To address the challenges and needs of the CEA (Controlled Environment Agriculture) farms, a complex and multi-purpose sensor robotic platform was developed. The objective was to solve the problem of providing a complete set of visual and numeric information in regards to operational environment and specific points of interest within the environment. In this article, an experimental sensor robotic platform model was constructed and tested by integrating technologies such as LiDAR (Light Detection and Ranging) mapping via ROS (Robot Operating System), point-cloud, RGB and IR imaging and image processing algorithms developed with OpenCV (Open Computer Vision) libraries. Real-time control and environment assessment were achieved by integrating an internet access point within the structure of the experimental model. Experiments show that a multi-sensory integration and operation can be successfully achieved within a compact and energy efficient robotic platform, reaching six hours of autonomy The LiDAR-based experiments show that the proposed system can achieve a ±7 ???? mapping precision, greatly enhancing the operation within the environment. Furthermore, the RGB, IR imaging, point-cloud and image processing algorithms proved to optimize the assessment and monitoring operations by providing valuable and precise visual information. The final results show that the proposed solution has great performance in controlled environments and can improve the safety and overall efficiency of CEA farms and related environments.

Addressing the critical challenge of energy efficiency loss in micro-cultivator transmission systems within Chinas hilly terrains, this study develops a direct-connected four-wheel-drive transmission system to enhance efficiency and reliability. Based on agronomic requirements and functional demands for gear positions, a combined gear-chain transmission scheme was implemented, enabling coordinated control between traveling and tilling units, supporting five operational modes, including fast/slow tillage and reverse gear. Through optimized gear parameters and structural configuration, along with carbonitriding treatment and micro-geometry optimization of 20CrMnTi gears, the system achieves significantly improved load capacity and compactness. Integrated validation with KissSoft and Romax that gear safety factors exceed design targets, shaft and bearing lifespan meet the 1,000-hour requirement, and the simulated transmission efficiency of 92.2% surpasses conventional pulley systems. Field verification demonstrates 78.58% soil fragmentation and 90.20% tillage depth stability at a forward speed of 3.46 km/h with a ploughing depth of 22.93 cm, meeting agronomic standards for micro-cultivator operations. This research provides a technical reference for transmission system design in small hillside agricultural machinery.

To address the issue of insufficient parameters in the simulation of processes such as barley seedling stem harvesting using the discrete element method, this study focused on barley seedling stems of the Plante variety. Based on EDEM software, rigid (Hertz-Mindlin no slip model) and flexible (Hertz-Mindlin with bonding model) discrete element models were established. Combined with the results of physical tests, including the angle of repose (20.42°) and the average maximum load of three-point bending (2.62 N), parameters were screened and optimized through tests such as Plackett-Burman. The results showed that under the optimal contact parameter combination of the rigid model, the simulated average angle of repose was 20.31°, with an error of 0.78% compared to the physical test. For the flexible model, under the optimal bonding parameter combination, the simulated maximum bending stress was 2.64 N, with an error of 0.76% compared to the three-point bending test, verifying the accuracy of the models. The study indicated that the established models and parameters can accurately reflect the physicomechanical properties of barley seedling stems, providing a theoretical basis for the design and optimization of their harvesting, conveying, and processing machinery.

To address problems such as low working efficiency and the difficulty of manual seed filling during seed breeding with a soybean plot seeder, a new type of automatic seed filling device with a rotational disc was designed. The design integrates a rotational seed disc coupled with seed cups. The automatic seed filling device consists of a mechanical part and an electrical control part. The mechanical part includes seed cups, a seed plate, a base, and a mounting bracket, while the electrical control part includes a PLC, motor, and driver. By analysing the soybean seed delivery process, the factors influencing the design of the seed cup structure were identified, and key structural design and parameter analysis of the seed cup were conducted. Field test results showed that the minimum qualification rate of seed cup offset was 98.78%, and the minimum qualification rate of seed filling without seed breakage was 96.18%. These results demonstrate good accuracy and reliability, meeting the agronomic requirements of field seeding with plot seeders, and the experimental performance reached the design expectations.

To accurately detect densely distributed kiwifruit flowers in complex orchard environments, this study proposes an improved detection model, YOLOv11-TYW, based on the YOLOv11n architecture. First, the RepViTBlock is integrated to enhance the model’s feature representation capabilities. Second, the ADown module is introduced to improve the downsampling structure, thereby increasing detection accuracy for small flowers and branches while enhancing inference efficiency. Third, a triplet attention module is embedded in the head network to improve detection performance under conditions of occlusion caused by branches and overlapping flowers. Experimental results show that the YOLOv11-TYW model achieves a precision of 88.4%, a recall of 89.1%, and a mean average precision (mAP) of 91.2%, representing improvements of 4.3, 4.4, and 6.7 percentage points, respectively, over the baseline YOLOv11n model. When tested on kiwifruit flower images captured in various orchard environments, YOLOv11-TYW produces more accurate bounding boxes, with fewer false positives and missed detections. Applying the improved model to the actual kiwi orchard environment, demonstrate that YOLOv11-TYW exhibits excellent detection performance in real-world orchard settings and offers technical support for automated kiwifruit flower pollination.

Soil compaction caused by agricultural machinery operations is becoming increasingly serious, damaging soil structure and affecting sustainable agricultural production. However, there has been a lack of systematic research on the quantitative effects of straw on soil compaction. Therefore, this study used the discrete element method to investigate the effects of different straw contents (0–50%) on soil compaction characteristics. The results showed that: (1) When the loading plate sinks at a constant speed of 0.01 m/s, the compressive force on the soil mixture at depths of 50 mm, 100 mm, and 150 mm with 50% straw content is reduced by 669.03 N, 639.79 N, and 382.04 N, respectively, compared to pure soil; (2) When the loading plate was applied with a constant load of 100 N, the compressive forces at the same depths were reduced by 1,289.36 N, 862.9 N, and 607.49 N, respectively. Furthermore, both simulation results and indoor compaction experiments indicate that settlement decreases with increasing straw content. Pore ratio analysis confirms that the “micro-spring” effect of straw can improve soil compressive strength. This study provides guidance for enhancing agricultural machinery mobility and mitigating soil compaction hazards.

Accurate measurement of plant phenotypic data can provide a comprehensive understanding of plant physiology and help to study the relationship between plant genes and the environment. The application of visible light and other multi-source and multi-dimensional imaging sensing technology can provide a rich data source for plant phenotype identification and monitoring. With the continuous development and application of computer technology in the field of plant phenotype analysis, deep learning techniques have achieved significant progress in plant phenotype recognition and monitoring. On the basis of reviewing the relevant research results at home and abroad at this stage, this paper firstly describes the common ways of plant phenotype image acquisition. Then, it discusses in detail the current status of the application of deep learning technology in the fields of classification, detection and segmentation of plant phenotypes, crop development and yield prediction, as well as plant drought and pest stress, etc. Finally, it discusses the challenges and future development goals of the deep learning method in the monitoring and recognition of plant phenotypes. This paper aims to provide theoretical support and technical reference for the development and application of deep learning technology in the field of agricultural plant phenotyping.

As the locomotion system of agricultural machinery, the chassis significantly influences operational efficiency and maneuverability. This study focuses on two key aspects: field-oriented agricultural machinery chassis components and their intelligent development. The study examines the historical evolution of agricultural machinery chassis. A detailed analysis is provided on the operational mechanisms, technical features, and global application status of chassis drive systems, steering mechanisms, and leveling control technologies. Building upon this analysis, the study further explores future trends in field-oriented agricultural machinery chassis development. Field agricultural machinery chassis will develop towards intelligent technology, modular design, new energy and autonomous driving technology applications. Incorporating advanced sensor networks, machine learning algorithms, and artificial intelligence, the chassis system will implement tightly coupled multi-sensor fusion and control strategies to enable synergistic optimization of powertrain, steering, and leveling mechanisms, with integrated prognostic and health management capabilities.

Measuring melon fruit diameter offers key insights into growth status and maturity. To overcome the limitations of manual measurement—namely high labor demands, time consumption, and large errors—this study introduces a method based on an improved Mask R-CNN algorithm. The model uses ResNet50 as the backbone and incorporates a Channel Prior Convolutional Attention (CPCA) mechanism and a bidirectional feature fusion pyramid network to enhance multi-scale feature extraction. A Self-Attention (SE) mechanism is added to the mask branch to improve segmentation accuracy. Measurement points are determined through contour segmentation, curvature analysis, and bounding rectangle fitting. A binocular camera provides depth information, and Euclidean distance is used to compute actual size. The improved algorithm achieves detection and segmentation precision of 94.2% and 92.7%, with recall rates of 94.5% and 93.6%. The method yields average relative errors of 7.1% (horizontal) and 7.6% (vertical), meeting practical agricultural needs and supporting maturity assessment.

Medicinal plants represent an important resource for diversifying agricultural production and creating new economic opportunities in rural areas. The extraction of active principles from these plants is becoming increasingly significant, as the demand for natural, ecological, and high-quality products continues to rise on both domestic and international markets. In this context, the present paper is a review study that analyses the role and potential of extraction technologies for bioactive compounds from medicinal plants, highlighting their applicability on farms and their contribution to the development of sustainable agriculture. Beyond their economic importance, modern extraction technologies can contribute to sustainable development through several mechanisms: economically, by enabling the production of value-added products and reducing dependence on conventional crops; ecologically, by employing efficient and environmentally friendly procedures that minimize resource consumption and waste; and socially, by generating new employment opportunities and supporting farmers’ access to local markets.

Traditional furrow openers used for corn planting in saline-alkali soils often face challenges such as high resistance, excessive soil disturbance, and unstable furrow depth, all of which negatively affect planting quality. This study presents an optimized design of a double-disk furrow opener for precision corn planting under saline-alkali conditions. The structure and working principle of the opener were described, and its force mechanism analyzed. Key structural parameters, including disk diameter, disk angle, and convergence angle of the disk points, were identified, with furrow resistance and furrow depth stability selected as evaluation indicators. Using a Box-Behnken design combined with EDEM simulation, the soil-machine interaction model was established, followed by variance analysis. The optimal parameters were determined as a disk diameter of 344 mm, disk angle of 12.9°, and convergence angle of 58.4°. Under these conditions, the operating resistance of the furrow opener was 370.234 N, and the furrow depth stability coefficient reached 93.87%. The results provide a theoretical basis for reducing furrow resistance and improving planting stability and efficiency in saline-alkali soils.

Driver drowsiness severely impairs the normal operation of harvesters, leading to casualties and economic losses. Effectively detecting driver drowsiness in harvesters remains a significant challenge. This paper introduces a lightweight convolutional neural network that identifies driver drowsiness in harvester operators by analyzing the drivers eyes, mouth, and head posture. The model comprises a lightweight CNN, a Long Short-Term Memory (LSTM) network, and an attention layer, achieving high efficiency and low latency. Experimental results demonstrate that the CNN-LSTM-Attention model effectively balances accuracy and computational efficiency, enabling rapid and precise drowsiness detection. This approach significantly improves safety during combine harvester operation.

Accurate and real-time measurement of tractor drive wheel slip ratio under plowing conditions is essential for improving overall machine performance and tillage quality. To address the limitations of existing methods—namely low measurement accuracy, poor anti-interference capability, and low efficiency—this study proposes an online slip ratio measurement method based on multi-sensor fusion and adaptive filtering. A real-time measurement system was developed by integrating GNSS, IMU, and wheel encoders. Furthermore, a lens-based quasi-oppositional learning strategy and a good-point-set initialization mechanism were introduced to enhance the mayfly algorithm, which was then used to optimize a parallel Kalman filter, forming the improved mayfly algorithm–parallel Kalman filter (IMA-PKF). This approach enables adaptive real-time adjustment to random noise disturbances encountered during plowing operations, thereby enhancing robustness. Simulation results show that under non-interference conditions, the IMA-PKF algorithm achieves a root mean squared error (RMSE) of 0.0214, representing a 74.8% reduction compared with the conventional KF algorithm. In addition, compared with PSO-PKF and MA-PKF, the RMSE accuracy is improved by approximately 62.23% and 49.41%, respectively. When disturbance points are introduced, IMA-PKF still maintains the lowest estimation error, with an RMSE of 0.0359, demonstrating excellent stability and anti-interference capability. Field experiments under different plowing depths further validate the robustness of the method: the maximum slip ratio measurement error is only 1.94%, with bias controlled within 2%. Compared with KF, the proposed method reduces mean absolute error (MAE) and RMSE by up to 36.29% and 37.06%, respectively. Overall, the IMA-PKF algorithm enables accurate and stable online measurement of tractor drive wheel slip ratio under diverse plowing conditions, providing a solid theoretical and technical foundation for improving tractor performance and operational efficiency.

The cleaning sieve box is a key component to achieve the cleaning of a combine harvester, and its service life directly affects the reliability of the entire machine. Aiming at the high quality of the cleaning sieve box and the cyclic loading during operation (which can easily cause fatigue damage and affect its service life), a fatigue durability analysis method for the cleaning sieve box based on a test bench is proposed. First, the deformation and stress distribution of the sieve box are analyzed through modal and dynamic simulation to identify the hotspots of fatigue damage in the sieve box. On this basis, a sieve box test bench was designed and built to collect load signals of the sieve box. The layout of sensor measuring points was optimized based on simulation results and the force characteristics of the sieve box was analyzed through signal processing. Then based on the load signals from multiple measuring points, a fatigue load spectrum was developed using nCode software, and the fatigue life of the sieve box was predicted using Miners fatigue damage theory. The results indicated that there are multiple stress concentration areas in the connection area between the shaking plate, fish scale screen and the side wall of the screen frame of the sieve box, which are the fatigue damage risk areas. The stress value in the connection area of the tail screen is the highest. Overall, the fatigue life of the front half of the sieve box is generally higher than that of the rear half. The connection area between the side walls of the screen frame and the tail screen is the weak fatigue durability area, with fatigue lives of 5.829 × 106 and 5.591 × 106 cycles, respectively. This study provides a certain basis for the design and optimization of the cleaning sieve box structure.

This paper uses panel data from Sichuan Province and Shandong Province from 2012 to 2022 to build an evaluation index system for the development of smart agriculture. It systematically assesses the impact and internal mechanism of smart agriculture on regional agricultural economic growth. This assessment is based on the Cobb-Douglas production function and panel regression methods. The regression results show that for each unit increase in the level of smart agriculture development, the elasticity contribution to agricultural economic output is 0.42, which is significantly higher than that of capital (0.28) and labor (0.17). Provincial regressions reveal significant differences, with Sichuan showing an elasticity coefficient of 0.45 and Shandong 0.37. Smart agriculture plays a significant and positive role in promoting agricultural economic growth. Its effect surpasses that of traditional input factors. Moreover, the effect of smart agriculture varies across regions. The analysis of mediation effects further shows the role of technological progress. It serves as a key pathway through which smart agriculture influences agricultural economic growth. Robustness checks and extended analyses confirm the reliability of these findings. Finally, the paper puts forward policy recommendations focusing on strengthening technological innovation, improving infrastructure, and cultivating talent.

To address the difficulty in evaluating the performance of the seeding unit profiling mechanism, a pitching testing platform for examining seeding units with profiling mechanisms was designed. The main system and key component parameters of the testing platform were determined, and a hydraulic system was developed to overcome the challenge of simulating field undulation curves. The feasibility of the hydraulic and transmission systems was verified using RecurDyn dynamic simulation technology. The simulation results showed that the hydraulic system of the testing platform could effectively reproduce wave curves at different speeds, with a maximum roller stress of 46.61 MPa and a maximum strain of -73.99 × 10-6. To further evaluate the actual performance of the testing platform, the seeding unit of the Debont 1205 high-speed no-till corn planter was used as the test object. Using the average adjustment time and average adjustment accuracy as evaluation indicators, parameters were collected at operating speeds of 2.22 m/s, 2.78 m/s, and 3.33 m/s, and compared with those obtained from the testing platform. The test results revealed that the maximum error in the detection of average adjustment time was 0.27 s, and the maximum error in the detection of average adjustment accuracy was 2.58 × 10?³ m. These findings indicate that the testing platform can effectively evaluate the regulation performance of the profiling mechanism at different operating speeds and provide an accurate indoor platform for testing seeding units.

Teff (Eragrostis tef), a staple crop in Ethiopia and an emerging global superfood, is increasingly being cultivated through transplanting to improve yield and resource efficiency. The development of efficient teff transplanting machinery requires a thorough understanding of the physical and mechanical properties of teff seedlings. This study investigated the physical and mechanical characteristics of four teff varieties (Quncho, Dukem, Tseday, and the local variety Gemechis) at 20, 25, and 30 days after sowing. The analyzed parameters included seedling weight, height, stem diameter, moisture content, tensile strength, shear stress, and coefficient of friction. Descriptive statistical analyses were conducted using Statistix 10 and MATLAB 2024b, and results were expressed as mean values for each parameter. The findings revealed that as the seedlings matured, their weight, height, stem diameter, tensile strength, and shear stress increased, whereas moisture content decreased. The measured tensile strength values were as follows: Quncho (12.17±0.053 N, 14.46±0.964 N, and 18.297±0.577 N), Dukem (12.36±0.058 N, 14.597±1.11 N, and 18.763±0.57 N), Tseday (12.66±0.057 N, 14.097±0.81 N, and 17.9±1.155 N), and Local (Gemechis) (12.18±0.057 N, 14.32±0.764 N, and 19.12±0.012 N) for 20-, 25-, and 30-day-old seedlings, respectively. All tensile strength values exceeded the forces applied by the transplanter, indicating that seedlings of these four teff varieties possess adequate strength and resilience, making them suitable for the design and development of mechanical teff transplanters.

This study addressed the issue of high grain breakage rates during mechanical maize harvesting, which significantly compromise grain quality and subsequent storage performance. A comparison was conducted between maize subjected to natural dehydration (CK) and maize treated with a desiccant (SY). The research analysed the moisture content of various plant organs, grain starch composition, and puncture resistance characteristics across three kernel regions during maturation. The results revealed a strong positive correlation between the grain dehydration rate and moisture loss in both the stalk and cob. Desiccant application accelerated grain dehydration and promoted the conversion of branched-chain starch. In both treatments, decreasing kernel moisture content led to the development of a thicker and denser internal cuticle layer, which increased the yield load and elastic modulus in the lateral and apical kernel regions. When kernel moisture content dropped below 25%, the SY group exhibited significantly higher yield load and elastic modulus than the CK group. These findings provide a theoretical foundation for improving kernel impact resistance and reducing grain breakage during mechanical harvesting.

Considering the current suboptimal calibration of critical contact parameters in the discrete element model (DEM) for rice straw, which consequently limits the applicability of DEM in the design of rice straw processing machinery, this study developed a DEM of rice straw using EDEM software to accurately calibrate the contact parameters. The physical dimensions and the repose angle of rice straw, as well as the friction coefficients between rice straw-rice straw and rice straw-steel plate were determined through physical experiments. Image processing combined with the least squares method was employed to obtain the angle of repose of rice straw. The Plackett-Burman experimental design was selected to screen the contact parameters that significantly influence the repose angle. The results indicated that the coefficient of restitution between rice straw particles, the static friction coefficient between rice straw particles, and the rolling friction coefficient between rice straw particles have a significant effect on the angle of repose of the granular pile. A Box-Behnken design (BBD) was used to establish a second-order response surface model between the contact parameters and the angle of repose. The optimal combination of contact parameters was obtained by performing target optimization using response surface methodology (RSM). A validation experiment for the repose angle was conducted, and the results indicated a relative error of 2.13% between the simulated angle of repose and the physical angle of repose. The result demonstrates that the calibrated parameters can be used for simulating the contact behavior of rice straw and can provide theoretical and data support for the discrete element simulation of the rice straw processing process.

To ensure food security and reduce harvest losses, improving the monitoring accuracy of grain combine harvester operation loss is of great importance. This paper systematically analyzes the technical progress of piezoelectric sensing applications in this field. In terms of materials, piezoelectric thin films (PVDF) exhibit faster response speeds (signal attenuation shortened by 30%), but are prone to short circuits in high-humidity environments. Piezoelectric ceramics (PZT), when combined with a double-layer vibration isolation structure, can effectively reduce vibration interference errors to below 5%, providing better stability. Regarding sensor structure, the array layout enhances multi-target recognition, while the innovative double-layer cross structure enables analytical positioning of the spatial distribution of grain collisions, offering a new approach for accurately calculating loss rates. In signal processing algorithms, support vector machines (SVM) and decision trees perform well with small sample sizes; however, combining them with discrete element simulation (EDEM) is necessary to optimize feature extraction. Among these methods, the WOA-BP algorithm can control monitoring error within 6.23% through adaptive parameter adjustment. Nevertheless, current technologies still face challenges such as insufficient adaptability to varying material environments and limited algorithm generalization under complex working conditions. In the future, multidisciplinary collaborative innovation is required to develop hybrid algorithm models that integrate weather-resistant composite materials, intelligent adaptive sensor structures, and physical mechanisms, thereby establishing a high-precision, low-cost monitoring system and providing theoretical support for the research and development of grain loss detection equipment.

This study utilized ABAQUS finite element software to model a flat-elliptical pipe greenhouse, focusing on the contact interactions among the greenhouse film, structural frame, and film-tensioning lines. A nonlinear finite element method was employed to investigate variations in contact pressure and shear stress under different prestress conditions of the greenhouse film and film-tensioning lines. The results showed that increasing the prestress of the greenhouse film increased contact pressure but decreased shear stress, whereas increasing the prestress of the film-tensioning lines raised both contact pressure and shear stress. Optimizing the combined prestress of the greenhouse film and film-tensioning lines can enhance the mechanical performance and wind resistance of the greenhouse structure.

Liquid fertilizers have gained widespread use owing to their higher nutrient use efficiency, improved nutrient uptake, and reduced environmental impact. However, the agronomic effects of different application methods and rates of liquid fertilizer on maize growth and yield remain insufficiently investigated. This study conducted a two-year field experiment to evaluate the effects of four fertilizer application methods: band application of granular fertilizer (BAGF), mixed hole application of liquid fertilizer (MHALF), side hole application of liquid fertilizer (SHALF), and a control treatment without starter fertilizer (CK). Four liquid fertilizer rates (45, 105, 150, and 300 kg/ha) were tested. The results indicated that the application method plays a critical role in determining maize emergence and early growth. In particular, SHALF at moderate rates (45 and 105 kg/ha) significantly improved emergence rate, dry matter accumulation, and plant height compared with MHALF and BAGF. In contrast, high fertilizer rates (150 and 300 kg/ha) in MHALF treatments negatively affected emergence rate. Fertilizer treatments also had a significant effect on maize yield and its components. The SHALF treatment at a rate of 150 kg/ha resulted in an average yield increase of 4.9% compared with CK. These findings suggest that a moderate rate of SHALF is a practical and effective strategy for improving maize productivity in cold temperate regions

To address low qualified planting rate caused by inaccurate longitudinal seedling feeding in rapeseed blanket transplanter, a longitudinal auxiliary feeding device was designed. The working principle of the seedling feeding device is elucidated, and mathematical model of mechanics is established. The parameter range of the key components of the longitudinal auxiliary seedling feeding device was determined. A multi-factor test was implemented, evaluating spring tooth thickness (0.2-0.4 mm), height (65-75 mm), and quantity (6-8 units) as independent variables, and the longitudinal compression amount, uniformity coefficient of longitudinal seedling delivery, and qualified cutting rate as evaluation indexes. The test parameters were optimized using the comprehensive scoring method. The optimization results of the regression model derived from the quadratic orthogonal combination bench revealed test that when the thickness of the spring teeth was 0.29 mm, the height was 74.3 mm, and the number was 10, the longitudinal compression was 11.86 mm, the uniformity coefficient of longitudinal seedling delivery was 0.97, and the qualified cutting rate was 91.13%. It was verified that the longitudinal compression was 12.6 mm, the uniformity coefficient of longitudinal seedling delivery was 1.08, and the qualified rate of cutting was 93.6 %. The optimization results of the regression model verified by field experiments showed that the qualified planting rate was 92.35 %, the precision of seedling delivery was improved, and the feasibility of auxiliary seedling delivery device was verified.

To achieve precise fertilisation and improve fertiliser utilisation, an eccentric intermittent spot fertilisation device was designed in this study. The device can concentrate the continuous fertiliser flow from the fertiliser discharger into intermittently discharged fertiliser clusters, and accelerate the discharge of fertiliser into the fertiliser furrow through a Geneva mechanism. Full factorial experiments based on DEM-MBD coupled simulation show that both travel speed of implement and fertiliser discharge mass had a significant effect on the degree of dispersion and the coefficient of variation (CV) of fertiliser discharge. When the travel speed was 2.5 m/s and the fertiliser discharge mass was 6 g, the device had the best performance (degree of dispersion was 5.57 and the CV of fertiliser discharge was 3.27%). The results of the soil bin verification experiment indicate that the fertiliser pile length was 81.9 mm and the CV of fertiliser discharge was 4.30% under the optimum combination of working parameters. The absolute error between this result and the simulation result was 0.04 and 1.03%. This study shows that the device has better performance under the optimal working parameters.

This study presents a digital simulation of a stone-picking machine using the Discrete Element Method (DEM) to analyze its interaction with heterogeneous soil and scattered stones. A detailed 3D model of the machine was developed in SolidWorks and imported into EDEM software to replicate the mechanical operation and material flow during field use. The simulation aims to evaluate the machines performance in terms of stone collection efficiency, component interaction with granular material, and potential clogging scenarios. By accurately modeling the geometry and motion of key parts, such as the collection system and conveyor belt, the study provides insight into operational dynamics that are difficult to capture through physical prototypes alone. This workflow demonstrates how integrating CAD-based design with DEM simulation can contribute to optimizing agricultural machinery for improved field reliability and performance. Additionally, the stone-picking machine can be effectively used in field preparation prior to soil work for establishing vineyards and orchards, helping to create a clean and suitable seedbed that supports sustainable crop development.

To achieve precise fertilisation and improve fertiliser utilisation, an eccentric intermittent spot fertilisation device was designed in this study. The device can concentrate the continuous fertiliser flow from the fertiliser discharger into intermittently discharged fertiliser clusters, and accelerate the discharge of fertiliser into the fertiliser furrow through a Geneva mechanism. Full factorial experiments based on DEM-MBD coupled simulation show that both travel speed of implement and fertiliser discharge mass had a significant effect on the degree of dispersion and the coefficient of variation (CV) of fertiliser discharge. When the travel speed was 2.5 m/s and the fertiliser discharge mass was 6 g, the device had the best performance (degree of dispersion was 5.57 and the CV of fertiliser discharge was 3.27%). The results of the soil bin verification experiment indicate that the fertiliser pile length was 81.9 mm and the CV of fertiliser discharge was 4.30% under the optimum combination of working parameters. The absolute error between this result and the simulation result was 0.04 and 1.03%. This study shows that the device has better performance under the optimal working parameters.

To address the challenges of millet threshing mixtures, namely their light mass, small volume, minimal differences in component suspension speeds, and susceptibility to interference from impurities during sorting, which lead to high loss and impurity rates, this study applies the Brazil Nut Effect (BNE) to optimize the operating parameters of an air-sieve-type grain sorting device. The discrete element method (DEM) was used to numerically simulate the sieving and segregation processes of the millet threshing mixture, to clarify the grain population structure most conducive to effective sieving, and to identify the main factors influencing segregation and stratification as well as the appropriate parameter ranges. A bench-scale multifactor performance test was conducted to establish regression equations describing the effects of amplitude, stepped jitter plate length, crank rotational speed, and fan shaft rotational speed on loss rate and impurity rate. Using a multi-objective optimization method, the optimal parameter combination was determined as follows: amplitude of 26 mm, stepped jitter plate length of 450 mm, crank rotational speed of 580 rpm, and fan shaft rotational speed of 645 rpm. Under these conditions, the loss rate was 2.68% (with coiling loss below 0.5%) and the impurity rate was 3.95%. The results of this study provide a reference for optimizing the operating parameters of millet cleaning devices.

To investigate the influence of airflow distribution stability above the screen surface on the cleaning performance of an air-sieve millet cleaning device, this study employed the lattice Boltzmann method (LBM) to construct simulation models of screen surface flow fields with different screen types. The effects of airflow angle and airflow velocity on the distribution characteristics of airflow near and above the screen apertures were analyzed. The results showed that the flat square-hole screen exhibited high flow-field stability under various airflow conditions, whereas the perforated and fisheye screens were more susceptible to turbulent interference and had poorer uniformity. A simulation validation experiment was carried out using a self-developed airflow velocity and volume monitoring system, and the simulated and measured results showed high consistency in both variation trends and magnitudes, confirming the model’s accuracy. Further bench-scale comparison tests indicated that the flat square-hole screen achieved the best cleaning performance, particularly when using the screen aperture combination of 10 mm (upper screen) and 8 mm (lower screen), resulting in the lowest loss rate and impurity rate. The findings of this study provide a theoretical basis and experimental reference for optimizing screen surface structures and improving the cleaning quality of millet cleaning devices.

Sanbai Melon, a nationally recognized geographical indication product and a specialty agricultural product of Shanxi Province, is named for its distinctive white skin, white flesh, and white seeds. Primarily cultivated in Wanrong County, Yuncheng City, Shanxi Province, it is not only rich in various vitamins and amino acids but also boasts significant medicinal properties. Known for its ability to clear heat, detoxify the body, purify the lungs, moisten the intestines, reduce internal heat, and protect the liver, Sanbai Melon is highly valued for both its nutritional and health benefits. However, Sanbai melon is only available on the market from July to December each year. Due to this extended seasonal gap, fresh supply falls short of meeting daily consumer demand. To address this issue, processing Sanbai melon into freeze-dried powder has become essential. This study investigated the optimal freeze-drying parameters for Sanbai melon by examining the correlation between its dielectric properties and moisture content. Using a combination of single-factor tests and response surface methodology, a three-factor, three-level experimental design was implemented. Based on preliminary experiments, the vacuum levels were set at 35~40 Pa, 40~45 Pa, and 45~50 Pa; the heating plate temperatures were set at 60°C, 70°C, and 80°C; and the material thicknesses were set at 3 mm, 6 mm, and 9 mm. With drying time as the target variable, and moisture content, dielectric constant, and dielectric loss factor as response variables, response surface analysis determined the optimal freeze-drying conditions as follows: vacuum degree of 40~45 Pa, heating plate temperature of 73 °C, material thickness of 7 mm, and a total freeze-drying time of 6 hours. Under these conditions, the resulting Sanbai melon powder exhibited a moisture content of 2.9%, a dielectric constant of 2.195, and a dielectric loss factor of 1.525.

In order to improve the accuracy of discrete element simulation of stubble cleaning soil-engaging parts of corn planter in wheat stubble field, taking the soil straw of Huang-Huai-Hai as the research object, the method of combining physical repose angle with EDEM simulation test was adopted, and the Hertz-Mindlin with bonding contact model was selected to calibrate the simulation contact parameters. Plackett-Burman was used to screen out the main factors that had a significant impact on the test indicators. Design-Expert was used to conduct a central combination test on the screening factors, and regression analysis and significance test were performed on the simulation results to find out the optimal combination of test indicators. The factor screening test showed that the primary and secondary factors affecting the soil repose angle were soil rolling friction factor, soil-device static friction factor, soil static friction factor and soil normal stiffness per unit area. The primary and secondary order of the factors affecting the straw repose angle was the device-straw rolling friction coefficient, the device-straw restitution coefficient, and the straw static friction coefficient. The significant test showed that the soil rolling friction coefficient was 0.574, the soil static friction coefficient was 0.93, the soil-device static friction coefficient was 0.373, the soil normal stiffness per unit area was 9.5×109, and the relative error between the optimized parameter simulation test and the actual test was 3.1%. The straw static friction coefficient was 0.598, the device-straw restitution coefficient was 0.754, the device-straw rolling friction coefficient was 0.11, and the relative error between the optimized parameter simulation test and the actual test was 1.45%.

To address the issues of high impurity content and seed loss in soybean cleaning, this study employs a CFD–DEM coupled simulation using SN52 soybeans and a single-factor experimental design to analyze the air winnowing process under different inclination angles, inlet air velocities, and inlet opening sizes. The results indicate that, at an inlet air velocity of 17 m/s and an inlet opening size of 220 mm, the impurity rate decreases with increasing inclination angle, while the loss rate first decreases and then increases. When the inclination angle is 21° and the inlet opening size is 220 mm, the impurity rate decreases with increasing inlet air velocity, with the loss rate again showing an initial decrease followed by an increase. Similarly, at a 21° inclination angle and an inlet air velocity of 17 m/s, the impurity rate decreases as the inlet opening size increases, while the loss rate exhibited the same initial decrease and subsequent increase. The optimal parameter combination is determined to be 17 m/s inlet air velocity, 21° inclination angle, and 220 mm inlet opening size, achieving an impurity rate of 1.59%, a loss rate of 0.26%, and a cleaning efficiency of 98.15%. These findings provide a theoretical basis for the optimized design of airflow-based soybean cleaning equipment.

The curing of tobacco is a critical process that determines the quality of the final product. Accurate recognition of tobacco curing states is essential for ensuring optimal quality. Existing recognition models mostly focus on the transient states within the curing barn. In contrast, this study incorporates multiple time steps to capture dynamic feature changes in the curing barn over time, providing a more accurate state recognition. A hybrid deep learning model combining Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) networks, and a novel Density-aware Channel Redistribution Unit (DCRU) based on Kernel Density Estimation is proposed. The model integrates the global feature extraction capability of TCN, the long-term dependency modeling strength of LSTM, and the complex channel feature extraction ability of DCRU, thereby enhancing the models performance in recognizing the stages of tobacco leaf curing. Tests conducted on a real-world tobacco dataset demonstrate that the model achieves a prediction accuracy of 0.989 and outperforms baseline models as well as existing tobacco curing state recognition methods. These results validate the effectiveness of the hybrid TCN-LSTM model in recognizing tobacco leaf curing states, with promising applications in agricultural automation.

To achieve mobile deployment of corn leaf disease detection, this study proposes a lightweight method, YOLOv8n-LSCSBD. The Lightweight Shared Convolutional Separable Batch normalization Detection (LSCSBD) is used to achieve cross-scale feature sharing convolution and independent normalization, thereby reducing computational complexity and preserving detection accuracy. Comparisons of YOLOv8 training strategies show that using YOLOv8n as the initial model, with a learning rate of 1e-2 and an optimizer of SGD, yields the best performance. Comparisons of different detection head schemes show that YOLOv8n-LSCSBD reduces the model size by 20.6% (to 5.0MB) compared to the original YOLOv8n model. When compared to YOLOv10n and YOLOv11n, the model size decreased by 13.8% and 9.1%, respectively. Notably, YOLOv8n-LSCSBD achieves P of 97.6%, R of 95.4%, mAP@0.5 of 97.7%, and mAP@0.5:0.95 of 87.3%. This method provides an efficient lightweight solution for mobile device deployment.

The objective of this research is to develop an information system for recording and storing data in a digital format. The system is designed to simplify documentation processes and enhance preparedness for applications related to geographical indication registration. It comprises three core functions: data entry, storage, and display. The findings of this study indicate that if local farmers receive systematic support, particularly in areas such as technology adoption and structured data integration, their agricultural competitiveness can be significantly improved. Such progress would enable them to meet internationally recognized standards.

To meet the requirements of tobacco topping and ensure the efficient removal of topped buds, leaves, and shoots from the field. A lightweight clamping conveyor device was designed through an analysis of the forces acting on critical components and their operational principles. A rigid-flexible coupling dynamic simulation of the tobacco top stalks clamping and conveying process was conducted using ADAMS. An orthogonal experiment with three factors (forward speed, clamping distance, and conveying speed) at three levels was performed, with clamping success rate and conveying success rate as evaluation indices. Field validation experiments showed that at a forward speed of 0.6 m/s, a clamping distance of 11 mm, and a conveying speed of 1.1 m/s, the average clamping success rate was 94.52%, and the average conveying success rate was 98.36%. The conveying process was stable, with a high collection rate of top stalk, demonstrating strong adaptability to agricultural practices and meeting the operational requirements of tobacco topping in the field.

PEF pretreatment enhances Scutellaria seed germination and growth, improving drought adaptability. Using PEG-6000 to simulate drought stress, germination potential (42.86%), germination rate (44.76%), and germination index (27.73%) were significantly improved under 12.5% PEG. PEF treatment also increased vigor index, root length, and dry weight, shortened germination time, enhanced SOD and POD activities, reduced MDA content, and elevated levels of soluble sugars, soluble proteins, proline, and a-amylase. Hormonal analysis revealed increased gibberellin and auxin contents accompanied by reduced ABA levels. Overall, PEF pretreatment effectively promoted seed germination and growth under drought stress conditions.

This study addresses the challenges of low-precision 3D visualization, unclear irrigation requirements, and inadequate smart decision-making in tea garden management. A twin data-driven 3D visualization and control system is proposed based on a six-dimensional digital twin framework, consisting of physical entities, virtual models, data connections, services, digital twin data, and decision mechanisms. First, real-time bidirectional data interaction is achieved through an OPC UA communication channel and multi-source sensor integration with a MySQL database. Second, parametric tea plant modeling in 3ds Max, combined with particle systems, dynamic shaders, ambient occlusion (AO), and level-of-detail (LOD) rendering, enables high-fidelity and dynamic 3D visualization. Finally, an AquaCrop-LSTM irrigation demand prediction model was developed by integrating the FAO Penman–Monteith method, the AquaCrop model, and a Long Short-Term Memory (LSTM) neural network. The complete system, deployed within Unity, forms a closed-loop architecture of perception, mapping, decision-making, and feedback. Experimental results show that the LOD strategy improves the frame rate by 121% while reducing vertex count by 93.7%. The AquaCrop-LSTM model achieves a mean absolute error (MAE) of 0.251 mm and an R² value of 0.927. Under a 30-user concurrent load test, the system maintained an error rate below 0.02%. These findings demonstrate that the proposed system provides reliable technical support for visual monitoring and efficient irrigation management in tea gardens.

Accurately counting the number of corn seedlings is the key to evaluating the growth status of corn. To address the problem of difficult detection and counting of corn seedlings in complex field environments, this study proposes an improved MEI-YOLOv11 model. By introducing MANet, EUCB module, and Inner-SIOU loss function, the ability to extract features and recognize small targets in complex environments is enhanced. The results showed that the mAP0.5, P, and R of the model reached 97.0%, 94.2%, and 95.7%, respectively, which were 2.8, 2.7, and 2.4 percentage points higher than YOLOv11, respectively. The parameter count and inference time only increased by 1.28 M and 0.4 ms, respectively, and the detection accuracy was better than other detection models. The accuracy of multi weather counting is above 90%, with the highest accuracy of 91.23% on sunny days (RMSE=4.5044, R ²=0.8508). This method can effectively identify corn seedlings in complex backgrounds, providing technical support for accurate detection and counting of corn seedlings in multiple weather conditions.

Aiming at the problem of uneven stubble height caused by large topographic relief and scattered plots of buckwheat fields in Hilly and mountainous areas, taking 4SZ-1.5 buckwheat windrower as the research object, an omni-directional header profiling strategy based on fuzzy control was proposed. By constructing the cooperative control system of the rotary lateral profiling mechanism (rotary hydraulic cylinder) and the lifting longitudinal profiling mechanism (lifting hydraulic cylinder), combined with the sliding plate angular displacement sensor, the real-time road elevation information is obtained. The random road excitation is generated by the filtered white noise time-domain model, and the Mamdani type fuzzy controller is established. The hydraulic cylinder expansion and contraction command is output with the left and right angle deviation as the input. Simulink simulation shows that the fuzzy controller can effectively perceive the real-time fluctuations of the terrain on both sides, realize the adaptive adjustment of the header to uneven ground, and has good stability and anti-interference. Field test verification: compared with the manual mode, the coefficient of variation (CV%) of stubble height in the automatic mode is 40%~60% lower than that in the manual mode, and the CV% remains = 11.76 when the vehicle speed rises to 2m/s, significantly improving the adaptability to complex terrain.

Plastic greenhouses, due to their lightweight structural characteristics, often exhibit limited resistance to strong winds. To improve their wind-resistant performance, this study applied a combined approach using finite element modeling and excitation testing. Dynamic response data of the greenhouse frame were collected through excitation experiments, and frequency-amplitude characteristics were obtained using fast Fourier transform (FFT). Modal analysis of the finite element model was then performed to verify the experimental results, and the structural damping ratio was subsequently calculated. The results show that the acceleration amplitudes at the excitation point in the x- and z-directions were 1.66 and 1.25 times higher than those measured at adjacent frame sections, respectively. The natural frequency of the greenhouse frame was determined to be 3.15 Hz, and the corresponding damping ratio was 0.018. These findings provide insight into the dynamic behavior of flat-elliptical pipe greenhouse structures and offer a methodological reference for future structural optimization and the development of relevant engineering design standards.

A lightweight vision-based model, GENet, is proposed to overcome the limitations of conventional missing-seed detection systems, which are highly sensitive to seed characteristics and constrained by slow response and complex configuration. Deployed on a small precision seeder featuring an oblique hook-shaped spoon-type metering device, GENet integrates Ghost Modules, C3Ghost structures, and an ECA attention mechanism. Experiments demonstrate an mAP50–95 of 85.2%, accuracy of 99.9%, and 185 FPS inference speed on the Jetson AGX Xavier platform, while reducing model parameters by over 40%. Validation on the JPS-12 test bench confirms its robustness, providing an efficient solution for intelligent precision seeding.

Paddy field tillage and soil preparation form the foundation of rice production. To address the low operational efficiency and insufficient leveling accuracy of traditional paddy field preparation equipment, this study developed an automatic leveling control system for paddy field land preparation machinery. The system integrates hydraulic technology with sensors and a microcontroller, using an M 4/3 solenoid directional control valve to achieve automatic leveling. Simulation results obtained using AMESim software indicate that the hydraulic cylinder piston can fully extend within 11 s, demonstrating suitability for the automatic leveling process. Prototype testing further verified that the system operates with stability, safety, and reliability. The implementation of this control system contributes to improving the degree of automation in tillage and land preparation machinery during rice cultivation and supports the advancement of intelligent and information-based agricultural equipment.

This paper presents the design of a linkage-based underactuated end-effector for sweet pepper harvesting. The end-effector employs a single motor to drive a spring differential system, enabling two three-phalanx fingers to sequentially and adaptively envelop the fruit in a proximal-middle-distal order, coupled with a pneumatic cutting mechanism for peduncle separation. Biomechanical tests on sweet peppers established a safe grasping force threshold range of 0.72 N to 33.56 N. The phalanx dimensions were optimized using a genetic algorithm, ensuring the workspace covers the fruit diameter range of 70–103 mm. Static analysis determined that the push rod inclination angle should be maintained between 5°–10° to avoid dead points. ADAMS simulations verified the effectiveness of the enveloping sequence, with a peak contact force of 15.89 N remaining within non-destructive range. Prototype tests demonstrated that at a rotational speed of 90 r/min, the harvesting success rate reached 89.34%, the single-fruit harvesting time was 21.70 s, and the damage rate was 0%, confirming the effectiveness and feasibility of the proposed mechanism for low-damage sweet pepper harvesting.

The integration of autonomous technologies in aquaculture has become essential for enhancing sustainability, biosecurity, and operational efficiency within increasingly intensive production systems. This paper presents the experimental validation of a functional prototype of an intelligent Remotely Operated Vehicle (ROV) developed for underwater environmental monitoring under controlled laboratory conditions that simulate aquaculture-specific scenarios. The proposed system integrates vectorial propulsion, an intelligent depth-hold controller, and a multisensor inertial navigation unit, enabling robust operation in GPS-denied environments and confined aquatic infrastructures. The hardware platform incorporates optical imaging alongside dissolved oxygen, pH, and temperature sensors, with data acquisition and command input managed via a mobile dashboard interface. A series of functional trials were conducted to assess depth-hold precision, trajectory-tracking accuracy, command latency, and imaging performance under dynamic test conditions. Based on these evaluations, the ROV exhibited a mean vertical deviation of ±0.10 m, a trajectory error of 0.16 m, and an average command latency of 290.7 ± 2.6 ms, demonstrating stable and repeatable behavior. These results validate the system’s potential as a non-invasive, semi-autonomous monitoring solution tailored to the requirements of precision aquaculture and scalable digital aquafarming frameworks.

Paddy field tillage and soil preparation form the foundation of rice production. To address the low operational efficiency and insufficient leveling accuracy of traditional paddy field preparation equipment, this study developed an automatic leveling control system for paddy field land preparation machinery. The system integrates hydraulic technology with sensors and a microcontroller, using an M 4/3 solenoid directional control valve to achieve automatic leveling. Simulation results obtained using AMESim software indicate that the hydraulic cylinder piston can fully extend within 11 s, demonstrating suitability for the automatic leveling process. Prototype testing further verified that the system operates with stability, safety, and reliability. The implementation of this control system contributes to improving the degree of automation in tillage and land preparation machinery during rice cultivation and supports the advancement of intelligent and information-based agricultural equipment.

To address the issues of high operating resistance and insufficient soil-loosening performance encountered by winged deep-loosening shovels in strip-tillage fertilization machines, a bionic optimization design for the shovel wing was carried out based on a medium-sized winged deep-loosening shovel. A mechanical contact model between the shovel wing and soil was established to analyze the forces acting on the wing and the soil above it, as well as the soil disturbance characteristics induced by the wing. Following the principles of bionics, the head morphology of the hammerhead shark was extracted and used to derive a characteristic geometric equation, which was then applied to the bionic redesign of the shovel wing. Using discrete element simulation technology, a deep-loosening shovel-soil interaction model was constructed. Comparative experiments on wings of different shapes showed that the bionic-optimized wing reduces operating resistance and increases soil disturbance area compared with the conventional wing. Simulation results indicated that the bionic wing achieved an average soil disturbance area of 1635.63 cm² and an average operating resistance of 1143.76 N. Finally, bench validation tests were conducted, demonstrating an average actual soil disturbance area of 1648.20 cm² and an average actual operating resistance of 1102.01 N, results which fall within the allowable error range. Therefore, the bionic shovel wing meets the operational requirements.

This article presents the development and experimental validation of an automated module for sorting, identifying, and temporarily storing soil samples, implemented on an Arduino development board. The system was conceived as an auxiliary unit that can be seamlessly integrated into existing soil sampling equipment, automating the post-extraction operations. By directing each sample automatically to a designated container, the module eliminates manual handling, which is labor-intensive and prone to error. Each container is assigned a unique identifier using the PN532 NFC/RFID module, while GPS coordinates, date, and time are simultaneously recorded. Tests performed on a functional prototype demonstrated reliable operation. The carousel mechanism demonstrated performance consistent with functional requirements, the inductive sensors achieved 100% detection accuracy, and the PN532 NFC/RFID module reached a 97% reading success rate. The system ensures full sample traceability, reduces contamination risk, and, by automating critical sorting and labeling steps, significantly enhances the overall efficiency of the analytical workflow.

The utilization rate of pesticides in China is low, which causes much pesticide waste and serious pollution of the ecological environment. The main reason of this problem is that the deposition rate of spray is not fairly high and the most fundamental factors affecting droplet deposition are the speed of droplet movement and the size of droplet particles. This study establishes a PWM intermittent spray system under variable pressure and flow control. Through CFD simulations, the atomization characteristics of the nozzle are analyzed. The results indicate that the system can generate finer droplet sizes and higher droplet velocities. By constructing a measurement platform for droplet experiments, tests are conducted on droplet sizes at different horizontal positions under four pressure levels: 0.3 MPa, 0.4 MPa, 0.5 MPa, and 0.6 MPa. The results indicate that as the spray pressure increases, the droplets exhibit a significant tendency to disperse toward both sides, and the droplet size decreases significantly. The field experiment is conducted with broad bean in flowering period. The results show that the average deposition rate increases by 9.9%, 26.4% and 22.9% when the spray pressure is 0.4 MPa, 0.5 MPa and 0.6 MPa compared with 0.3 MPa. This paper verifies the feasibility of the system in improving the spray pressure and quantitatively controlling the spray flow rate, and improving the deposition of droplets by obtaining finer particle size and higher spray velocity. In this sense, this paper has a certain reference significance for improving the effective utilization rate of pesticides and protecting the environment.

The recognition of Agaricus bisporus is a key step in the intelligent picking of Agaricus bisporus. Given the complex background and limited computing resources of edge devices in actual planting scenarios, an improved Mask-RCNN model for Agaricus bisporus recognition was proposed. In this method, the backbone feature extraction network of the baseline Mask-RCNN model was replaced with the lightweight MobileNetV3 network to reduce the model complexity. Meanwhile, the BiFPN network was used to replace the original FPN feature fusion network, thereby strengthening feature fusion and enhancing the models ability to learn image features and acquire contextual information. Experimental results showed that the improved Mask-RCNN models parameters and floating-point operations were 24.46 M and 173 G, respectively, which were 44.4% and 24.5% lower than those of the baseline Mask-RCNN model, and the frame rate increased by 3.55 FPS, indicating a better prospect for deployment on edge devices. This method can provide technical support for the development of the visual system of Agaricus bisporus picking robots.

With the advancement of intelligent farming technology, computer vision-based animal behavior recognition has become an important tool for improving production efficiency and animal welfare in modern farming management. To overcome the challenge of balancing computational efficiency and accuracy in existing behavior recognition systems, this study proposes an optimized model based on YOLOv8-EAPNet for accurately recognizing four main pig behaviors: standing, sitting, lateral lying, and prone lying. The framework adopts a multi-level lightweight design, incorporating three advanced technologies—C2f-ECA, SPPELAN, and Detect_AFPN—to enhance joint feature response, resolve spatial differences between sitting and lateral lying, and reconstruct semantics in occluded areas. This strengthens the models robustness in complex farming environments and significantly improves the accuracy of pig behavior recognition. Validated on farm data, the model achieved an average precision improvement of 1.5% on a self-built dataset, with specific accuracy increases of 0.9% for standing, 1.7% for sitting, 3.0% for prone lying, and 0.3% for lateral lying. This technology provides an automated tool for early warning of limb injuries and respiratory diseases in pigs, promoting the upgrade of intelligent health management in the livestock industry and supporting the modernization of large-scale pig farming.

To address the problems of slow path planning speed, high path cost, and visual positioning errors encountered by grain harvesters during field operations, this study proposes an improved rapidly-exploring random tree algorithm integrated with visual servoing (VS-IRRT). By employing visual servoing technology to acquire environmental information in real time, the algorithm enables accurate positioning and attitude correction of the harvester. On this basis, heuristic sampling strategies and a path optimization function are introduced to enhance node expansion efficiency and accelerate the convergence of the search tree. To further reduce path cost, a path evaluation model incorporating environmental feature costs is established, which comprehensively considers terrain complexity, crop distribution density, and the machine’s turning radius. This model dynamically adjusts the search direction and improves path smoothness. Simulation and field navigation experiment results indicate that the VS-IRRT algorithm reduces path planning time by approximately 32% compared to the traditional RRT algorithm, decreases the average yaw error by 42%, reduces the path curvature variation rate by 33%, and lowers turning frequency by 21%. The algorithm also maintains high robustness and planning accuracy under visual noise and positioning disturbances. Overall, this study provides an effective path planning approach and technical support for autonomous navigation and efficient operation of grain harvesters in complex agricultural environments.

To address the issues of large number of parameters and low deployment efficiency on mobile devices in the existing YOLOv8-DSFF model for corn pest detection, this study proposes an improved object detection model that integrates the CSPPC lightweight module and the Wise-IoUv3 loss function. The optimized model reduces the number of parameters by 85.6%, achieves an mAP@0.5 of 90.8%, reaches 204 FPS inference speed on PC and 42 FPS on mobile devices. This provides a practical low-power solution for real-time field monitoring of corn pests.

This study uses discrete element method (DEM) simulations to examine how soil type, moisture content, and working speed affect forces on a tree planting machine’s covering paddle, this method was applied to mimic real-world conditions and optimize working parameters. Maximum paddle forces increased from 263 N to 589 N across soil types and moisture contents, with higher water content and faster speeds increasing loads, especially in sandy soils. For example, in sandy soil, increasing moisture from 0% to 50% raised forces from 276 N to 392 N at 1 km/h and from 300 N to 589 N at 3 km/h. Clay soils showed generally lower forces (263–445 N). All measured forces remained within design limits, the objective was to establish a quantitative relation between soil moisture, working speed, and paddle reaction forces, highlighting their importance to ensure consistent seedling placement, minimize wear, and enhance equipment longevity.

The implementation of artificial intelligence (AI) techniques and tools in all agricultural sectors can ensure the transformation of agriculture into a smarter, more efficient and more sustainable sector, ready to face the challenges of the future. The paper provides a review of recent applications of AI, focused on crop monitoring, precision agriculture, robotics, animal management and supply chain optimization, with examples of research, studies and applications carried out in this regard in the last 5 years. The general conclusion is that, in the current conditions of the need to develop the agricultural sector on a sustainable basis and for economic efficiency, the use of emerging technologies (AI) and their implementation in all activities and processes related to agriculture must be accelerated.

Currently, research on cherry detection and recognition is relatively limited, and existing methods for agricultural product inspection often suffer from slow speed and low classification accuracy. To address these issues, this paper introduces an improved YOLOv11n-based model for detecting cherry ripeness, designed to enhance both the accuracy and efficiency of identifying cherries at different maturity stages. First, improvements were made to the backbone network of the YOLOv11n model by replacing the original backbone with ConvNeXtv2. This replacement achieved a broader global receptive field and enhanced multi-scale learning, which helped reduce computational costs and significantly improve efficiency while maintaining high performance. Second, a DCNv4 convolution module—an advanced convolutional layer with adaptive receptive fields—was added to the neck of the model. The neck is an intermediate stage that combines features from different layers, and the DCNv4 adapts the receptive field to help accurately locate occluded cherries of any shape and scale. This improves detection performance for small cherries without increasing computational complexity. Finally, the convolutional attention module CBAM was introduced. CBAM adaptively focuses on important image features while suppressing irrelevant background by using both channel and spatial attention mechanisms. Together, these additions significantly improve cherry detection accuracy and robustness. Our experimental results show that the improved M-YOLOv11n algorithm achieved a 4.84% increase in mAP@50 compared to the original YOLOv11n model. Precision and recall also improved by 1.25% and 0.4%, respectively. Overall, the enhanced model outperformed not only its base version but also the YOLOv5n and YOLOv8n models. Compared to multi-stage models, the proposed model demonstrates superior accuracy, speed, and reduced computational requirements. This improvement enables more efficient and precise identification of cherry ripeness, thereby enhancing the efficiency of cherry harvesting and facilitating optimal harvest timing. These advancements support the optimization of storage and transportation conditions for cherries and provide robust technical support for intelligent orchard management and the advancement of automated fruit sorting systems.

To address the problems of high kernel breakage rate and high unthreshed rate during direct maize kernel harvesting under conditions of high moisture content and dense planting - common in the single longitudinal axial-flow harvesters widely used in Southwest China - a tangential–transverse axial-flow double-drum maize threshing device was designed and optimized. The drum combination configuration was determined through theoretical analysis, and the influence of the vertical spacing between the two drums on threshing performance was investigated. The flexible concave screen and transition sieve plate were also redesigned. Using EDEM and Adams software, the threshing process was simulated and analysed to verify the rationality of the design. A prototype was fabricated, and drum speed, deflection angle, and feeding rate were selected as test factors. A response surface experiment was conducted with kernel breakage rate and unthreshed rate as evaluation indices. Multi-objective optimization of the regression model was performed using the response surface methodology (RSM). After rounding, the optimal parameter combination was determined as follows: drum speed of 453 r/min, deflection angle of 69°, and feeding rate of 6.7 kg/s. Verification tests yielded a kernel breakage rate of 2.76% and an unthreshed rate of 0.11%, meeting production requirements and providing a valuable reference for the design of maize threshing devices in Southwest China.

To address the challenges of difficult soil penetration and high digging resistance encountered by peanut harvesters in saline-alkali soils due to compaction, this study designed a drag-reducing digging shovel for peanuts in such environments, using the dung beetle head as a bionic prototype and incorporating agronomic requirements. Basic physical parameters of the saline-alkali soil were calibrated, and Bonding-model bond parameters were configured to establish a discrete element model of the soil-root system. The Hertz-Mindlin with JKR contact model was selected as the discrete element simulation model for the soil. A 3D scanner was employed to capture the morphology of the dung beetle, obtaining its precise three-dimensional model. The curve equation of the bionic digging shovel was determined, and its 3D model was constructed. Comparative simulation tests between the bionic and conventional digging shovels were conducted, during which particle flow velocities were tracked and their vector distributions analyzed to elucidate the drag reduction mechanism. Furthermore, by comparing the resistance forces acting on the conventional and bionic shovels at speeds of 0.4 m/s, 0.6 m/s, and 0.8 m/s, drag reduction rates of 4.82%, 3.03%, and 3.85%, respectively, were achieved for the bionic shovel. These results validate the accuracy of the mechanical model and the rationality of the bionic structural design.

Compared with manual harvesting of Lycium barbarum, existing large-scale harvesters have achieved a certain degree of efficiency improvement. Nevertheless, their intermittent operation mode remains a bottleneck restricting overall harvesting performance. To address this issue, a continuous vibration-based L. barbarum harvesting device was developed in this study. Plackett–Burman experiments indicated that vibration angle, vibration frequency, and the spacing between the upper and lower vibrating rods were the primary factors affecting the harvesting performance. Further parameter optimization experiments were carried out by considering the harvesting rate of ripe fruits, the mis-harvesting rate of unripe fruits, and the damage rate of ripe fruits. The optimal parameter combination was determined as a vibration angle of 46°, a vibration frequency of 9 Hz, and a spacing of 62 mm between the upper and lower vibrating rods. Based on these parameters, performance verification tests were conducted. The results showed that the harvesting rate of ripe fruits reached 85.40%, the mis-harvesting rate of unripe fruits was 4.61%, and the damage rate of ripe fruits was 3.19%. These findings provide technical and equipment support for the development of continuous mechanized harvesting of L. barbarum.

The accuracy of seed placement is critical for achieving uniform plant spacing in the field. During seeding operations, the final seed placement is influenced by multiple factors. Irregular bouncing and rolling caused by seeds colliding with the walls of the seed guide tube during discharge is a key factor leading to low seeding uniformity. To investigate the optimal structural parameters and operational parameter ranges for the seed movement constraint device designed in this study, a kinematic analysis of its operational process was conducted. This analysis evaluated the devices operational effectiveness and the uniformity of seed placement. A multi-factor experiment was designed using central composite design and seed placement/posture detection technology. The experimental factors included buffer plate length, horizontal installation position of the guide plate, and working speed of the seed distributor. The experimental indicators were pass rate and coefficient of variation. Experimental results were processed and optimized using Design Expert-13 software. The most ideal combination was determined to be: a buffer plate length of 87.55 mm, and a guide plate horizontal installation position of 22.57 mm. At these settings, seed placement uniformity and stability were optimal, with a seed placement qualification index of 93.431% and a coefficient of variation of 9.324%. This confirms the rationality of the designed seed movement restraint device.

To improve the accuracy of contact parameters in discrete element simulations of seed metering devices and avoid errors arising during parameter measurement, this paper does not determine the values of each parameter in advance during parameter calibration. Instead, it directly uses the sliding friction angle, angle of repose, and rebound height as indicators, and employs regression analysis to calibrate the static friction coefficient, rolling friction coefficient, and restitution coefficient. The accuracy of the simulation parameters is verified through comparative experiments with the seed metering device. The results show that: the static friction coefficient is proportional to the sliding friction angle, and the correlation between the two is independent of material properties. The calibrated static friction coefficients between seeds, PMMA, and PLA are 0.39, 0.43, and 0.54, respectively; the rolling friction coefficient of seeds is exponentially related to the angle of repose, and the rolling friction coefficient between seeds is 0.029; the restitution coefficient is quadratically related to the rebound height in drop tests, and the calibrated restitution coefficients of seeds with PMMA and PLA are 0.33 and 0.32, respectively; the verification experiments demonstrate that the motion patterns of seed populations in simulation and physical experiments are similar, and the differences in seed filling rates between the two are not significant, proving the reliability of the calibrated simulation parameters

The study investigates the influence of different substrates on seed germination and early plant development under controlled environmental conditions, using a specially designed intelligent plant growth simulation system. Experiments were conducted with lettuce and radish seeds sown in peat, garden compost, and podzol. The system allowed precise control of temperature, humidity, soil moisture, light, and carbon dioxide, enabling a reliable comparison of treatments. The initial physical and chemical properties of the substrates were characterized, showing the lowest moisture and density in peat, while compost and podzol displayed higher values and more balanced nutrient content. The results demonstrated that lettuce germinated successfully across all substrates, although peat delayed germination by 5–7 hours and true leaves appearance by up to 12 hours. In radish, the effect was more pronounced: compost and podzol ensured rapid germination (94–95 hours) with high success rates (~98%), whereas peat resulted in delays exceeding 100 hours and germination rates below 25%. Correlation analyses highlighted strong links between delayed germination and reduced performance, especially in peat. Overall, the study confirmed that substrate choice is critical for achieving uniform and efficient germination, with compost and podzol providing optimal conditions, and emphasized the practical value of simulation systems for improving crop establishment.

To address the requirements for automation and intelligence of agricultural robots, this paper develops a 14-degree-of-freedom dynamic model for wheeled agricultural robots. The model aims to provide a dynamic modeling foundation under the framework of modern control theory for the automation and intelligence of wheeled agricultural robots. It incorporates the Ackermann steering mechanism, MacPherson independent suspension system, tire model, and deformable soil model based on Bekkers formula. The vertical tire pressure is calculated using the deformable soil model via Bekkers formula, while tire forces are predicted by combining the tire slip angle and slip ratio with the Magic Formula Tire Model. By analyzing the force transmission effect of the suspension system, integrating the center-of-mass coupling effect analysis and the robot body model equations, the precise prediction of the attitude and motion trajectory of the wheeled agricultural robot is achieved. A co-simulation experiment using MATLAB and CarSim under the double lane change (DLC) condition is designed for validation. Experimental results demonstrate that the proposed model exhibits high consistency with the CarSim simulation results. The mean absolute errors (MAE) are 0.327° for steering wheel angle, 0.677°/s for yaw rate, 0.691° for body roll angle, and 0.944 m/s² for lateral acceleration. All errors are less than 1.5, meeting the requirements of dynamic simulation. This model can effectively predict the body attitude of wheeled agricultural robots and lay a foundation for the subsequent development of optimal control algorithms for agricultural robots.

In this study, a machine-vision-based automatic control system for the topsoil stripping of Fritillaria ussuriensis Maxim. (FUM) was designed to address the problems of manual adjustment, low control accuracy, and response lag in stripping-depth control during FUM harvesting. An improved YOLOv5s-SA target detection algorithm was used to calculate FUM density and was deployed on the Jetson Nano edge-computing platform. Combined with a fuzzy control algorithm, it drives the servo electric cylinder to achieve dynamic depth adjustment of the scraping board. Test results showed that, after deploying the target detection algorithm on the edge AI device and accelerating it with TensorRT, the average inference time was 0.077 s, and the system response time was 0.26 s, meeting the real-time requirements of agricultural operations. Simulation results indicated that the average error between the stripping depth of the automatic control system and the preset depth was 3.72 mm, representing a 44.1% improvement compared with fixed-depth control. The average ideal stripping rate reached 54.96%, an improvement of 21.66% over the 33.3% achieved under fixed-depth control.

To achieve high-precision non-destructive monitoring of SPAD values in winter wheat, this study proposes an estimation method integrating multi-feature optimization with machine learning. Based on UAV multispectral imagery and synchronous ground measurement data from 33 plots, the research was conducted during three critical growth stages: jointing, heading, and grain filling. The PCC-RF-CV method was employed for feature fusion and optimization, identifying optimal feature combinations for each stage from multiple vegetation indices and texture features. Six machine learning models were constructed for comparison. Results indicate: the multi-source feature fusion strategy demonstrated superior performance throughout all growth stages; PCC-RF-CV effectively optimized feature inputs, establishing optimal feature sets for each stage; The XGBoost model developed for the grain filling stage achieved the best estimation performance (validation set R² = 0.92, RMSE = 0.36, MAE = 0.30). This study provides a reliable method for accurately estimating SPAD values in winter wheat and analyzing canopy spectral dynamics, offering robust technical support for crop growth monitoring and precision agriculture.

To address issues such as clogging of threshing units, high grain loss rates, and elevated impurity levels in wheat breeding harvesters, this study developed a combing-type threshing and separation system for wheat plot harvesters. The system adopts a closed plate-tooth threshing drum structure, where the airflow generated by the rotation of drum blades is combined with external impurity-cleaning airflow, thereby reducing material accumulation and seed retention in the threshing and separation system. CFD-DEM coupling simulations determine the optimal parameter combination: when the drum rotational speed is 891 r/min, the feeding rate is 0.58 kg/s, and the impurity-cleaning airflow speed is 22.56 m/s, the system achieves a loss rate of 0.87% and an impurity rate of 9.06%. A field performance test using this parameter combination showed that the system’s loss rate was 1.09% and impurity rate was 11.13%. This study provides a reference for the design and research of threshing and separation systems in comb-type wheat plot harvesters.

This paper presents a computer vision-based methodology for evaluating the grasping performance of a soft robotic gripper fabricated from mechanical metamaterials, designed specifically for fruit and vegetables harvesting applications. Due to the fragile nature of fruits such as tomatoes or strawberries, the ability to assess and control the deformation of the gripper during interaction is critical to avoid damage while ensuring a secure grasp. A deep learning approach is proposed, leveraging convolutional neural networks (CNNs) to classify grasp outcomes from visual input. The model is trained on a custom dataset of images captured during robotic harvesting trials and optimized to detect subtle variations in gripper shape and fruit contact. The integration of soft metamaterial-based grippers with computer vision algorithms enables a robust, non-invasive grasp assessment pipeline, contributing toward fully autonomous and adaptive fruit-picking robots. The proposed method achieved an accuracy of 94.0% for correct grasps, 91.5% for failed grasps, and 95.9% for no-object cases, with an average inference time of 87 ms (ranging from 75 to 98 ms).

In response to the problems of high glume cluster rate, significant losses, and limited adaptability during millet combine harvesting in China, an independent test bench for a single longitudinal axial-flow threshing and separation device was designed and constructed. Millet moisture content, feeding rate, and threshing drum rotational speed were selected as the influencing factors, while impurity rate, glume cluster rate, and total loss rate were used as evaluation indices. Single-factor experiments and orthogonal rotational combination tests were conducted. The experimental results show that the order of influence on impurity rate, glume cluster rate, and total loss rate is: moisture content > rotational speed > feeding rate. The optimal operating parameters obtained were: moisture content of 24.82%, feeding rate of 2.25 kg/s, and drum rotational speed of 835 r/min. Under these conditions, the impurity rate reached 31.90%, the glume cluster rate was 21.56%, and the total loss rate was 0.72%. These findings provide a technical reference for the future design and development of single longitudinal axial flow threshing and separation devices specialized for millet harvesting.

In response to the problems of high glume cluster rate, significant losses, and limited adaptability during millet combine harvesting in China, an independent test bench for a single longitudinal axial-flow threshing and separation device was designed and constructed. Millet moisture content, feeding rate, and threshing drum rotational speed were selected as the influencing factors, while impurity rate, glume cluster rate, and total loss rate were used as evaluation indices. Single-factor experiments and orthogonal rotational combination tests were conducted. The experimental results show that the order of influence on impurity rate, glume cluster rate, and total loss rate is: moisture content > rotational speed > feeding rate. The optimal operating parameters obtained were: moisture content of 24.82%, feeding rate of 2.25 kg/s, and drum rotational speed of 835 r/min. Under these conditions, the impurity rate reached 31.90%, the glume cluster rate was 21.56%, and the total loss rate was 0.72%. These findings provide a technical reference for the future design and development of single longitudinal axial flow threshing and separation devices specialized for millet harvesting.

This study develops a dual-objective control system to enhance transplanter performance on uneven terrain by synergistically integrating lateral leveling and longitudinal depth profiling. A kinematic analysis revealed strong motion coupling, which is effectively decoupled using a novel Super-Twisting Sliding Mode Active Disturbance Rejection Control (STSMC-ADRC). Co-simulation and field tests confirmed the superiority of STSMC-ADRC over conventional PID and ADRC, demonstrating faster response, minimized steady-state error, and robust disturbance rejection. The proposed strategy significantly improves platform stability and planting depth consistency, offering a robust solution for slope operations.

To address the challenges of irregular morphology and the difficulty in rapidly and accurately measuring the area of laser cladding zones during the inspection of agricultural rotary tiller blades, this paper proposes a machine vision-based method for rapid area extraction. A comprehensive processing workflow encompassing image preprocessing, contour extraction, region of interest (ROI) extraction, and pixel integration was established. For region segmentation, an improved Alpha Shapes segmentation algorithm was proposed and compared against conventional Convex Hull and Delaunay triangulation methods. Validation was conducted using 100 rotary tiller blade samples, with electron microscopy manual calibration results serving as reference. Results indicate the improved Alpha Shapes algorithm delivers optimal segmentation accuracy, yielding the smallest absolute area error (-7.40×10-7 ± 2.69×10-5)m² and lowest relative error (13.48 ± 8.47)×10-3, with high consistency against microscopic measurements. Compared to conventional manual measurement, the area extraction algorithm proposed in this study offers the advantages of automation, non-contact operation, and high efficiency, meeting the engineering application requirements for laser cladding quality inspection.

This study investigates the effect of electrostatics in UAV spraying to enhance droplet deposition efficiency during the blooming stage of apple trees. Experiments were conducted under two operating conditions - electrostatic spraying and non-electrostatic (conventional) spraying - at UAV flight heights of 1.0 m, 2.0 m, and 3.0 m above the apple tree canopy. Spray deposition was measured by placing test paper on the upper, middle, and lower canopy layers, on both the front and the back sides of the leaves. The results show that, compared with conventional UAV spraying, electrostatic force increased droplet number density by 17.6% and reduced droplet size by 25.3%, thereby improving overall spray performance. This provides notable advantages for pollination and pest control during the early growth stage of apple trees. The electrostatic charge generates an attractive force between charged droplets and the target surface, enhancing droplet adhesion and deposition on the back side surfaces of lower leaves and flowers. However, this advantage is not significant on the back sides of upper and middle canopy leaves due to limited recirculating airflow reaching those locations. These findings support the application of electrostatic technology to improve pollination efficiency and precision pesticide spraying during the early developmental stage of apple trees.

This study investigates the interaction mechanisms between the dibber, soil, and garlic during directional garlic seeding using the Discrete Element Method (DEM), aiming to improve planting uprightness and planting-depth consistency. A discrete element model of the garlic seeding process was established to analyze the dynamic interactions among the dibber, soil, and garlic from a microscopic perspective, and the reliability of the simulation results was verified through macroscopic experiments. The results show that dibbling depth, lifting height, dibbling speed ratio, and soil-particle surface energy are key factors influencing planting uprightness and depth consistency. Within a certain range, increasing the dibbling depth and lifting height significantly improves uprightness, although the effect diminishes beyond critical thresholds. The influence of the dibbling speed ratio on uprightness exhibits a nonlinear trend, in which both excessively low and excessively high ratios reduce uprightness. In addition, lower soil-particle surface energy leads to increased uprightness and improved soil backfilling performance. Experimental validation confirmed strong agreement between the simulation and physical test results, with an average relative error of less than 10%. This study provides a theoretical foundation and numerical simulation tools for optimizing directional garlic seeding technology, offering important guidance for improving planting uprightness and planting-depth consistency.

To address the limited research on the material properties of ruminant protein supplement feed pellets and the lack of an accurate calibration framework for discrete element method (DEM) contact parameters, which constrained the optimization of related mechanical equipment, this study investigated the fundamental physical and contact characteristics of protein supplement pellets. A DEM-based particle model was established, and the angle of repose was selected as the evaluation index. The Plackett–Burman design was employed to identify the primary influencing factors, followed by a Steepest Ascent test and a Box–Behnken design to develop a regression model for parameter optimization and calibration. The results indicated that when the static friction coefficient, rolling friction coefficient, and restitution coefficient between particles were set to 0.52, 0.03, and 0.37, respectively, the restitution coefficient between particles and materials, as well as the static and rolling friction coefficients between particles, had significant effects on the angle of repose. The optimal parameter values were determined to be 0.45, 0.55, and 0.05, achieving the theoretical optimum. Validation experiments showed that the average deviation between the calibrated and measured angles of repose was 0.92%, demonstrating a high level of agreement between the simulated and experimental results. These findings provided a reliable theoretical reference and parameter basis for the optimization of processing, conveying, and key component design in ruminant protein supplement feed machinery

Feed rate is a critical operating parameter in combine harvesters, as it directly influences working efficiency and harvesting quality. To enable real-time and accurate feed rate monitoring, this study proposes a wireless monitoring approach based on pressure measurements at the bottom plate of the chain-rake conveyor inlet. A coupling model relating feed rate, chain-rake speed, and bottom plate pressure was established through theoretical analysis. To enhance robustness against signal fluctuations, the trimean was selected as the feature metric. A monitoring system incorporating LoRa wireless transmission was developed to achieve data acquisition, transmission, and visualization. Bench test results showed that the established model achieved a goodness-of-fit R² of 0.9963, and the predictive relative error was below 5% under most working conditions, demonstrating that the system provides high accuracy and stability in complex operating environments. The proposed method offers an effective technical solution for feed rate monitoring in combine harvesters.

To address the low level of standardization in cassava planting and the poor operational performance of existing harvesting machinery in China, this study proposes a wide-ridge, double-row cassava planting pattern that integrates current agronomic practices with harvesting requirements. Based on this planting pattern, a double-row vibrating cassava harvester was designed, and its overall structure and key parameters were determined. Using analytical methods, the displacement equation and motion-trajectory equation of the digging-shovel tip were established, and the corresponding motion-trajectory plots were generated using MATLAB. The cutting, lifting, and impact interactions between the digging shovel and the soil/cassava–soil complex were theoretically analyzed. Field tests of the prototype showed that its pure working productivity reached = 0.52 hm²/h, the clean-root rate was = 97.33%, the damaged-root rate was = 1.43%, and the total loss rate was = 2.67%. The results demonstrate that the double-row vibrating cassava harvester operates stably and reliably. The vibrating digging shovel exhibits strong soil-breaking ability and enables rapid and effective separation of cassava from the soil. All performance indicators meet or exceed the design specifications.

With the continuous increase in the volume of goods produced, the need to develop high-capacity road transport vehicles that can distribute this volume to various destinations has also increased. The increase in transport capacities involves technical equipment that can take over these capacities. Among the structural components of the mentioned systems, an important role is played by the running system, which at high transport capacities must be equipped with three/four axles. For the most efficient running and with acceptable efforts and wear, especially when entering corners, these running systems must be equipped with one/two self-steering axles, built in such a way as to take over certain additional demands that arise compared to rigid axles. Most of the constructive and functional solutions adopted by established manufacturers for self-steering running system include data resulting from their own research, less widely disseminated, intellectually protected, based on testing, starting from known concepts and previous experience. This paper presents theoretical and experimental research of an alternative way of approaching the concept, by performing an analysis of the kinematics of the self-steering axle when passing over bumps, following which the constructive and functional running parameters will be evaluated, the effects that occur and ways to mitigate or eliminate vertical and transverse oscillations of the axle as well as conclusions and recommendations for manufacturers. The values of the recorded vertical oscillations ranged between 40-80 mm for unevenness between 25-100 mm speeds between 0.5-5 m/s and the transverse ones between 30-10 mm, under the same conditions.

As a vital food crop, rice plays a crucial role in the global food supply. Accurate seed sorting is critical for planting and sales, but traditional variety identification methods are time-consuming, inefficient, and prone to causing physical damage to seeds. To enhance identification efficiency and classification accuracy, this study employed an image acquisition system to capture images of eight locally grown rice seed varieties. After preprocessing and segmenting the original images to improve data quality, multi-dimensional features were extracted and analyzed to construct a deep learning model for rice seed identification. The results showed that the Rice-Transformer model, based on the Transformer architecture, achieved a classification accuracy of 97.71%, demonstrating excellent identification capabilities. Additionally, this study developed a user interface based on PyQT5 to visualize the identification results. It can provide a feasible solution for the efficient and non-destructive identification of rice seed varieties and has the potential to be applied in consumer markets and the food industry.

This paper proposes a Hybrid Complete Coverage Path Planning (HCCPP) algorithm to enhance the efficiency and smoothness of suspended mowers operating in convex polygonal fields. Combining straight-in, nested, and outward-spiral strategies, it optimizes internal and boundary coverage while using Hybrid A* and Bézier curves for smooth transitions. The simulation experiment uses a suspended lawn mower (Dongfanghong LX804 rear lawn mower) as the test platform, with parameters: dimensions of 6.50 m × 2.17 m × 2.87 m, working width of 2.5 m, minimum turning radius of 6.2 m, and minimum row spacing of 12.5 m. The simulation experiment of running 5 times for each of the 3 plots shows that HCCPP achieves >99.7% coverage, <5.4% overlap, 4–9% shorter paths, and lower curvature variation, outperforming traditional methods and offering an efficient solution for autonomous agricultural path planning.

At present, the oblique seedling picking-and-release device of vegetable transplanters has low positioning accuracy and poor stability, which easily leads to issues such as failing to pick, damaging seedlings, and breaking seedling grippers during operation. Conventional PID control is difficult to meet the positioning control requirements. A step positioning control system based on fuzzy PID was designed, the transfer function of the control system was determined, and a fuzzy PID controller was established. Simulation analysis shows that with the displacement of the seedling picking-and-release frame as the system input, under optimal parameters, the times to reach steady state taken for conventional PID control and for fuzzy PID control are 1.2 s and 0.5 s, respectively. Field test results show that under fuzzy PID control, the maximum displacement error is reduced to 2.6 mm, with a relative error of 1.24%, which is less than the maximum relative error allowed for the displacement of the seedling picking-and-release frame. Fuzzy PID control has a shorter adjustment time, enabling rapid and precise positioning of grippers, and improving the success rate of seedling picking. The research results can provide reference and basis for the development of the control system for seedling picking-and-release device.

In recent years, the development of vineyard robots has emerged as a significant development in agricultural equipment, playing an increasingly vital role in precision agriculture and intelligent operations. These robots are capable of precise navigation, obstacle avoidance, and real-time path planning within agricultural settings. The paper employs laser Simultaneous Localization and Mapping (SLAM) technology as the primary method for achieving real-time, accurate positioning of the robot, thereby providing reliable environmental perception capabilities and prior map information for the vineyard robot. The Robust-Time Elastic Band (R-TEB) local planning algorithm developed in this study automatically generates a smooth, continuous inspection path within the operational area. This objective is pursued by the consideration of parameters such as the robots working width, minimum turning radius, and operational strip width, with the aim of achieving a minimization of energy consumption. Utilizing the Root Mean Square Error (RMSE) metric to gauge prediction accuracy, the R-TEB algorithm yielded values ranging from 0.016 to 0.022 meters, while the TEB algorithm produced values between 0.012 and 0.025 meters. The findings indicate that the R-TEB algorithm optimizes trajectory quality in vineyard environments, thereby enhancing the robots autonomous navigation capabilities and obstacle avoidance efficiency.

Intensive agriculture-driven soil degradation has become a global environmental challenge, urgently requiring governance strategies that integrate farmer behavior with policy intervention to support sustainable development. In this study, matched soil experimental data and household survey responses were used to assess the status and degradation characteristics of key soil nutrient indicators, including soil pH, total nitrogen, ammonium nitrogen, nitrate nitrogen, available phosphorus, quick-acting potassium, organic carbon, and the C/N ratio. The effects of policy measures and farmers’ behavioral practices on both individual soil nutrient indicators and the overall degree of soil degradation were empirically examined. Across 178 plots with an average management duration of 17.93 years, widespread soil acidification (mean pH = 4.535), nitrogen saturation effects, and other nutrient imbalances were identified. The application of restoration technologies was found to significantly reduce soil degradation, although the magnitude of improvement varied by nutrient type. Technical training and farmland transfer policies indirectly mitigated soil degradation by promoting the adoption of restorative practices. Furthermore, combinations of policy instruments demonstrated synergistic effects, compensating for the limitations of single-policy approaches. These findings highlight the need for policy frameworks that incorporate degradation-based targeted guidance, restoration subsidies, and standardized farmland transfer mechanisms. The study deepens understanding of the micro-level mechanisms linking farmer behavior with soil ecological processes and provides empirical evidence supporting progress toward the United Nations Sustainable Development Goal of achieving zero net land degradation.

Currently, most vegetable transplanters are semi-automatic and require manual seedling picking and feeding into the transplanting mechanism. To achieve fully automatic seedling picking and feeding, this study developed an automatic seedling feeding device for a typical single-disc vegetable transplanter. The device consists of three main functional components designed to ensure accurate and continuous seedling delivery: a seedling conveying unit, a seedling feeding unit, and a seedling guiding unit. The seedling feeding success rate was selected as the evaluation indicator, while root plug moisture content, pick-up needle angle, and seedling height were chosen as test factors. Experimental results showed that the optimal conditions - root plug moisture content of 45%, pick-up needle angle of 9°, and seedling height of 50 mm - resulted in a feeding success rate of 92.4%. These findings demonstrate that the proposed device meets the required transplanting efficiency and precision standards for vegetable transplanters. Overall, the developed seedling feeding device provides a theoretical and technical basis for advancing automation in vegetable transplanting equipment.

In the modern food industry, improving bakery product quality through functional ingredients is a growing focus. Fruit powders or flours derived from pulp or by-products such as peels and seeds provide fibers, antioxidants, and bioactive compounds that enhance nutritional value, functionality, and shelf life. However, soil contamination with heavy metals negatively impacts cereal crops, lowering flour quality and food safety. Incorporating fruit powders from chokeberry, sea buckthorn, currants, cranberries, rosehip, raspberry, apple, or grape can mitigate these effects while improving dough properties and texture. This study demonstrates fruit powders’ potential as sustainable ingredients for high-value functional bakery products.

The objective of this study is to address the challenges associated with manual, labor-intensive plum harvesting in hilly and mountainous regions. To overcome these limitations, a crawler-type plum harvester was developed based on the operating characteristics of orchards in such terrain. The harvester employs an excitation-and-vibration mechanism to detach fruits and facilitate efficient collection. To investigate the key factors influencing plum detachment under vibratory conditions, a mechanical vibration model of the fruit–stem system was established. The model analysis identified vibration frequency (?) and vibration amplitude (A) as the primary influencing parameters. A finite element simulation of plum trees was subsequently conducted using ANSYS software. The simulation results indicated that the suitable vibration frequency range for effective plum detachment is between 6.60 Hz and 9.43 Hz. Finally, field experiments were carried out to determine the optimal vibration frequency. The net harvesting rate and fruit loss rate were selected as evaluation indicators. The experimental findings verified the accuracy of the theoretical model, and the optimal vibration frequency for plum harvesting was determined to be 7–8 Hz.

Addressing issues such as high soybean seedling detection omission rates and inaccurate target recognition during mechanical weeding operations in soybean fields, which lead to low weeding efficiency, this paper proposes a lightweight convolutional model based on an improved YOLO11 model. Deployed on an intelligent mechanical soybean weeding robot, it utilizes precisely identified soybean seedling coordinates to perform mechanical weeding operations, thereby enhancing weeding efficiency. Building upon the original YOLO11 architecture, this model replaces standard convolutional blocks with Deep Separable Convolution (DWconv) modules. It performs channel pruning on the C3K2 lightweight convolutional module and employs Point-Shuffle operations for channel mixing to enhance feature map information flow, thereby improving edge feature recognition for small targets. The introduction of an Efficient Channel Attention (ECA) mechanism increases channel selectivity for large target features, enhancing sensitivity to critical semantic information. The original loss function is optimized by incorporating an improved bounding box loss function (SIOU), accelerating model convergence and strengthening generalization capabilities. The improved YOLO11 model achieved a 2.0 percentage point increase in mAP50% on the self-built soybean dataset compared to the original YOLO11, reaching 94%. Model parameters and floating-point operations were reduced from 2.59MB and 6.4×106 to 1.97MB and 5.0×106 respectively, representing decreases of 23.9% and 21.9%. This achieves synergistic optimization of model lightweighting and computational efficiency while maintaining detection accuracy.

This study scientifically validates a tool for identifying and assessing occupational risks in the agricultural and food industry, aiming to enhance worker health and safety. Developed through a systematic literature review, the tool integrates theoretical and operational perspectives, addressing both traditional and emerging risks from digitalization - such as ergonomic, psychosocial, and managerial factors linked to technology use and automation. Validation, via comparative and statistical analyses (e.g., Gaussian distributions, Cronbach a coefficient), confirmed its reliability and applicability. The tool proves effective in supporting risk prevention strategies adapted to evolving, digitally influenced organizational environments.

In this study, an autonomous navigation robot for poultry house inspection was designed, and a path optimization and obstacle avoidance strategy was proposed. First, a filtering algorithm was used to extract regions of interest from the 3D point cloud data collected by the inspection robot in caged poultry houses. Then, the geometric structure of cage-row lines was estimated using the least-squares method and refined using the RANSAC algorithm. The refined lines were projected to obtain boundary contour features. Finally, the A* algorithm was improved by removing redundant nodes, reducing the number of turning points, shortening the total path length, and increasing the weight of the cost estimation. The improved A* algorithm was also validated through physical robot simulation tests. Experimental results showed that compared with the least-squares method (LSM), the RANSAC-based approach achieved cage-row line slope values of 0.223 and 0.224 under Gaussian noise and manually added noise, respectively, demonstrating superior noise robustness and real-time performance. The results further indicate that the improved A* algorithm enhances path planning efficiency, enabling the robot to make timely decisions when encountering static or dynamic obstacles, thereby improving overall stability and reliability.

Agricultural soil contamination is one of the major global environmental problems, with direct effects on ecosystem quality, agricultural productivity, and human health. Among the methods for remediating contaminated soils, bioremediation is one of the most economical and environmentally favourable innovation. Laccases, enzymes produced by fungi, bacteria, and certain plants, are especially valuable because they can degrade a wide range of hazardous organic pollutants by simple oxidation reactions. This review summarizes the bioremediation concept, the most recent developments in soil bioremediation, and the environmental importance of this concept. Also, the article presents the action mechanisms of laccases, the life cycle analysis for bioremediation systems with laccases, providing a comprehensive overview for future developments.

With the acceleration of high-performance, green, and intelligent agricultural equipment, premature wear and failure of agricultural machinery tools became a key bottleneck that restricted the high-quality development of agricultural machinery and equipment. Digital twin technology provided innovative theoretical and technical support, which enabled the accurate prediction and evaluation of the wear performance of agricultural machinery tools under dynamic and complex working conditions. This paper explained the key elements of digital twin technology and summarized the development history of tool wear research, categorizing it into three stages: physical experiment-driven, numerical simulation, and digital twin integration. Additionally, it highlighted the progress made in agricultural machinery tools based on digital twin technology, particularly in data acquisition, modeling, and data-driven approaches. The paper also introduced a case study of a self-developed agricultural machinery tool wear performance test machine. However, it addressed the key challenges faced in the application of digital twin technology for monitoring agricultural machinery tool wear, including difficulties in data perception and fusion, insufficient accuracy in multi-physical field modeling, and inadequate real-time performance. Future research focused on developing accurate multi-physics field coupling models, optimizing data processing mechanisms, and creating intelligent analysis frameworks. Additionally, it aimed to promote low-cost and efficient digital twin solutions to enhance the intelligence level and feasibility of agricultural machinery tool wear monitoring.

This study provides an original contribution through an integrated analysis of the vegetative growth and yield dynamics of the raspberry cultivar ‘Opal’, conducted over two consecutive years (2023–2024) under the specific pedoclimatic conditions of the Baneasa area in Bucharest. During the planting year (2023), early vegetative growth was evaluated, while in the productive year (2024), vegetative, yield-related, and pedoclimatic parameters were analyzed. A comprehensive analytical approach was applied, combining polynomial regression models (R² > 0.95) with multiple regression and Pearson correlation analyses to investigate multifactorial relationships. The results revealed a pronounced seasonal asynchrony: yield reached a clear maximum in June (391.6 g per plant) and subsequently declined, whereas vegetative growth continued, indicating a marked reallocation of resources following fruiting. Yield showed a strong negative correlation with vegetative development (r = -0.93) and positive correlations with solar radiation and soil moisture (r = 0.78). The final multiple regression model, integrating plant height, stem diameter, solar radiation, and soil moisture, explained 99.8% of yield variability (R² = 0.998), demonstrating strong predictive capability. The findings provide a solid scientific basis for optimizing raspberry cultivation practices and support the development of more efficient yield systems adapted to local climatic variability.

At present, peanuts are predominantly harvested using a two-stage harvesting method. During the digging and laying stage, peanut plants are generally laid sideways for drying. Owing to differences in light exposure and air permeability, the moisture content of the pods becomes non-uniform, which adversely affects subsequent picking and harvesting operations. To address these issues, a two-ridge, four-row directional inversion peanut digging and spreading harvester was designed based on the plant directional inversion mechanism. Field experiments demonstrated that the primary operating parameters influencing peanut inversion and laying performance were clamping height, conveying speed, and the horizontal inclination angle of the clamping chain. Using peanut inversion degree and pod loss rate as evaluation indicators, an orthogonal experimental design was employed to determine the optimal parameter combination: clamping height of 170 mm, conveying speed of 1.5 m/s, and clamping chain inclination angle of 15°. Field test results showed that, after clamping, conveying, and inversion operations, the peanut inversion degree reached 94.4%, while the pod loss rate was limited to 3.5%.

Aeolian sand transport is a key driver of desertification; however, accurately modeling particle–fluid interactions remains challenging. Many existing numerical simulations assume spherical grains, which can introduce systematic errors in transport predictions. To address this limitation, a CFD–DEM framework incorporating superquadric particles was developed, enabling a more realistic representation of grain geometry. Simulations were conducted at wind speeds of 9, 12, and 15 m/s, with systematic variations in particle axis ratio and shape parameters, and the results were validated against wind tunnel experiments. The results reveal a clear hierarchical control of aeolian transport dynamics. Wind speed dominates transport intensity and temporal evolution. The particle axis ratio exerts the primary influence on streamwise transport, producing variations of up to 96% in mean particle velocity, whereas shape parameters induce smaller changes of approximately 53%. In contrast, particle shape parameters govern vertical transport behavior, causing velocity variations of up to 91.6%, compared with less than 74.4% attributable to axis ratio effects. Moreover, the influence of shape parameters weakens with increasing wind speed, with maximum variations of 83%, 67%, and 59% at wind speeds of 9, 12, and 15 m/s, respectively. This study enhances the accuracy of wind-sand transport simulations and contributes to improved predictions of wind-driven sand impacts on soil, crops, and water resources in farmland. The improved simulations provide scientific support for agricultural wind and sand control and ecological restoration, promoting sustainable agricultural development and mitigating the negative effects of desertification.

Traditional manual cutting of seed potatoes is a labor-intensive, time-consuming, and inconsistent process that limits large-scale agricultural productivity. To address these challenges, this study aimed to develop and validate an automated, high-speed robotic system for precise cutting angle estimation. SPADE (Smart Potato Angle Decision Engine), an innovative framework integrating deep learning and machine learning algorithms, is proposed. The SPADE framework is implemented in three stages. First, a custom detection model, termed BUD-YOLO, was developed for the high-precision identification of potato eyes. Second, the K-means algorithm was employed to partition the spatial coordinates of the detected eyes into two distinct clusters. Finally, a Support Vector Machine (SVM) determined the optimal cutting plane by identifying the maximum-margin hyperplane between these two clusters. The proposed SPADE framework was implemented and tested on a custom-built robotic platform with a sample of 100 potatoes. The system achieved an 85% cutting qualification rate with an average processing time of 2.5 seconds per potato, a speed approximately 2-3 times faster than traditional manual labor (5-9 seconds per potato). This study successfully demonstrates an end-to-end solution for the automated cutting of seed potatoes. The developed SPADE framework not only achieves a competitive qualification rate but also significantly enhances production throughput, offering substantial practical value for the advancement of intelligent agricultural equipment.

In view of the low mechanical sowing rate of peanuts in China, the existing seeders had problems such as insufficient sowing accuracy and slow speed, with the core goal of improving sowing accuracy, a key component of the sowing integrated machine - the spoon-wheel type seed conveyor - was designed. Based on mechanical analysis, the key parameters of the U-shaped groove inclination Angle of the seed spoon being 26° and the side Angle not less than 50° were obtained. The discrete element simulation model of the seed seeder was established by using EDEM software. Taking the rotational speed of the seed seeder, the side Angle of the seed spoon, and the top width of the seed spoon as the experimental factors, and the replay rate and missed broadcast rate as the indicators, single-factor and three-factor three-level orthogonal experiments were carried out, and the parameters were optimized through the response surface method. The results showed that the optimal parameter combination was the side Angle of the seed spoon 54°, the rotational speed of the seed spoon wheel 20r/min, and the width of the top surface of the seed spoon 11.9mm. At this time, the replay rate was 5.19% and the missed broadcast rate was 5.43%, both of which met the requirements of industry standards.

Vacuum impregnation is a minimal processing method with wide applicability in porous food products, and can be used both for the development of functional foods and for the production of fortified food products. The experimental research presented in this paper aimed to highlight the effects of process parameter variation on mass gain during the vacuum impregnation of apple slices, using an experimental vacuum impregnation system developed by INMA within a national research program. Water was used as the impregnation liquid in the demonstrative experiment. The process parameters monitored during vacuum impregnation were: the vacuum pressure inside the impregnation vessel, immersion depth, holding time at the set vacuum pressure, and internal pressure equilibration time. The experimental results showed that vacuum pressure (investigated range: 50–350 mbar) is the main factor influencing mass gain of the treated product, decisively affecting the efficiency of the impregnation process. In this context, the lower the vacuum pressure (in this case, 50 mbar), the greater the capacity of the product to incorporate physiologically active compounds, with a potential direct positive effect on product quality, nutrient content, and shelf life. The effect of vacuum pressure is further modulated by the holding time, particularly at short holding periods (120 s). It appears that extending the holding time does not necessarily improve impregnation efficiency, especially at low vacuum pressures.

Soybean emergence uniformity is critical for yield formation, and the early acquisition of this information provides valuable guidance for field management. However, regional variations in cultivation patterns restrict most existing detection models to a single pattern, thereby limiting their practical applicability. To overcome this limitation, this study developed a universal detection system for assessing soybean emergence uniformity across diverse cultivation patterns. The system employs unmanned aerial vehicles (UAVs) to acquire field images, after which the YOLOv12 model is used to detect seedlings and extract their center coordinates. A two-stage clustering algorithm (Elliptical DBSCAN + K-means) is applied to classify seedlings into rows, and plant spacing is calculated by integrating Euclidean distance with ground sampling distance (GSD). Emergence uniformity is subsequently evaluated using the ISO-standard Multiple and Miss Indices. Field validation across ridge-based double-row, triple-row, and quadruple-row cultivation patterns yielded coefficients of determination (R2) of 0.9919, 0.9887, and 0.9924, respectively, with no significant differences compared to manual measurements (all p > 0.05). The system achieved plant-spacing detection accuracies of 96–97% during the low-occlusion VE (Vegetative Emergence) and VC (Vegetative Cotyledon) stages; however, accuracy for the quadruple-row pattern decreased to 79% at the V1 (Vegetative 1) stage due to severe leaf occlusion. This study presents the first detection framework applicable across multiple soybean cultivation patterns, providing a high-accuracy and reliable tool to support informed field management decisions.

To address the issue of high damage rates during the operation of peanut shelling machines, a low-damage peanut sheller was designed. This machine effectively reduces damage while improving shelling efficiency. The mechanism and working principles of the device are elaborated, and the critical shelling components are designed based on theoretical and mechanical analysis. The shelling process was simulated using Edem simulation software. Additionally, a quadratic orthogonal composite design experiment was developed using Design Expert to determine the optimal parameters. The final configuration, with a primary shelling drum speed of 302 r/min, a primary concave sieve gap of 10 mm, and a secondary concave sieve gap of 8 mm, achieved a shelling efficiency of 98.28% and a pod damage rate of 4.93%, outperforming industry standards. Field tests showed minimal discrepancy between experimental and simulation results.

Population growth and technological progress significantly contribute to the climate change intensification, the increasing global energy demand, and the depletion of natural resources, while also generating massive amounts of waste. Agriculture is one of the main sectors with the highest biomass production, representing an essential factor for the bioeconomy. Agricultural wastes represent a sustainable and abundant source of organic matter, used in the production of biofuels, which are considered a promising alternative to meeting the global energy demand. Thus, considering the challenges associated with agricultural waste generation and their environmental impacts, this review aims to provide a comprehensive analysis of recent advances in the valorization of agricultural waste for sustainable biofuel production. The paper focuses on the main categories of agricultural waste, the associated conversion technologies, and the integration of these processes within the circular economy framework. In addition, to achieve the objectives of the study, a bibliometric analysis was conducted on publications from 2012–2024 indexed in the Web of Science Core Collection (WoS), highlighting research trends, leading journals, and thematic clusters in the field of agricultural waste valorization.

Frequent greenhouse collapses under strong wind conditions highlight the limitations of existing structural design methods. Although most previous studies have focused on the stability and load-bearing capacity of greenhouse frames, the dynamic interaction among the greenhouse film, tensioning straps, and structural frame remains insufficiently investigated. This study proposes a novel flat-elliptical pipe plastic greenhouse and derives its mechanical equilibrium equations to analyze the stress behavior of the greenhouse film under wind loading. Using ABAQUS finite element software, an advanced contact model was developed to examine the wind-induced response of the greenhouse system. The results indicate that the structural frame plays a critical role in governing film deformation. In particular, the contact pressure and shear stress between the frame and the film are significantly higher than those between the film and the tensioning straps, underscoring the necessity of explicitly considering contact effects in greenhouse structural design.

The article presents a developed design of a sectional pneumatic screw conveyor intended for laboratory experimental studies on the transport of bulk materials used in the food industry. To determine the influence of bulk material transport parameters and conveyor design parameters on productivity (the optimization parameter Q), a full factorial experiment was conducted. Based on the experimental results, corresponding regression equations and response surfaces were developed to establish the influence of the controlled factors on productivity. Investigation of the transport process made it possible to determine the dependence of productivity on several factors characterizing the process, namely: the screw rotational speed n (rpm), the outlet opening area of the conveyor hopper S (cm2), the moisture content of the transported material W (%), and the air pressure P (MPa). Thus, productivity can be expressed as a functional relationship of the form: Q = f (n, S, W, P).

In the context of rising electricity costs and the increasing impact of climate change, modern agriculture faces major challenges in ensuring reliable water and energy resources, particularly in isolated rural areas. Irrigation systems require a secure and continuous energy supply; however, access to the electrical grid is often limited or unstable. The aim of this paper is to analyze, size, and evaluate the energy, economic, and environmental performance of a hybrid solar–hydro energy system designed to supply water pumps in an organic agricultural farm. The methodology includes mathematical modeling of the system components, simulation of system operation in the MATLAB/Simulink environment, and performance analysis based on a real case study—the EcoVerd organic farm. The results demonstrate that the hybrid system, consisting of a 4.8 kW photovoltaic installation, a 1.5 kW micro-hydropower unit, and a 9.6 kWh energy storage system, fully meets the energy demand of a 2.2 kW irrigation pump. System implementation leads to an approximately 95% reduction in energy costs, annual savings of up to €3,885, a payback period of less than four years, and the avoidance of approximately 1.8 tons of CO2 emissions per year.

To address problems such as frequent stubble entanglement and the heavy burden associated with separating potatoes from impurities during potato harvesting operations, a belt-type clamping and pulling device for potato stubble was developed to achieve complete stubble extraction. By analyzing the interaction between the stubble and the clamping and pulling belt (CPB), the causes of missed extraction and stubble breakage were identified, and the primary parameter ranges affecting extraction effectiveness were determined. An EDEM–RecurDyn coupled simulation model was established to investigate the stubble pulling process. Using the stubble breakage rate and stubble miss rate as evaluation indices, the main factors influencing pulling performance were identified as the forward speed of the device, the linear velocity of the CPB, and the ground clearance of the CPB. A three-factor, three-level orthogonal experiment was conducted, and quadratic regression models were developed with stubble breakage rate and stubble miss rate as response variables. Response surface analysis and parameter optimization were subsequently performed. The results indicate that at a device forward speed of 0.512 m/s, a CPB linear velocity of 1.08 m/s, and a CPB ground clearance of 50 mm, the stubble breakage rate and stubble miss rate were 6.45% and 8.73%, respectively. An experimental platform was constructed to validate the simulation results. Experimental tests showed that under the optimal parameter combination, the stubble miss rate was 7.5%, with a small relative error compared to the simulation results. These findings demonstrate that the proposed device meets the operational requirements for effective potato stubble pulling.

The identification of farmland weeds and variable rate herbicide spraying technology are core components of precision agriculture, playing a significant role in enhancing agricultural productivity, reducing pesticide usage, and protecting the ecological environment. Currently, global agriculture faces dual challenges of increasing resource constraints and rising environmental protection demands. This technology, by precisely locating weed distribution and adjusting pesticide application rates accordingly, has become a key approach to breaking the vicious cycle of "pesticide overuse-weed resistance-ecological pollution." Based on bibliometric methods and using the Web of Science database as the data source, this study retrieved literature related to farmland weed identification and variable rate herbicide spraying from 2005 to 2024. VOSviewer software was employed for visual analysis, systematically examining the temporal evolution characteristics, regional collaboration networks, institutional contributions, and keyword clustering patterns in this field. The results indicate that research in this area entered a rapid development phase after 2018, driven significantly by artificial intelligence technology. Research hotspots focus on image recognition algorithms, multi-source data fusion, variable rate herbicide spraying system design, and field application validation. Current studies face challenges in adaptability to complex environments and multi-scale data coordination. Future efforts should strengthen lightweight recognition model optimization, space-air-ground integrated data fusion, cost-effective smart equipment development, and interdisciplinary collaboration to provide technical support for the sustainable development of precision agriculture.

This paper presents the experimental development and evaluation of an autonomous photovoltaic-powered aquaculture system designed for small-scale fish farming in isolated areas. The system integrates renewable energy generation, automated control, and real-time monitoring to ensure energy self-sufficiency and environmental sustainability. The experimental setup, consisting of a 6 kWp photovoltaic array (15 panels of 400 W each), a 24-unit 24 V battery bank, and a diesel generator for emergency backup, was tested at INMA Bucharest over a two-year period. The installation supplies power to essential aquaculture subsystems, including water recirculation, aeration, automatic feeding, lighting, and surveillance. Experimental data showed that the photovoltaic system fully met the average daily energy demand of 3.35 kWh, with hourly peaks of approximately 255 W, maintaining functionality even during winter periods with low solar radiation (0.8–1.0 PSH/day). The hybrid configuration ensured up to 48 hours of energy autonomy and reliable operation under variable climatic conditions. Fish farming under a polyculture regime was also tested, representing an integrated biological mechanism for self-cleaning that enhances the overall sustainability of the aquaculture system. Results demonstrate that autonomous hybrid systems represent a viable solution for sustainable aquaculture, improving energy efficiency, reducing environmental impact, and supporting the viability of small-scale fish farms in remote regions.

To address the increasing demand for potato seed-piece cutting and the persistently low level of mechanization dominated by manual operations, an automatic cutting device was designed and developed based on the physical characteristics and agronomic requirements of seed potatoes. Guided by a design strategy that integrates positional clamping with segmented cutting, the system consists of a clamping–feeding module, a flipping unit, and a PLC-based control system. The clamping mechanism secures the positioned tuber, the flipping unit rotates it by 90°, and the PLC coordinates the sequential operations to achieve precise gripping, orientation adjustment, and quartering. Furthermore, the cutting process was analyzed to identify the key factors affecting the quality and uniformity of seed-piece cutting. The qualified cutting rate and blind-eye rate were selected as evaluation indicators, and a three-factor, three-level response surface experiment was conducted using cutter inclination angle, cutting speed, and clamp width as experimental factors. The results indicated that the optimal parameter combination consisted of a cutter inclination angle of 20.7°, a cutting speed of 0.42 m/s, and a clamp width of 15.5 mm, resulting in a qualified cutting rate of 96.66% and a blind-eye rate of 1.85%. All performance indicators met the operational requirements for potato seed-piece cutting.

The utilization of phytomass derived from energy crops and agricultural residues for bioenergy production has attracted increasing attention in recent years, particularly in the context of rising fossil fuel prices. This study aimed to evaluate the quality indices of phytomass – agricultural residues remaining after seed harvesting – from several Brassicaceae species (Brassica napus (rapeseed), Bunias orientalis (Turkish warty cabbage), Isatis tinctoria (woad), and Sinapis alba (white mustard)) and Poaceae species (Dactylis glomerata (orchard grass), Lolium perenne (perennial ryegrass), and Zea mays (maize)), cultivated in the experimental sector of the “Alexandru Ciubotaru” National Botanical Garden (Institute) of Moldova State University, in order to assess their suitability as feedstock for the production of biogas/biomethane, bioethanol, and solid biofuels (pellets). The results showed that the phytomass collected after seed harvesting from the investigated Brassicaceae and Poaceae species contained 36.30–98.80 g/kg crude protein, 51.00–88.00 g/kg acid detergent lignin, 363.00–424.00 g/kg cellulose, 191.00–299.00 g/kg hemicellulose, and 44.00–71.40 g/kg ash. The analysed phytomass substrates exhibited a carbon-to-nitrogen (C/N) ratio ranging from 29 to 73, with a biochemical biomethane potential of 232–314 L/kg of dry organic matter, while the theoretical ethanol production potential varied between 430 and 523 L/t of organic matter. The solid biofuel produced in the form of pellets showed a net calorific value of 14.55–15.70 MJ/kg, a bulk density of 528–832 kg/m³, and a mechanical durability of 90–97%. Overall, the agricultural residues generated after seed harvesting from the examined Brassicaceae and Poaceae species represent a versatile and promising raw material for renewable energy production in the Republic of Moldova.

To optimize the simulation process of seed pellet coating, this study employs the discrete element method to precisely model and analyze particles, using ice grass seeds as the research subject. The key procedures include constructing a three-dimensional pseudo-ellipsoidal geometric model based on the hyperquadratic surface pseudo-ellipsoid equation and defining it as the mesh division range for the DEM model. The Hertz–Mindlin with JKR contact model was selected to describe inter-particle interactions. A standardized filling sphere addition method for the ellipsoidal model was proposed. Using a central maximum filling sphere with a diameter of 1.2 mm as the baseline, composite models consisting of 17, 9, and 5 spheres were constructed with sphere diameters equal to 0.25, 0.5, and 0.75 times the baseline diameter, respectively. The filling sphere size corresponds to the largest inscribed sphere tangent to the ellipsoid. Through static angle of repose simulation tests, the optimal parameter combination was determined to achieve a target value of 30.54°, resulting in a shear modulus of 1.9 × 107 Pa, a collision restitution coefficient of 0.5 for ice grass seeds, and a filling sphere diameter multiplier of 0.35. Under these conditions, the simulated static angle of repose averaged 30.67°, with a relative error of only 0.43%. Further dynamic calibration tests were conducted using a rotating drum. With a filling rate of 40%, a rotational speed of 58 r/min, and a simulation duration of 10 s, the simulated dynamic angle of repose was 38.12°, exhibiting a relative error of 0.88% compared with the physical test value of 38.46°. These results provide a valuable reference for discrete element modeling and parameter calibration of ellipsoidal particles.

The technological parameters of a device for air purification and disinfection in livestock premises based on the synergistic use of ozonation and ultraviolet (UV) bactericidal radiation are justified. A systematic approach is proposed that ensures high purification efficiency without exceeding permissible ozone concentration limits. An analytical study of the relationships between power input, system performance, bactericidal efficiency, and energy consumption was conducted, and optimal operating modes providing up to 99% air disinfection were identified. The proposed technology is suitable for use in facilities of various purposes, aiming to improve air purification efficiency, reduce energy consumption, and minimize anthropogenic environmental impact.

During straw removal, bale collection, transportation, and stacking are key factors affecting efficiency and labor intensity. Conventional traction-type machines involve fragmented operations and low automation, requiring extensive manual work. To address this, a self-propelled rectangular-bale pickup and stacking machine integrating pickup, conveying, stacking, and unloading was developed. Its structure and working principles were analyzed, emphasizing the innovative pickup, tilting, and hydraulic systems. Field tests showed an operating speed of 15 km/h, pickup efficiency of 410 bales/h, and bale integrity above 98%. The machine significantly improves efficiency, adaptability, and automation, offering a novel solution for intelligent straw removal.

Real-time, high-precision detection of wheat grains is crucial for food security and intelligent management, yet fully supervised methods require extensive annotations and struggle with occlusion and overlap. This paper proposes a lightweight YOLOv8-CoT model based on EfficientTeacher. FasterNet is integrated with CoTAttention to optimize the FC-C2f unit, enhancing channel–spatial feature representation, while a CBAM module is inserted at the end of the neck to improve recognition of occluded and overlapping grains. A pseudo-label self-training strategy is adopted using 80% unlabeled data and 20% labeled samples. The proposed method achieves 91.7% accuracy in field scenarios, improves efficiency by 6.6%, and reduces annotation cost to one-fifth.

Aiming at the problem of poor adaptability and insufficient stability of ridge height caused by hilly terrain, this study designed an adaptive ridge height adjustment system for crawler ridger. By analyzing the process of ridging operation, the mathematical model of ridge height control is established, and the control strategy based on incremental PID is used to dynamically adjust the push rod expansion of hydraulic cylinder in real time, so as to realize the precise adjustment of machine lifting. Combined with the inertial measurement unit, the pitch angle and height information of the vehicle body are collected in real time, and the ridge height is dynamically compensated. The field experiment results show that when the target ridge height is 30 cm and 40 cm, the average standard deviation of ridge height is 0.98 cm and 0.97 cm respectively, and the qualified rate of ridge height is more than 94 %. It shows that the designed ridge height adaptive adjustment system can effectively track the height instruction, and significantly improve the ridge quality and operation stability under complex terrain.

Replacing fossil fuels with renewable energy alternatives has become a major global challenge of the 21st century and a key element of sustainable development. Phytomass has considerable potential as a source of energy and value-added products within the circular economy. This study aimed to evaluate the quality indices of fresh whole-plant biomass harvested during the flowering period and dry stem biomass collected in early spring from the perennial energy crops cup plant (Silphium perfoliatum ‘Vital’) and giant knotweed (Polygonum sachalinense ‘Gigant’) cultivated in the experimental plots of the National Botanical Garden, Chi?inau. The fresh biomass contained 289-375 g/kg DM, with 10.8-11.7% CP, 6.0-8.3% ADL, 31.6-39.3% Cel, 21.3-23.9% HC, and 8.4-11.1% ash, while the biomethane yield ranged from 270 to 310 L/kg VS. The dry stem biomass collected in early spring contained 522-563 g/kg Cel, 234-248 g/kg HC, 110-128 g/kg ADL, and 2.61-4.24% ash, with an estimated theoretical ethanol yield of 558-578 L/t VS. Densified biomass fuels, including briquettes and pellets, exhibited a high calorific value. The local cultivars of perennial energy crops Silphium perfoliatum ‘Vital’ and Polygonum sachalinense ‘Gigant’ represent versatile and promising raw materials for renewable energy production in the Republic of Moldova.

The article focuses on determining the power requirements of the actuator for an original centrifugal pneumatic sieve separator. The research was conducted on a laboratory test bench using the ?50 measuring complex. During the study, the functional relationships between the total power consumption of the separator actuator and the power required for seed movement along the sieve were established as functions of the grain mixture velocity along the sieve (V=11.7–19.6 m/s) and the specific grain mixture feed rate for three crops – wheat, oat, and sunflower (qF=9.6–41.6 kg/m2s). The results showed that the energy intensity of separation for wheat and oat grain mixtures is nearly identical, whereas that for sunflower is lower. At the same time, the influence of the grain mixture velocity on power consumption exceeds the effect of specific feed rate by approximately one order of magnitude. It was determined that the total power consumption of the separator actuator ranges from N = 100–192 W during the separation of wheat and oat grain mixtures and from N = 98–178 W during sunflower separation within the specified velocity and feed rate ranges. The power required for seed movement along the sieve varies from Nn = 7–84 W for wheat and oat grain mixtures and from Nn = 5–70 W for sunflower. In addition, a decrease in the rate of power increase with rising specific feed rate was observed, indicating the presence of interparticle interactions within the material layer during separation.

Wood waste is a growing environmental and economic challenge particularly due to disposal costs and loss of recoverable resources in the European Union, with annual volumes reaching 50 million m³ and projected to rise to 59–67 million m³ by 2030. Wood residues, generated from packaging, construction, demolition, and industrial processes, remain underutilized despite their recycling potential. Contamination and inefficient management hinder sustainable reuse, yet literature highlights opportunities for energy recovery, material substitution, and biochar production as part of circular economy. This review synthesizes current knowledge, emphasizing environmental benefits, resource efficiency, and future directions for optimizing wood waste utilization.

The study examined the distribution and use of tractors and agricultural machinery in Romania between 2014 and 2024 at national, regional, and county levels. Its objective was to identify territorial disparities in technical endowment and assess mechanization in relation to agricultural economic performance. Using standardized statistical data from the National Institute of Statistics, indicators such as technical endowment, mechanization level, economic efficiency, and composite mechanization indices were calculated. Results showed strong contrasts between the well-equipped western regions and the less mechanized eastern and southern areas. Projections to 2035 highlight the role of investment in reducing regional gaps and supporting agricultural modernization and competitiveness.

Nanopriming enhances seed germination and early vigor by delivering micronutrients at the nanoscale level. This study evaluated the size-dependent effects of ZnO (7 nm, 100 nm) and Fe2O3 (4.5 nm, 16.7 nm) nanoparticles (NPs) on sunflower (Helianthus annuus L.) germination dynamics. Seeds were primed with 10–100 µg/mL NPs, and germination was assessed through mean germination time (MGT), germination speed index (GSI), and synchrony (CV). NP type and size, but not concentration, significantly affected MGT and GSI. ZnO 7 nm enhanced speed and uniformity, while Fe2O3 4.5 nm delayed and desynchronized germination. These results show that nanoparticle physicochemical properties critically modulate germination, highlighting ZnO nanopriming for improved seedling emergence.

Peanuts are an important source of oil and feed crops. It is vital to elevate the economy and feed the increasing population. Due to urbanization, the decrease and aging of the workforce threaten the agriculture sector. Consequently, countries around the world are urgently in need of innovation, particularly from an adoptable peanut harvesting technology perspective. As a result, this work focused on reviewing the developments in peanut harvesting, emphasizing the impacts of manual to mechanical harvesting methods. Specifically, mechanical harvesting is affected by soil characteristics, peanut plant characteristics, maturity at harvest, land size, peanut harvesting methods, and the importance of innovating peanut harvesting machinery. This endeavor covers legitimate scientific publications from 2010 to 2023, sustained by online public data from different government agencies. The review ends with the recommendation for standardization of peanut harvester machine specifications and testing methods to easily compare machine performances, aiding farmers, researchers, and industry stakeholders in better decisions when selecting mechanical peanut harvesters. Further, standardizing testing methods could establish operational efficiency and foster innovation and improvement within the industry.

Automated detection of tomato ripeness is crucial for achieving precise harvesting and enhancing agricultural productivity. However, detecting tomatoes in natural scenes poses challenges such as missed detections and false positives due to significant variations in target scale, frequent occlusions, and complex backgrounds. Additionally, existing detection models face limitations when deployed on mobile devices. To address these issues, this paper proposes SFH-YOLOv11, a lightweight detection model based on an improved YOLOv11n. Building upon YOLOv11n, this model achieves lightweight performance while maintaining high accuracy through three key enhancements: introducing an attention mechanism in the backbone network to strengthen feature selection capabilities, designing lightweight convolutional modules to reduce model complexity, and reconstructing the feature pyramid network in the neck to enhance multi-scale feature fusion. Experimental results demonstrate that SFH-YOLOv11 outperforms other algorithms, achieving mAP50 and mAP50-95 scores of 91.8% and 78.2%, respectively—representing improvements of 1.7% and 1.0% over the original model. While enhancing performance, SFH-YOLOv11 reduces the number of parameters, computational complexity, and model size by 37.2%, 15.9%, and 34.5%, respectively, compared to the original model. This research provides effective technical support for lightweight maturity detection tasks in complex agricultural scenarios.

To address the uneven drying issue caused by temperature gradients during the cross-flow drying of paddy, this study designs a grain-turning device that periodically rotates clockwise, based on the cross-flow ventilation drying process of paddy. The device is intended to promote sufficient mixing of paddy grains in the drying section, thereby enhancing the drying uniformity and processing quality of paddy. Based on the thermo-hydro-mechanical (THM) coupling theory, computational fluid dynamics (CFD), and discrete element method (DEM) for particles, two mathematical analytical models of the cross-flow drying section (with and without the grain-turning device) were established. Using the EDEM-Fluent software coupling method, numerical simulations were conducted to investigate the variation patterns of temperature and moisture content of paddy grains in the two drying sections. The results indicate that the drying section equipped with the grain-turning device can effectively resolve the uneven distribution of moisture content and temperature in the grain layer caused by the air supply direction, verifying the feasibility of the device. To further verify its performance, a comparative experiment was carried out under the same parameters (drying duration: 100 min): before the improvement, the average moisture content of paddy in each layer was 15.24%, with a standard deviation of 1.26 and a variance of 1.59; after the improvement, the average moisture content decreased to 14.61%, with a standard deviation of 0.192 and a variance of 0.037. One-way analysis of variance (ANOVA) shows that the inter-group F-statistic reaches 41.536, which is much higher than the critical value (4.41) corresponding to the significance level of 0.05, with p < 0.001.In conclusion, the drying uniformity of paddy with the grain-turning device is significantly superior to that of conventional drying, providing a practical technical reference for the optimization of paddy drying production processes.

To improve fertilization accuracy and uniformity in rice production, a variable-rate fertilization device was designed based on field soil sampling data and model optimization. Ming Shui in Heilongjiang Province, China, was selected as the study area, where soil nutrient data were collected to generate fertilization prescription maps. First, bilinear interpolation was applied to densify the sampling data and enhance spatial continuity, and the interpolation accuracy was evaluated using mean absolute error (MAE = 2.61), root mean square error (RMSE = 4.2), and coefficient of determination (R² = 0.74), supplemented by t-tests and Q–Q plot analysis. The results indicated that the method achieved satisfactory accuracy. Subsequently, fertilization prescription maps were generated using the Kriging method in ArcGIS. Based on the fertilization rate range derived from the prescription map, a variable-rate fertilization device equipped with a fertilizer impeller and an adjustable baffle was designed. EDEM simulations and bench tests were conducted to analyze the effects of impeller speed, forward speed, and baffle angle on fertilization uniformity and fertilizer breakage rate. Response surface analysis showed that all factors had significant effects. The optimal parameters were an impeller speed of 65.6 rad/min, a forward speed of 0.39 m/s, and a baffle angle of 54°, resulting in a fertilizer distribution deviation of 8.54% and a breakage rate of 9.16%. Compared with conventional fluted-wheel or pneumatic fertilization devices, the proposed device features two independently adjustable fertilization structures, improving system stability and making it suitable for precision fertilization in paddy fields.

To mitigate tracking degradation caused by unstable speeds in weeding robots, this study integrates Linear Active Disturbance Rejection Control (LADRC) with the Pure Pursuit (PP) algorithm. An Improved Northern Goshawk Optimization (INGO) algorithm is employed to optimize the LADRC parameters, enabling more precise speed regulation. Field experiments conducted at speeds of 0.5, 0.8, and 1.0 m/s compared the proposed approach with a conventional PID-PP controller. The results demonstrate that the proposed method reduced the maximum lateral tracking error by 9.67%, 19.0%, and 20.5%, respectively, while consistently improving both MAE and RMSE. These findings confirm that the proposed control strategy effectively enhances path tracking stability and precision, thereby improving the autonomous navigation performance of weeding robots.

Aimed at mitigating the elevated leakage rate and multiple rate, as well as unstable plant spacing observed during high-speed (5–8 km/h) operation of potato planters, this study designs the key components of a spoon-type potato seed metering device. The primary configuration and operating principle of the metering device are presented, and the critical components involved in the metering process are subjected to theoretical analysis and structural design. To identify the optimum operating parameters, a three-factor, three-level orthogonal test was performed with seed belt linear speed, seed-spoon opening diameter, and seed-box inclination angle as experimental factors and leakage rate and multiple rate as evaluation indicators, thereby optimizing the high-speed potato seed metering device. The experimental results indicated that, at 5.38 km/h, the device performed best when the seed belt linear speed was 0.42 m/s, the seed-spoon opening diameter was 58.34 mm, and the seed-box inclination angle was 10°, resulting in a leakage rate of 3.68% and a multiple rate of 4.07%.The comparison experiments demonstrated that, at 5.38 km/h, the curved guide plate decreased the leakage rate by 1.31 percentage points, the multiple rate by 0.45 percentage points, and the plant-spacing coefficient of variation by 5.02 percentage points. Field experiments indicated that the high-speed potato seed metering device attained a leakage rate of 4.35%, a multiple rate of 4.72%, and a plant-spacing coefficient of variation of 13.51%, providing a basis for the optimization design of high-speed seed metering devices.

This paper designs a maize interplant weeding machine to address issues like high seedling injury, low efficiency, and excessive tool stress in corn weeding. Using MATLAB, parameter optimization for plant spacing, protection zone radius, and tool radius results in a maximum weeding area coverage of 78.69%. Trajectory analysis improves adaptability in complex environments. A soil trough model based on EDEM identifies key factors affecting tool stress, with weeding depth having the greatest impact. The optimized parameters—plant spacing of 250 mm, weeding depth of 62 mm, and forward speed of 0.25 m/s—achieve 86.06% weeding rate and 2.76% seedling injury.

To improve the accuracy of numerical simulations of the rolling process of giant Juncao stems using the discrete element method, this study focuses on the giant Juncao stems. In conjunction with physical experiments on giant Juncao stems and discrete element simulation methods, the Hertz-Mindlin with Bonding model is selected to establish a bonding model, and the parameters of the discrete element model are calibrated. The relative error between the numerical simulations angle of repose and the angle of repose from physical experiments is used as an evaluation index. The Plackett-Burman test, Steepest Ascent test, and Box-Behnken test are designed to optimize the relevant parameters. The optimal parameter combination obtained includes a static friction coefficient between stems of 0.25, a rolling friction coefficient of 0.42, and a static friction coefficient between the stem and the steel plate of 0.52; the average angle of repose is 18.032°. Validation simulation tests are conducted with this parameter combination, resulting in a relative error of 1.1% between the obtained angle of repose and that from physical experiments. This indicates that the calibrated parameter results for the giant Juncao stems can be used for discrete element numerical simulation of crushing studies.

This study addresses the quality deterioration induced by starch phase transitions during rice drying, which arise from coupled temperature and moisture variations. A mathematical model for intermittent drying is proposed that integrates the glass transition mechanism with a three-dimensional, realistic grain geometric structure. The model was validated using continuous drying (CD) and two-stage intermittent drying (2SID) experiments. The results showed excellent agreement between simulations and experimental data, with correlation coefficients exceeding 0.95. Model analysis revealed that during 2SID at 45 °C, the glassy region rapidly formed from the grain surface inward, accounting for approximately 19.6% of the total grain volume at the end of the first drying stage. After a 20-min tempering period, the internal moisture gradient was effectively reduced, and the glassy region expanded only slightly by about 2.1%. Following the second drying stage, the glassy region increased to approximately 47.9%, while the central layer of the grain remained in the rubbery state throughout the process. Compared with CD, 2SID not only reduced the total drying time by approximately 9.09% but, more importantly, significantly regulated the internal moisture distribution through the tempering stage, thereby helping to mitigate fissure formation and improve head rice yield. The proposed model provides a reliable theoretical framework and predictive tool for optimizing intermittent drying parameters and simultaneously enhancing drying efficiency and paddy rice processing quality.

During field tomato harvesting, challenges such as stem-leaf occlusion, fruit overlap, and difficulties in stem localization significantly hinder the performance of harvesting robots. To address these issues, a joint detection model for fruits and fruit stems, termed YOLO-TRS, is proposed based on the YOLO11n network. First, a novel C3k2-DS module is designed and integrated into the backbone network, enhancing the model’s ability to represent complex structural features of fruit stems. In addition, a CAA module is incorporated into the backbone to improve long-range feature modeling, thereby effectively reducing missed detections of fruits and fruit stems under occlusion conditions. The proposed model is evaluated using a self-constructed dataset. Experimental results show that YOLO-TRS achieves precision, recall, and mAP values of 89.9%, 91.5%, and 94.8%, respectively, outperforming the baseline YOLO11n model by 2.3%, 1.0%, and 2.4%. Compared with other classical object detection algorithms, YOLO-TRS demonstrates clear advantages in both detection accuracy and computational efficiency. These results confirm that the proposed model can effectively support fruit ripeness-related detection and accurately localize stem positions in complex field environments, providing a theoretical basis for intelligent agricultural harvesting.

To address the issue that single-satellite navigation systems are prone to signal occlusion and weakening, resulting in insufficient positioning accuracy when power chassis equipment operates in hilly and mountainous areas, a combined navigation scheme integrating Precise Point Positioning (PPP), Real-Time Kinematic (RTK), and an Inertial Measurement Unit (IMU) was proposed. A simulation model was developed using the PSINS (Precision Strapdown Inertial Navigation System) toolbox within the MATLAB environment. A straightline trajectory was designed to simulate weakly nonlinear operating conditions, while a circular trajectory was used to represent the strong nonlinear continuous-turning conditions. The performance of the Extended Kalman Filter (EKF) was compared with that of the Unscented Kalman Filter (UKF). A combined PPP-RTKIMU navigation test system was constructed, and both field experiments and verification tests were conducted in hilly and mountainous regions. The results showed that, in the circular trajectory simulation, the standard deviations of eastward, northward, and vertical position errors obtained using the UKF were reduced by 22%, 19%, and 18%, respectively, compared with those of the EKF. In field tests, the UKF demonstrated significantly better consistency with the reference values than the EKF. Results from five repeated field verification tests showed that the average maximum absolute lateral position deviation was 10.96 cm, the mean absolute deviation averaged 3.08 cm, and the average standard deviation was 3.02 cm, all meeting operational requirements. Overall, the findings indicate that the UKF is more suitable for strongly nonlinear operating scenarios encountered in hilly and mountainous terrain, and that the proposed combined navigation system effectively mitigates satellite signal occlusion, thereby meeting the precision requirements of modern agricultural machinery operations.