An Intelligent Field Monitoring System Based on Enhanced YOLO-RMD Architecture for Real-Time Rice Pest Detection and Management

Abstract

This study presents a comprehensive solution for precise and timely pest monitoring in field environments through the development of an advanced rice pest detection system based on the YOLO-RMD model. Addressing critical challenges in real-time detection accuracy and environmental adaptability, the proposed system integrates three innovative components: (1) a novel Receptive Field Attention Convolution module enhancing feature extraction in complex backgrounds; (2) a Mixed Local Channel Attention module balances local and global features to improve detection precision for small targets in dense foliage; (3) an enhanced multi-scale detection architecture incorporating Dynamic Head with an additional detection head, enabling simultaneous improvement in multi-scale pest detection capability and coverage. The experimental results demonstrate a 3% accuracy improvement over YOLOv8n, achieving 98.2% mean Average Precision at 50% across seven common rice pests while maintaining real-time processing capabilities. This integrated solution addresses the dual requirements of precision and timeliness in field monitoring, representing a significant advancement for agricultural vision systems. The developed framework provides practical implementation pathways for precision pest management under real-world farming conditions.