The germination of castor seeds was affected by different damage forms after shelling. Traditional methods could not express the change of mechanical damage characteristics on the surface of castor seeds. In the study, an improved migration learning algorithm for castor seed damage classification was adopted. The convolution kernel size of the first convolutional layer of the AlexNet model was modified, part of the convolutional layer was divided into two layers to increase the depth of the convolution model. Then a multi-scale convolution kernel was added to extract the damage characteristics of castor seeds. The results showed that combined with the hyperparameter optimization of convolutional layer stratification and the AlexNet model,the classification effect was improved. The average test accuracy was 98.10%. After the addition of multi-scale convolution, the average test accuracy was improved by 0.57%. The results show that the classification accuracy of cracked castor seeds is 71%, and the classification accuracy of castor seeds with missing shells is 63%. The classification accuracy of whole castor seeds is 67%. The verification of damage identification device for castor seeds was developed to verify the correctness of the algorithm. This study provided a theoretical and convolutional network model supported for the development of an online real-time damage classification detection system for castor seeds.

To address the issue of the lack of suitable integrated harvesters for the soybean-corn intercropping mode in China, this paper designs a 4DYZ-4/2 model soybean-corn integrated harvester header and develops a reliable integrated header with low loss rates during harvesting. This header integrates functions such as snapping soybean stalks, separating soybean and corn, corn snapping and conveying, corn cob cutting, and soybean stem conveying, with innovative structural adjustments to the overall frame, ensuring efficient harvesting of soybeans and corn while reducing the labor intensity of operators. Based on the characteristics and requirements of the soybean-corn intercropping mode, the operational performance parameters and key parts were optimized. The main design parameters include: a header width of 1400 mm, a divider width of 400 mm, a header row spacing of 450 mm, a reel radius of 550 mm, six snapping rollers, a snap roller speed of 4.8 m/s, and a reel rotational speed of 1314 rpm. Field test results show that the header achieves a soybean loss rate of 1.28% and a corn loss rate of 1.42%. The research results confirm the reliability and practicality of this header design, providing technical support for soybean-corn intercropping integrated harvesting.

Efficient cotton stalk recycling is crucial for agricultural sustainability. Traditional manual removal methods suffer from low efficiency and high labor intensity. Existing mechanical harvesters face limitations such as low removal rates, high stalk breakage rates, and significant omission rates. To address these challenges, this study focuses on optimizing a self-developed multi-row disc-type stalk puller. The conventional flat disc was redesigned into an obliquely angled disc with a bending angle. This 3D curved design enhances stalk constraint capability, thereby improving gripping stability and lifting height. A kinematic model was developed using the ADAMS dynamics simulation platform, revealing the relationships between motion trajectories and key structural parameters. Parametric analysis quantified the effects of disc diameter and bending angle on lifting height and working width. A three-factor, three-level orthogonal experimental design was implemented to evaluate the influence of speed ratio, disc diameter, and disc inclination on three performance indicators: removal rate (Y1), breakage rate (Y2), and omission rate (Y3). The results showed that for Y1 and Y3, the primary influencing factors, in order of importance, were: disc diameter > bending angle > speed ratio; for Y2, the order was: speed ratio > bending angle > disc diameter. The optimal parameter combination was determined to be: 600 mm disc diameter, 20° bending angle, and 0.7 speed ratio. Field tests achieved a pull-out rate of 95.7%, a breakage rate of 3.2%, and an omission rate of 1.1%. These findings provide both theoretical and technical support for enhancing the efficiency of mechanized cotton stalk harvesting.

To address the lack of contact parameters between Corydalis tuber and mechanical, soil interfaces during planting, harvesting, and processing stages, this study calibrated discrete element simulation parameters for Corydalis tuber by combination of simulation tests and physical tests. 3D contour model of Corydalis tuber was obtained via 3D scanning technology, and a multi-sphere bonded particle model of Corydalis tuber was constructed by automatic filling method. Simulation tests were conducted with the restitution coefficient, static friction coefficient, and rolling friction coefficient between Corydalis tuber and Q235 steel, as well as between Corydalis tuber and soil, as independent variables. The rebound height, friction angle, and rolling distance were used as dependent variables. A second-order polynomial fitting method was applied to the experimental results. The actual test values were substituted into the polynomial equations to obtain simulation values for the contact parameters of Corydalis tuber with Q235 steel and soil, and these values were then validated against the experimental results. The findings indicate that the restitution coefficients of Corydalis tuber-Q235 steel and Corydalis tuber-soil were 0.728 and 0.44, respectively. The static friction coefficients of Corydalis tuber-Q235 steel and Corydalis tuber-soil were 0.41 and 0.76, respectively, while the rolling friction coefficients were 0.02 and 0.033, respectively. Under these conditions, the relative error between the simulation tests and physical tests was minimized. This study provides particle models and calibrated simulation contact parameters for mechanical processing tasks such as sowing, harvesting, and drying of Corydalis tuber.

To address the challenges of complex farmland environment, an intelligent obstacle avoidance control algorithm for agricultural unmanned aerial vehicles (UAVs) is developed. The objective is to solve the problem of efficient obstacle avoidance in farmland scenarios characterized by dense dynamic obstacles and variable terrain. In this article, a target detection algorithm based on improved YOLOv5 (You Only Look Once v5) is proposed, and an intelligent obstacle avoidance system is constructed by combining reinforcement learning path planning and adaptive motion control strategy. Ghost module is introduced to improve the lightweight of YOLOv5, and the design of CIOU (Complete Intersection Over Union) loss function is optimized, which improves the detection accuracy of the model for small targets and dynamic obstacles. Experiments show that the error of path planning is reduced to less than 2.1 meters, and the time consumption is reduced by about 35%. In addition, fuzzy logic controller is used to realize adaptive PID control, which further enhances the flight stability of UAV in complex environment. The results show that the improved algorithm has excellent performance in many typical farmland scenes. This study provides theoretical and technical support for autonomous flight of agricultural UAV in complex farmland environment.

To address the Autonomous navigation and operation requirements of agricultural robots in complex terrain environments of vineyards, this report proposes a point cloud Ground segmentation algorithm based on surface fitting, aiming to solve the problems of reduced segmentation accuracy and insufficient adaptability of traditional planar assumption methods in unstructured terrains such as sloped fields and ridge furrows. The core of the algorithm lies in adopting a point cloud representation method based on a non-uniform polar grid, which dynamically allocates face element sizes according to point cloud density and the width between vineyard ridges, effectively addressing the issues of point cloud sparsity and representability. Subsequently, the moving least square method is used to fit surface models. During the fitting process, strategies such as Gaussian weight function, cosine and sine basis functions, and set of orthogonal functions are introduced to shorten the algorithm’s running time and reduce computational complexity. The algorithm’s performance is evaluated on the public dataset KITTI and in real-world environments, and compared with algorithms such as RANSAC, GPF, R-GPF, and Patchwork. Experimental results show that the proposed algorithm outperforms other algorithms in both Precision and Recall. In practical environments, the algorithm can accurately and effectively segment complex vineyard environments, meeting the operational requirements of agricultural robots and providing technical support for the advancement of smart agriculture.

Accurate detection of apple stems is crucial for robotic cutting. This study proposed an improved YOLOv8-stem method for apple stem detection in overhead imagery under occlusion conditions. First, several im-provements were made to the YOLOv8 neural network: the conventional convolutional process within the in-termediate neck layer was substituted with the AK Convolution mechanism, a small object detection head was added, and ResBlock+CBAM attention mechanism was incorporated. Second, stem occlusion was determined by analyzing the positional relationship between the detected bounding boxes of stems and apples. The exper-imental results showed that compared to the original YOLOv8, this method improved apple stem detection ac-curacy by 6.0% (from 79.9% to 85.9%) and increased harvesting completeness from 84.2% to 93.2%.

To solve the problems of low precision in locating stem picking points and difficulty in recognizing occluded strawberry during the operation of strawberry picking robots, this paper proposed an improved YOLOv8-pose method for strawberry fruits identification and key points detection at the red ripe stage. Based on the YOLOv8-pose human posture estimation model, three categories (strawberry, stem, and picking points) were annotated. The acquired images were divided into training, validation, and test sets in an 8:1:1 ratio. In order to improve the feature extraction ability of the model for small targets, shuffle attention (SA) mechanism was added into the backbone network of YOLOv8-pose. Additionally, a comparative analysis was conducted to assess the impact of six attention mechanisms of CBAM (Convolutional block attention module), SimAM (Simple attention module), GAM (Global attention module), EMA (Efficient multi-scale attention), SK (Selective kernel attention), and SA on the detection results. Experimental results show that the proposed method can quickly and accurately detect strawberry fruits and key points for picking. The Precision (P), Recall (R), and mean average precision (mAP)50 values for both bounding boxes and key points based on SA mechanism were 99.7%, 100.0%, and 99.5% respectively, which were superior to the other attention mechanisms. Compared with YOLOv5-pose and YOLOv8-pose models, the improved model had the best P, R, and mAP50 values, and its memory usage was 6.4MB, which was also optimal. The improved method can provide crucial technical support for precise robotic strawberry picking.

The influence of the devices structure and operating parameters, along with the material properties of millet, on threshing and separation performance forms the theoretical basis for designing and researching a single longitudinal axial flow threshing and separation device specifically adapted to millet. Therefore, a theoretical model for grain threshing and separation in a single longitudinal axial flow threshing device was established based on variable mass theory. To validate the theoretical model, single-factor tests were conducted on the feeding rate, rotational speed, and water content of Longgu 31 millet. The error analysis between the experimental and calculated values indicates that within a moisture content range of 17.14% to 32.93%, feeding rates varying from 1 to 3 kg/s, and rotational speeds ranging from 700 to 1000 r/min, the R-squared values consistently exceed 0.97. This indicates an excellent fit of the theoretical model. The theoretical model will serve as a valuable reference for the design and investigation of the single longitudinal axial flow separation device.

Crop diseases significantly diminish agricultural production, resulting in economic losses. Early detection and species identification remain major challenges. This paper introduces a lightweight Convolutional Neural Network (LCNet) designed for the detection of corn diseases, including blight, common rust, and gray leaf spot, using an efficient, low-latency model. The suggested architecture consists of three convolutional layers, three pooling layers, and one fully linked layer. Experimental findings indicate that LCNet surpasses the pretrained architecture MobileNetV2, DenseNet201, and ResNet50, with an average accuracy of 94.65%. This method enables prompt disease identification, assisting farmers in averting significant crop losses while minimizing human labour in oversight and administration.

To improve the seed separation uniformity in the seed storage device of a wheat plot seeder, this paper investigates the key factors influencing its performance. First, the working principle of the seed storage device and the movement of seeds are analyzed, and the lifting mechanism of the seed storage tube is controlled by an electric pusher. Based on this analysis, the relevant parameter factors are identified. Next, EDEM software is used to simulate the seed storage process and determine the optimal combination of parameters. A three-factor, three-level central composite design experiment is conducted, with the inner diameter of the seed storage tube (D), the lifting height (L), and the lifting speed (V) as the test factors. The coefficient of variation of seed distribution uniformity (m1) is used as the performance index. The optimal values determined for the parameters are D = 0.06 m, L = 0.015 m, and V = 0.05 m/s. Finally, bench experiments are carried out to verify the results. The experimental findings show that the average coefficient of variation for seed distribution using the traditional lifting lever is 6.46%, while the optimized parameters reduce it to 4.12%, significantly improving seed separation uniformity and meeting the seeding requirements.

This study is based on the discrete element method to design a tracked precision solid particle pesticide applicator and establish a coupling model between the pesticide application device and particle swarm EDEM. Simulation shows that particle characteristics significantly affect the uniformity of pesticide application. The coefficients of variation for particles 1, 2, and 3 are 4.25%, 4.63%, and 5.57%, respectively, with relative deviations of 8.51%, 9.28%, and 11.33%. After response surface testing optimization, the measured coefficient of variation of the prototype was 8.25% and the relative deviation was 18.07%, both of which met industry standards.

Traditional rice seed classification methods rely on manual observation of morphological features, which are inefficient and limited in accuracy. To improve the efficiency and accuracy of rice seed classification, this paper proposes a deep learning-based rice seed classification method using the SE-ResNet network architecture. This architecture integrates SENet into ResNet, enabling the model to capture and learn sensitive differential features among rice seeds. Through comparative experiments, the classification accuracies of SE-ResNet, ResNet, and AlexNet on the rice seed dataset were 89.58%, 72.97%, and 76.35%, respectively. The results demonstrate that SE-ResNet significantly outperforms ResNet and AlexNet in classification accuracy, validating its superiority in rice seed classification tasks.

To improve the uniformity and stability of seed guidance in ordered seed flow and meet the requirements of precision sowing operations, a brush-type seed guiding device capable of constraining all degrees of freedom of the seeds was designed. Bench tests were conducted to optimize the factors affecting performance, namely the nut-bolt head spacing, the brush-to-housing gap, and the brush belt speed. Through single-factor experiments, the influence of each factor on seed guidance performance was clarified, and reasonable value ranges were determined. Combined with a response surface test, a multiple quadratic regression model was established to describe the relationship between these factors and the variation coefficients of pass rate, missed seeding rate, and seed spacing. The results show that optimal seed guidance performance is achieved when the nut-bolt head spacing is 39.15 mm, the brush-to-housing gap is 0.22 mm, and the brush belt linear velocity is 0.644 m/s. This study provides a theoretical foundation and data support for the development of precision sowing technology and its associated seed guiding devices.

The diamondback moth (Plutella xylostella) is a destructive pest that severely compromises Chinese cabbage production. Infestations caused by this pest significantly reduce both yield and quality, making efficient and accurate detection crucial for cultivation management. To address the challenges of detecting small targets and extracting phenotypic features in complex environments, this study proposes SAFF-YOLO—a YOLO11-based pest detection algorithm specifically designed for diamondback moths in Chinese cabbage fields. First, the loss function was refined to enhance the models learning capacity for pest samples, optimizing it for precision-driven bounding box regression. Second, Alterable Kernel Convolution (AKConv) was incorporated into the backbone network, strengthening feature extraction capabilities while reducing model parameters. Third, Single-Head Self-Attention (SHSA) was integrated into the C2PSA (Channel and Position Spatial Attention) module, enhancing the backbone networks feature processing efficacy. Fourth, the neck network employed Frequency-aware Feature Fusion (FreqFusion) as the upsampling operator, specifically designed for precise localization of densely distributed targets. Finally, the Feature Auxiliary Fusion Single-Stage Head (FASFFHead) detection module was implemented to boost multi-scale target detection adaptability. Experimental results demonstrate that SAFF-YOLO achieved detection metrics of 90.7% precision, 89.4% recall, and 92.4% mAP50 for diamondback moths in Chinese cabbage, representing improvements of 7.4%, 8.0%, and 8.4% respectively over YOLO11. With only 7.3 million parameters and computational cost of 12.8 GFLOPs, this corresponds to 60.1% and 40.7% reductions compared to the baseline model. These results confirm an optimal balance between model lightweighting and high detection accuracy. Under complex field conditions characterized by small and densely distributed targets, severe background interference, and intense illumination, SAFF-YOLO consistently demonstrates robust detection capabilities, effectively reducing both false negative and false positive rates while maintaining high operational robustness. This research provides a practical solution for real-time diamondback moth detection in field-grown Chinese cabbage.

This study investigates the excessive energy consumption of traditional furrowing cutters used in sticky, heavy soils of orchards located in hilly and mountainous regions. A novel furrowing cutter was designed, taking the wetland rotary bending cutter as a reference. The bending radius, bending angle, and alpha angle were selected as design variables. Quadratic orthogonal regression and discrete element method (DEM) simulations were employed to analyse tool–soil dynamic interactions and assess the influence of key parameters on furrowing power consumption. Response surface analysis revealed that at a bending radius of 30.02 mm, bending angle of 109.97°, and alpha angle of 51.74°, the optimised cutter reduced power consumption by 12.9%, from 0.85 kW to 0.74 kW.

Tiger-nut is widely cultivated in sandy soils across China, and the mechanization level for harvesting remains low. Harvesters are mainly medium- and small-sized tractor-drawn models, which exhibit weak driving capability, a large turning radius, and poor stability. This paper presents the design of a crawler-type tiger-nut harvester hydraulic-driven travel system, with the required hydraulic motor displacement of 44.65 mL·r-1 and hydraulic pump displacement of 44.58 mL·r-1. Experimental results from the harvester showed that the harvester’s linear travel deviation rate ranged from 1.19% to 2.10%, with an average travel speed of 1.08 m·s-1 and the average value of radius for forward left turn and forward right turn is 2740 mm and 2748 mm. The harvester can achieve bidirectional stepless speed regulation in the field, ensuring stable harvesting performance and meeting the design requirements for the tiger-nut harvester.

To address the low recognition accuracy and slow detection speed of cotton leaf pests and diseases in natural environments, a detection method based on an improved YOLOv5s model was proposed. The enhanced model integrates the Ghost module and the C3Faster module to increase inference speed and reduce model complexity, achieving lightweight performance without significantly compromising accuracy. To counteract the tendency of common cotton pest and disease features to be lost in complex natural scenes, a Coordinate Attention (CA) mechanism was introduced to improve the networks recognition and localization capabilities. The parameters, FLOPs, and weight file size of the improved model were reduced to 65.5%, 66.2%, and 67.1% of those of the original YOLOv5s model, respectively. On a self-built dataset, the improved YOLOv5s model achieved a mean average precision (mAP) improvement of 10.5%, 0.2%, and 0.4% compared to YOLOv4, YOLOv5s, and YOLOv7, respectively. The model was deployed on a Jetson Orin NX development board with CUDA acceleration, achieving a real-time detection speed of 76.3 frames per second.

To address the issue that existing seed-guiding devices struggle to meet the high-speed operational requirements of delta-row planters for dense maize planting, a seed-guiding device with air assistance and a guided groove was designed based on the principle of the brachistochrone. The overall structure and working principle of the device are described, and the curved segment of the seed guide tube was optimized using the brachistochrone principle while accounting for frictional effects. Computational fluid dynamics (CFD) simulations were conducted to analyse the flow field characteristics of the seed guide tube at inlet airflow velocities of 63.48, 60.64, 57.73, 54.69, 51.50, and 48.15 m/s. A multi-factor test was performed using chamber pressure and operating speed as test factors, with the qualified index of grain spacing and the coefficient of variation as evaluation metrics. Comparative tests were conducted using a traditional guided-groove seed guide tube and a brachistochrone-based seed guide tube without a guided groove. Results showed that the optimal parameter combination for the newly designed device was a chamber pressure of 3.124 kPa and an operating speed of 12.0 km/h. Under these conditions in the bench test, the qualified index reached 97.04%, and the coefficient of variation was 6.18%, outperforming the other two types of seed-guiding devices. These findings demonstrate that the seed-guiding device based on the brachistochrone principle can significantly improve the seeding quality of delta-row planters for dense maize planting under high-speed operation.

To address the lack of simulation parameters when applying discrete element simulations to guide the optimal design of grapevine burying machines, this study focused on the soil conditions in Xinjiang. The soil was divided into three layers, and the static and dynamic angle of repose characteristics of each layer were investigated using the Hertz-Mindlin with Johnson-Kendall-Roberts (JKR) contact model. First, the Plackett-Burman test was used to eliminate parameters that had no significant effect on the static and dynamic angles of repose. Then, the steepest ascent test was applied to narrow the parameter ranges, followed by the Box-Behnken test to develop regression models between the repose angles and the significant parameters. The results showed that the soil-soil restitution coefficient, soil-soil rolling friction coefficient, soil-steel static friction coefficient, and JKR surface energy were the key parameters influencing the static and dynamic repose angles. Experimental validation demonstrated that the average relative errors between the simulated and measured angles of repose using the optimized parameters were 0.92% and 0.32%, respectively, confirming the validity of the selected parameters. This study provides an important reference for the design optimization of grapevine burying machines and for discrete element simulation of other cohesive particulate materials.

Solid-liquid separation is essential for utilizing livestock and poultry manure, impacting greenhouse gas emissions and pollution control. This study aimed to address the high water content and low efficiency issues in screw extrusion solid-liquid separation technology. A screw separator was designed, and a Box-Behnken test scheme was used to optimize parameters with dairy manure as the test object. The study identified extender length, screw rotation speed, and counterweight mass as the influencing factors, with extrudate water content and solids extraction rate as evaluation indices. A quadratic regression model was established to describe the relationships between the influencing factors and evaluation indices. Optimal parameters were determined as: extender length=10 cm, screw rotation speed=55 r/min, and counterweight mass=34.73 kg. The predicted extrudate water content was 65.44 %, and solids extraction rate was 2.39 m³/h. Validation tests showed an average extrudate water content of 67.71 % and solids extraction rate of 2.10 m³/h, with relative errors of 3.3 % and 9.6 % respectively. The models reliability was confirmed, providing a reference for designing livestock and poultry manure screw extrusion separators.

For the automatic control of soybean harvester header height, this study uses Area array LiDAR for header height detection. An improved quartile range algorithm is used to dynamically remove outliers under crop residue interference. Linear, quadratic, and cubic nonlinear terrain fitting models are established based on the surface undulation characteristics of soybean fields. The Huber loss function is introduced to enhance the robustness of parameter estimation. The balance between model complexity and fitting goodness is quantified using Bayesian information criterion (BIC), and the model intercept term with the smallest BIC value is selected as the terrain reference height. Aiming at the hysteresis characteristics of valve controlled asymmetric hydraulic cylinders, a telescopic dual-mode transfer function model is established, and a Bang Bang switch lead compensation strategy with position threshold is proposed. By predicting the trend of terrain changes, the electromagnetic directional valve is triggered in advance when the height error of the header exceeds the set threshold, effectively reducing the system response delay. Field comparative experiments have shown that at a working speed of 1m/s, the automatic control mode significantly improves the uniformity of cutting compared to the manual mode. When the cutting threshold is set to 20, 25, and 30mm, the coefficient of variation of cutting height is reduced by 2.13%, 1.71%, and 0.55%, respectively. Moreover, the automatic mode maintains a gentle distribution characteristic within the threshold range of 15-35mm, verifying the strong robustness and control accuracy advantages of the designed system in complex farmland environments.

In the context of agricultural emissions in China, it is common for fields to be bordered by row windbreaks, which - when located downwind of emission sources - can complicate gas flux measurements. To address this challenge, the environmental adaptability of a laser-based detection system for quantifying gas emissions from agricultural sources was evaluated through a controlled gas emission field simulation experiment. Using methane as a representative gas, a flux measurement system based on open-path laser absorption spectroscopy was developed. The study employed an artificially simulated methane volatilization source and two measurement devices to conduct experiments under three conditions: an ideal environment, a laser path positioned downwind of the source, and a laser path set directly above the source. Results show that the standard deviations of the ratio QbLS/Q were 0.0277, 0.0283, and 0.0256, respectively. The corresponding maximum fluctuation amplitudes were 7.3%, 7.4%, and 5.9%. These findings suggest that for a row windbreak located downwind of an emission source, selecting an optimal measurement strategy - such as positioning the optical path above, across, or near the vertical downwind axis of the source - can minimize environmental interference and enhance the reliability of methane flux measurements in agricultural settings.

Due to their delicate and thin skin, ‘huangguan’ pears are very vulnerable to pressure and impact during picking, packing and transportation, which can cause bruising. Early detection of bruises allows for timely identification of affected fruits to reduce potential food safety risks. However, early bruises in ‘huangguan’ pears, particularly those that occur within the 30 minutes, often show no visible differences in external features compared to healthy tissue, making conventional techniques such as manual and machine vision sorting ineffective. Accordingly, a near-infrared (NIR) camera imaging technique combined with deep learning segmentation algorithm for early bruise ‘huangguan’ pears detection is proposed in this study. Firstly, a near-infrared camera imaging system is applied to collect early bruise images of ‘huangguan’ pears, and then a lightweight segmentation model based on the DeepLabV3+ architecture, referred to as MCC-DeepLabV3+ is presented. In the MCC-DeepLabV3+ model, MobileNetV2 is used as the backbone network, reducing the parameter size and enhancing deployment efficiency. Additionally, the coordinate attention (CA) mechanism is integrated into the shallow feature extraction and ASPP modules to improve the extraction of positional information across various features, minimizing the discrepancy between segmented areas and the actual bruised regions. Furthermore, a cascade feature fusion (CFF) strategy is incorporated into the encoder to reduce segmentation edge discontinuities and ensure effective multi-level semantic fusion, improving segmentation accuracy. The experimental results show that the proposed model has achieved a mIoU of 95.68%, and mPrecision of 97.58% on the self-built dataset of early bruising in ‘huangguan’ pears. Compared to benchmark models such as U-Net, SegNet, PSPNet and HRNet, the proposed model demonstrates superior segmentation performance, offering promising support for the development of nondestructive detection techniques for agricultural product quality.