Rethinking feature representation and attention mechanisms in intelligent recognition of leaf pests and diseases in wheat.

Abstract

Complex pest and disease features appearing during the growth of wheat crops are difficult to capture and can seriously affect the normal growth of wheat crops. The existing methods ignore the full pre-interaction of deep and shallow features, which largely affects the accuracy of identification. To address the above problems and needs, we rethink the feature representation and attention mechanism in intelligent recognition of wheat leaf diseases and pests, and propose a representation and recognition network (RReNet) based on the feature attention mechanism. RReNet captures key information more efficiently by focusing on complex pest and disease characteristics and fusing multi-semantic feature information. In addition, RReNet further enhances the perception of complex disease and pest features by using four layers of detection units and fast IoU loss function, which significantly improves the accuracy and robustness of wheat leaf disease and pest recognition. Tests on a challenging wheat leaf pest and disease dataset with twelve pest and disease types show that RReNet achieves precision, recall and mAP as high as 94.1%, 95.7% and 98.3% respectively. Also, ablation experiments proved the effectiveness of all parts of the proposed method.

Plant phenotyping relevance

小麦葉の病害・害虫状態を画像から認識する深層学習ネットワークを提案し、データセット上で性能評価とアブレーション実験を行っているため、植物状態の取得・推定手法が中心である。

Code and data availability

The paper's phenotyping-relevant asset is the authors' annotated sub-dataset LWDCD2020Detection (900 annotated wheat pest/disease images derived from the public LWDCD2020 dataset), used for all detection experiments. No public repository, code, or model release is stated; the Data availability statement says datasets '

No evidence-backed public reproduction asset is currently recorded.