Abstract: 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.