Design, development, and testing of a cassava storage root-cutting robot utilizing a Stewart platform and mask R-CNN for precision agriculture

摘要

• Developed a Cassava Storage Root-Cutting Robot (CSRCR) with a Stewart platform for precision harvesting. • Integrated Mask R-CNN for accurate detection and alignment of cassava storage root cutting. • Optimized cutting performance with ELA speed of 22 mm/sec and CU rotational speed of 882 rpm. • Achieved an average cutting alignment error of only 0.65 degrees in roll and pitch directions. • Demonstrated significant reduction in harvesting loss (1.44%), trash (0.66%), and cutting time (4.05 sec/stem). Separating cassava storage root from its stem, known as cassava storage root cutting, represents a pivotal stage in cassava harvesting. It has become increasingly challenging due to a shortage of skilled labor. This research introduces an innovative solution: a cassava storage root-cutting robot (CSRCR) utilizing computer vision technology. The Mask-RCNN model is employed for precise cutting alignment detection. The moving mechanism utilizes a Stewart platform, and the cutting action is performed by a cylinder saw integrated into the robot. The specifications of these components, including dimensions, load capacity, and speed, were meticulously defined and calculated based on a physical survey of cassava plants. The robot's performance was evaluated through a three-step process. First, motion performance was assessed, and the results demonstrated acceptable levels of accuracy, repeatability, and workspace. Second, the optimal moving speed and the cutter's speed were determined. In the third step, the robot was integrated with computer vision technology. The integration achieved a remarkable success rate of 100%. The average loss and trash were minimized to 1.44% and 0.66%, respectively, and the cycle time was 32.43 seconds. This successful integration not only demonstrates the robot's ability to cut cassava stems accurately in various orientations but also significantly improves efficiency by reducing loss and trash. The research findings pave the way for enhanced traditional cassava harvesting practices.