Real-Time Monitoring and Precision Control Technology for Agricultural Machinery Operation Quality Based on Machine Vision
Abstract：Traditional agricultural machinery operation quality is mostly dependent on manual experience, which leads to problems such as delayed monitoring and insufficient control accuracy. To address this, a real-time monitoring and precision control technology system based on machine vision is proposed. This system integrates key technologies such as multispectral imaging, deep learning object recognition, multi-sensor fusion, and intelligent decision-making control, and establishes a closed-loop control system that includes 'perception, analysis, decision-making, and execution'. The paper elaborates on the real-time identification algorithm for operation objects based on an improved YOLOv5, the operation quality evaluation model formed by multi-modal data fusion, and the adaptive control strategy based on fuzzy PID. Field experiments show that in deep loosening and sowing operations, the system improves the stability coefficient of operation depth to 95.3% and the sowing qualification rate to 98.1%, with better results than traditional methods.
Keywords：Machine VisionAgricultural Machinery IntelligenceOperation Quality MonitoringReal-Time ControlMulti-Sensor FusionDeep LearningPrecision Agriculture
Publication Date：2025-11-25
Online Publishing Date：2025-12-18(First online date of this platform, not the publication date of the document)
Pages：4( 14-17 )