ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
This article is part of the Research TopicAdvancements and Challenges in Visual Perception for Autonomous Crop Row Detection in AgricultureView all articles
Rice Pest Detection via Multi-scale Edge Network and Wavelet Attention Enhancement
Provisionally accepted
- Jiangxi University of Science and Technology, Ganzhou, China
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This paper addresses key challenges in rice pest and disease detection, including small target recognition difficulties, high morphological similarities, and complex field backgrounds, by proposing BEAM-YOLO (Bi-branch Edge Attention Multi-scale YOLO) detection framework. We construct the JRICE-PD dataset encompassing 11 economically significant rice pests and design four innovative modules: a Multi-scale morphological Edge Network (MEN) that enhances feature discrimination; a Bi-branch Attention Feature Enhancement (BAFE) module utilizing Haar wavelet transform for foreground-background separation; an Enhanced Multi-scale Bidirectional Feature Pyramid Network (EM-BFPN) optimizing information interaction; and a Spatial-Channel Augmented Upsampling (SCAU) improving small target detection capabilities. Experimental results demonstrate that BEAM-YOLO achieves 86.6±0.5% mAP@50 and 72.7±0.9% mAP@50-95 on rice pest datasets, outperforming YOLOv11 by 3.3% and 3.0% respectively, while maintaining relatively low computational overhead and parameter count. This research provides reliable algorithmic support for intelligent agricultural pest monitoring systems, contributing to the advancement and application of precision agriculture technologies.
Keywords: deep learning, Edge feature enhancement, object detection, Rice pest detection, Wavelet Attention Mechanism
Received: 17 Nov 2025; Accepted: 20 Jan 2026.
Copyright: © 2026 Huang and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ruoxuan Zhou
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