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

Jiangdong Yin, Jun Zhu, Gang Chen, Lihua Jiang, Huanhuan Zhan, Haidong Deng, Yongbing Long, Yubin Lan, Binfang Wu () and Haitao Xu ()
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Jiangdong Yin: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Jun Zhu: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Gang Chen: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Lihua Jiang: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Huanhuan Zhan: National S&T Innovation Center for Modern Agricultural Industry, Guangzhou 510520, China
Haidong Deng: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Yongbing Long: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Yubin Lan: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
Binfang Wu: Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, Jiaying University, Meizhou 514015, China
Haitao Xu: College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 8, 1-21

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.

Keywords: rice pests; multi-scale detection; RFAConv; MLCA; DyHead (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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