DESIGN AND IMPLEMENTATION OF A VISUAL MONITORING SYSTEM FOR PADDY FLOW INSTABILITY IN INTELLIGENT HUSKERS

Abstract

To achieve precise prevention and control of rubber roll wear in huskers, this study established a complete experimental platform integrating mechanical transmission, feeding control, and visual acquisition units, and subsequently developed a real-time monitoring system for paddy flow instability based on machine vision. An image acquisition system composed of a CMOS camera and a customized light source was built to construct a dedicated dataset for paddy flow states. Based on the lightweight YOLOv8n detection model, Python programming was adopted in the PyCharm environment with the Ultralytics library integrated, realizing real-time recognition and quantitative analysis of paddy flow states. The results demonstrated that the system realized a real-time detection efficiency of 14 FPS on a local workstation, and the YOLOv8n model achieved a recognition accuracy of 90.8% in terms of mean Average Precision at IoU threshold 0.5 (mAP@0.5) for sparse and overlapping grain states. The system could effectively capture key abnormal states, including inclined paddy grains entering the rolling zone, sparse paddy flow lasting more than 5 seconds, and overlapping paddy flow density exceeding 10 grains/cm². This study transformed the mechanical characteristics of paddy flow instability into pixel-level quantitative indicators and established an integrated visual monitoring paradigm of "perception-analysis-decision", providing effective technical support for the intelligent management and control of huskers.