This study addressed several significant challenges encountered in the visual calibration of RGBD cameras and Delta parallel robots used in cotton topping devices. Specifically, it tackled the issues of the separation between the camera's field-of-view and the operational space, as well as the problem of insufficient calibration accuracy. An innovative approach known as Disjoint Area Visual Calibration (DAHEC) was proposed to address these issues effectively. The study explored the principles and procedures of visual calibration technology and, based on cotton planting patterns and topping requirements, selected an eye-to-hand visual calibration method. The DAHEC method was specifically developed to handle situations where the camera's field-of-view is separate from the operational space. By leveraging the principles of projective geometry and least-squares estimation, the DAHEC method simplifies the calibration process and enhances accuracy. An experimental setup was constructed to integrate the RGBD camera with a Delta parallel robot mounted on a conveyor belt, establishing a comprehensive visual relationship system. A visualization program was developed using Python and OpenCV, and comparative experiments were conducted against the traditional TSAI visual calibration method. Detailed statistical analysis was performed on average positioning errors, dispersion characteristics, and offset errors from these comparative experiments. The results indicated that the DAHEC method achieved an offset error of (4.72±0.86) mm, whereas the TSAI method had an offset error of (7.97±1.46) mm, demonstrating a clear advantage of the DAHEC method over TSAI. To further optimize the calibration process, a three-factor experiment was designed using Box-Behnken design theory, with lighting intensity, arm cumulative movement, and camera calibration board distance as experimental factors. The main objective was to ensure that the offset error remained within the acceptable range of the disk knife radius. Orthogonal tests were conducted to analyze the impact of these factors on offset error and determine the optimal working parameters for the cotton topping knife. The results revealed that the most significant factors affecting offset error were the camera calibration board distance, arm cumulative movement, and lighting intensity. The optimal working parameters were identified as a lighting intensity of 800 lux, arm cumulative movement of 99 times, and a camera calibration board distance (distance from the RGBD camera to the cotton top bud) of 300-560 mm. Under these optimized parameters, the topping verification tests showed an average offset error of 9.76 mm, which fell within the acceptable range of the disk knife radius. The topping rate was 93.75%, while the missed topping rate was 6.25%. The average time per topping was 1 800 ms, meeting the stringent requirements for efficient and precise topping. This research not only validated the effectiveness of the new DAHEC method through comparative and orthogonal experiments but also provided a scientific basis for setting working parameters in the practical application of cotton topping devices. The findings are of significant practical importance for advancing agricultural mechanization and automation, thereby enhancing agricultural productivity and crop yields. Future research will focus on exploring adaptive calibration technologies to accommodate varying environmental conditions and extending this method to other robotic applications in agriculture. By further refining and validating these methods, researchers aim to improve the reliability and applicability of robotic systems in agricultural tasks, ultimately contributing to sustainable agricultural practices and global food security.

Chopped straws can help replenish soil nutrients, improve soil structure, and increase the amount of organic matter contained in soil. The grinding ability of crop straws is influenced by the frictional characteristics of the materials involved in the grinding process. Studying the frictional properties of peanut stem, residual film, and external contact material is essential to understanding the grinding action of peanuts. This study discussed the frictional properties between films and between residual film and external contact materials. A physical test was conducted using a friction coefficient detector. The results showed that the average value of the dynamic sliding friction coefficient (f_k) was 0.34, the average value of the static sliding friction coefficient (f_s) between the film and the 40Cr steel plate (as the external contact material) was 0.38, and the average f_s value between the films and between the residual film and the external contact material was 0.36. Based on the Box-Behnken test, second-order response models were established for the static rolling stability angle (μ_e) and the static sliding friction coefficient (f_s). On the basis of establishing the static rolling stability angle (μ_e) and static sliding friction coefficient (f_s) of the evaluation index, the different friction characteristics between straw and external contact materials were investigated under varying moisture content, external contact materials and particle sizes. The study results can provide a basis for the development of equipment that can be used for peanut straw crushing and membrane separation.

This study constructed a numerical model using the discrete element software EDEM to address the current lack of calibrated contact parameters for peanut seedling membranes and the absence of precise simulation model parameters for mechanized separation. The Hysteretic Spring Contact Model (HSCM) was employed to calibrate the contact parameters of peanut seedling membranes. The angle of repose of peanut seedling membranes was determined through image processing combined with the least squares method. Through central composite design (CCD), a second-order response model linking the contact parameters to the angle of repose was established. Optimization was achieved by using the angle of repose obtained from physical tests as the objective. Secondary simulation tests were conducted with the calibrated parameters, revealing a relative error of 1.37% between the simulated and physical angles of repose. This confirmed the effectiveness of the parameters in calibrating peanut seedling membrane characteristics. The findings offer theoretical and empirical support for discrete element simulations of peanut seedling membrane separation and peanut straw pulverization processes.