crop row centerline extraction in agriculture and can further enrich the theoretical and technical foundation for visual navigation of agricultural robots.

This study explores the potential of harvesting and utilizing thermoelectric energy generated during biochar production. Biochar, a carbon-rich material produced from biomass pyrolysis, offers a sustainable pathway for waste management and soil improvement. However, the biochar production process also generates significant heat, much of which remains underutilized. This research investigates the integration of thermoelectric generators (TEGs) into biochar production equipment to convert waste heat into electricity. The systems design and optimization aim to enhance energy efficiency while supporting renewable energy goals, by using the harvested energy to power up small devices, such as sensors. Experimental results demonstrate the feasibility of capturing thermal energy and converting it into usable electrical power, contributing to the overall sustainability of the process. The experimental results revealed a maximum output voltage of 1.226 V, recorded at a temperature gradient of approximately 42.0 °C, a peak power output of approximately 37.6 mW, an overall maximum temperature gradient of approximately 42.8 °C reached slightly before the voltage peak, and a cumulative harvested energy of 288.92 J. The findings also highlight key factors influencing thermoelectric conversion efficiency, including temperature gradients, TEG material properties, and system integration challenges. This approach not only mitigates energy losses but also aligns with global efforts toward carbon neutrality and resource optimization. The study underscores the dual benefit of biochar production: providing agricultural benefits while supporting energy sustainability.

To address the environmental pollution caused by discarded non-biodegradable fruit bags in orchards, an air-suction orchard fruit-bag collection machine was developed. First, theoretical calculations and analyses were conducted on the key machine components to determine the main operating parameters. Kinematic analysis was then performed using ADAMS, and the airflow field was simulated using Fluent. Next, a prototype was manufactured. Based on multivariable response surface analysis conducted in Design-Expert, the optimal parameter combination was identified as a travel speed of 2 km/h, a suction-inlet air velocity of 15.5 m/s, and a helical-conveyor rotational speed of 66 r/min. Orchard field tests showed that, under the optimal operating conditions, the actual collection rate reached 82.5%, confirming that the machine met the intended design requirements.

To address the low quality of mechanized wheat seeding in rice-stubble fields in the middle and lower reaches of the Yangtze River, caused by high soil moisture content and heavy straw coverage, a press-groove uniform seeding machine was designed. Key mechanisms for rotary tillage, precision seedbed preparation, depth-controlled furrow formation, and banded soil covering were developed, and the overall transmission system and blade-roller arrangement were designed to create a wheat seeder suitable for high-moisture rice-stubble fields. Field tests showed that the vegetation coverage rate after operation was 94.2%, the qualified rate of seeding depth was 98.0%, and the coefficient of variation of seeding uniformity was 18.1%. The machine effectively reduced seed skips and exposed seeds during mechanized seeding in high-moisture fields.