The high clearance spray is a type of large and efficient agricultural machinery used for plant protection, and path tracking control is the key to ensure the efficient and safe operation of spray. Sliding mode control and other methods are commonly used abroad to track vehicles, while fuzzy control, neural networks and other methods are commonly used at home. However, domestic and foreign research on autonomous agricultural machinery is mainly focused on tractors and other machinery, while research on self-propelled spray in high clearance is less abundant. This paper takes the path tracking algorithm in the integrated navigation system of spray as the main research goal, studies the path tracking control algorithm for straight lines and turning curves that can realize the automatic driving of spray by establishing the path tracking algorithm for unmanned spray based on dual control strategies, designs the path tracking controller, including the preview model theoretical path tracking controller and variable domain fuzzy controller, and determines the preview model through the design of the preview model theoretical path tracking controller. The lateral and longitudinal errors of the model algorithm are analyzed, and the driving characteristics under the complex spray road surface are analyzed. The design of the variable domain fuzzy predictor theory path tracking controller is proposed, and the design of the road model selection controller is calculated and analyzed in detail, including the determination of the road roughness coefficient and the selection of the range of the difference between the average value of the excitation before and after sampling, which improves the performance of the spray path tracking algorithm. The experiment shows that the proposed path tracking control algorithm can meet the path tracking requirements of unmanned spray in the current road environment, and provide a reliable solution for the automatic control of high clearance spray.

Unmanned aerial vehicle (UAV) imagery provides an efficient approach for monitoring cotton defoliation and boll-opening rates. Deep learning, particularly convolutional neural networks (CNNs), has been widely applied in image processing and agricultural monitoring, achieving strong performance in tasks such as disease detection, weed recognition, and yield prediction. However, existing models often suffer from heavy computational costs and slow inference speed, limiting their real-time deployment in agricultural fields. To address this challenge, we propose a lightweight cotton maturity recognition model, RTCMNet (Real-time Cotton Monitoring Network). By incorporating a multi-scale convolutional attention (MSCA) module and an efficient feature fusion strategy, RTCMNet achieves high accuracy with substantially reduced computational complexity. A UAV dataset was constructed using images collected in Xinjiang, and the proposed model was benchmarked against several state-of-the-art networks. Experimental results demonstrate that RTCMNet achieves 0.96 and 0.92 accuracy on defoliation rate and boll-opening rate classification tasks, respectively. Meanwhile, it contains only 0.35 M parameters—94% fewer than DenseNet121—and only requires an inference time of 33 ms, representing a 97% reduction compared to DenseNet121. Field tests further confirm its real-time performance and robustness on UAV platforms. Overall, RTCMNet provides an efficient and low-cost solution for UAV-based cotton maturity monitoring, supporting the advancement of precision agriculture.

This study investigates a duckbill-type hole seeder to elucidate the kinematic and force characteristics of hole formation under the dry sowing–wet emergence regime and to provide theoretical support for the optimization of key structural parameters. A bidirectional coupling simulation model based on the discrete element method (DEM) and multibody dynamics (MBD) was established to analyze the motion trajectories of the fixed and movable duckbills, the evolution of three-directional forces, and the associated soil–plastic film disturbance under different combinations of front and rear angles. The results indicate that soil disturbance during hole formation is dominated by vertical penetration and uplift, accompanied by forward cutting and lateral redistribution. The three-directional forces acting on the fixed duckbill exhibit a non-monotonic response with respect to the front angle, decreasing first and then increasing, while the force level during the expansion stage of the movable duckbill generally increases with the rear angle. Within the investigated parameter range, a front angle of 18° combined with a rear angle of 38° resulted in a relatively lower overall force level during penetration and expansion, which is favorable for stable hole formation. Field experiments conducted with this configuration showed an average seed placement deviation of 0.50 cm, satisfying the requirements for precision cotton planting under plastic mulch. The findings provide theoretical insight and methodological support for the structural optimization and engineering design of cotton hole seeders operating under the dry sowing–wet emergence regime.

The aim of this study is to improve the film removal rate of a film removal device on a residual film recovery machine and mitigate the soil compaction caused by film removal operations during the residual film recovery process. We designed a bionic film-lifting shovel by applying the contour curve of the first claw of the North China mole cricket’s front foot to the soil-penetrating portion of the film-lifting tines. Based on agronomic requirements and mechanical analysis of the operation process, the biomimetic blade was developed to break up soil more effectively and lift the residual film more efficiently. The contour features were obtained using high-definition cameras, with the fitting equation guiding the design of the soil-penetrating structure. A three-dimensional model was constructed using SolidWorks. Tensile tests provided the physical parameters of the autumn residual film, enabling the creation of a finite element model using the Mohr–Coulomb yield criterion. Simulation comparisons showed that the biomimetic shovel teeth reduced the operating resistance by 9.3% compared to conventional teeth. Soil trench experiments validated these results, demonstrating a 4.24% higher film-lifting rate and average resistance of 411.49 N for the bionic shovel versus 454.70 N for the conventional one. The close match between the experimental and simulation results confirms the effectiveness of the bionic design in meeting the resistance reduction requirements.

This paper presents a rubber strip and spring tine residual cotton pickup device integrating picking, cleaning and conveying functions, developed to tackle the problems of severe waste of residual cotton, low picking efficiency and high impurity content of existing equipment after mechanized cotton harvesting in Xinjiang. Structural parameters of key components, including the pickup mechanism and sawtooth cleaning unit, were designed using dynamic analysis, and the dynamic processes of picking, cleaning and cotton detachment were analyzed to reveal the working principle of the device. A prototype was tested with a three-factor, three-level Box–Behnken design, taking operating speed, pickup drum speed and belt spacing as experimental factors and pickup rate and impurity content as performance indicators. Multi-objective optimization via Design Expert 10.0.4 yielded optimal parameters of 0.67 m/s operating speed, 119.87 mm belt spacing and 251.19 r/min pickup unit speed, achieving a pickup rate of 90.20% and impurity content of 18.27%, with relative errors of 0.74% and 2.4% in verification tests respectively. The results provide technical support for mechanized residual cotton harvesting technology and equipment and offer a new approach for the research and development of picking methods and devices for irregular lumpy flexible materials.