AGRICULTURAL DISEASE AND PEST DETECTION BASED ON MACHINE VISION

With the growth of the global population and the rise of agricultural demand, crop pest monitoring and control has become a key link to ensure food security. The traditional manual monitoring method has low efficiency and poor accuracy, which is difficult to meet the needs of modern agriculture. Therefore, a monitoring system for agricultural pests and diseases based on machine vision was designed, which combined with image recognition, deep learning, embedded system and wireless communication technology to realize real-time monitoring and automatic identification of pests. The experimental results show that the identification accuracy of common pests is 92%, the data transmission is stable, and the average response time is 10 seconds, which can meet the real-time monitoring needs of farmland. The introduction of solar powered systems further reduces maintenance costs and improves the sustainability of the system. This study provides an efficient and accurate solution for agricultural pest monitoring, which is of great significance for improving agricultural production efficiency and ensuring food security.