ArticleFrontiers in plant science2026
Rice pest detection via multi-scale edge network and wavelet attention enhancement.
Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
- Comparative Evaluation of CNN and Transformer-Based Instance Segmentation Architectures for Whitefly Detection in Citrus Crops.Plants (Basel, Switzerland) · 2026Article
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2 authors.
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Abstract
Rice pest detection faces critical challenges including small target recognition difficulties, high morphological similarities, and complex field backgrounds. This study proposes BEAM-YOLO (Bi-branch Edge Attention Multi-scale YOLO) to address these limitations.We constructed the JRICE-PD dataset encompassing 11 economically significant rice pests (4,565 images) and developed four innovative modules: a Multi-scale morphological Edge Network (MEN) enhancing 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.BEAM-YOLO achieves 86.6±0.5% mAP@50 and 72.7±0.9% mAP@50-95, 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 precision agriculture advancement and application.
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