ZDAM: a new deep learning model for bean leaf disease diagnosis.

Abstract

Introduction Accurate disease diagnosis is crucial for enhancing agricultural productivity and reducing postharvest losses, directly impacting food quality and safety. Traditional detection methods often rely on extensive feature modeling and perform poorly in complex field environments. Methods This study proposes a deep learning model called ZDAM, based on an improved ZFNet integrated with a dual attention mechanism. The classical ZFNet is first optimized to improve feature extraction efficiency. A combined channel and spatial attention mechanism is then incorporated to refine feature representation for disease identification in key crops. Finally, a residual module is added to boost accuracy. Results Evaluated on a dataset of 11,903 bean leaf images covering healthy leaves and four disease types, including leaf mould, rust, mosaic, and white spot, the model achieves an average recognition accuracy of 99.02%, outperforming MobileMamba, Vision Transformer, and Chest- OMD. Discussion This approach offers a scalable solution for automated disease monitoring, supporting postharvest quality preservation and sustainable crop production.

Plant phenotyping relevance

豆葉の病害状態を画像から推定する深層学習モデルを開発し、複数モデルとの性能比較も行っており、植物フェノタイピング手法が研究の中心である。

Code and data availability

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