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

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

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...

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Bibliographic Details
Main Authors: Jiangdong Yin, Jun Zhu, Gang Chen, Lihua Jiang, Huanhuan Zhan, Haidong Deng, Yongbing Long, Yubin Lan, Binfang Wu, Haitao Xu
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Agriculture
Subjects:
rice pests
multi-scale detection
RFAConv
MLCA
DyHead
Online Access:https://www.mdpi.com/2077-0472/15/8/798
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Summary: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.
ISSN:2077-0472

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