基于YOLOv7-tiny模型的马铃薯播种机漏重播检测系统的设计与试验

Abstract

In response to the issues of missed and repeated planting during the operation of the chain-spoon type potato planter in China, as well as the low recognition rate for missed planting and the difficulty in identifying repeated planting using existing detection methods, an innovative Potato Planter Missed and Repeated Planting Detection System has been designed. This system is built with a PLC as the lower-level controller and an industrial computer as the core, incorporating the YOLO object detection algorithm for detecting missed and repeated plantings during the operation of the potato planter. Using the YOLOv7-tiny object detection network model as the core, and combining model training with hardware integration, the system performs real-time detection of the potato seed situation within the seed spoon during the operation of the potato planter. It can quickly distinguish between normal planting, missed planting, and repeated planting scenarios. By incorporating the working principles of the planter, the system designs a positioning logic to identify the actual coordinates of missed and repeated planting locations when a lack or excess of planting is detected. This is achieved through the positioning module, enhancing the system's capability to accurately obtain coordinate information for actual missed and repeated planting positions. The system was deployed and tested on a 2CM-2C potato planter. The results indicate that the detection accuracy for missed and repeated plantings reached 96.07% and 93.98%, respectively. Compared to traditional sensor detection methods, the system improved the accuracy of missed planting detection by 5.29%. Additionally, it successfully implemented the functionality of detecting repeated plantings, achieving accurate monitoring of quality-related information during the operation of the potato planter.