DESIGN AND TESTING OF AN AUTOMATIC CONTROL SYSTEM FOR TOPSOIL STRIPPING OF FRITILLARIA USSURIENSIS MAXIM. BASED ON MACHINE VISION

In this study, a machine-vision-based automatic control system for the topsoil stripping of Fritillaria ussuriensis Maxim. (FUM) was designed to address the problems of manual adjustment, low control accuracy, and response lag in stripping-depth control during FUM harvesting. An improved YOLOv5s-SA target detection algorithm was used to calculate FUM density and was deployed on the Jetson Nano edge-computing platform. Combined with a fuzzy control algorithm, it drives the servo electric cylinder to achieve dynamic depth adjustment of the scraping board. Test results showed that, after deploying the target detection algorithm on the edge AI device and accelerating it with TensorRT, the average inference time was 0.077 s, and the system response time was 0.26 s, meeting the real-time requirements of agricultural operations. Simulation results indicated that the average error between the stripping depth of the automatic control system and the preset depth was 3.72 mm, representing a 44.1% improvement compared with fixed-depth control. The average ideal stripping rate reached 54.96%, an improvement of 21.66% over the 33.3% achieved under fixed-depth control.