FDRW-Net: A feature dynamic reweighting network for cotton disease detection in natural scenes

Abstract

Current cotton disease detection technologies often rely on complex computations and large parameter counts to achieve high accuracy. However, the trade-off between precision and resource demands constrains their applicability in smart agriculture. To address this limitation, a lightweight Feature Dynamic Reweighting Network (FDRW-Net) is proposed based on the YOLO11 model for cotton disease detection in natural scenes, enhancing detection performance while reducing resource consumption. First, an Enhanced Multi-Scale Spatial Attention (EMSA) mechanism is developed and integrated into a novel Information Sharing Head (ISH). Multi-scale dynamic sensing capability is enhanced and background noise is suppressed through this design. Then, an Adaptive Downsampling Module (ADM), based on ADown, incorporates adaptive weight allocation and input feature weighting, effectively reducing computational costs. Finally, the C3T combining MetaFormer and a Convolutional Gated Linear Unit (CGLU) is introduced into the feature extraction framework. Limitations of convolutional neural networks are addressed and sensitivity to fine-grained features is enhanced by this structure. The performance of the proposed model was evaluated on a self-built dataset containing fusarium wilt, brown spot disease, verticillium wilt, and red leaf blight. Two public datasets were also included in this assessment. Results from the self-built dataset indicate that mAP50 is improved by 3.2 % to 93% compared to the baseline network. Parameters, floating-point operations, and model size are reduced by 46.5 %, 39.7 %, and 43.4 %, respectively, in this evaluation. Experiments on public datasets further validate that the proposed model outperforms existing cotton disease detection methods in overall performance. Based on this improved model, a real-time cotton disease diagnosis system was developed for field monitoring and early warning.

Plant phenotyping relevance

綿花の病害状態を自然画像から推定する軽量画像解析モデルを開発し、複数データセットで性能検証しているため、植物病害フェノタイピング手法が中心です。

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