Residual attention based multi-label learning for apple leaf disease identification

文献类型： 外文期刊

第一作者： Zhou, Changjian

作者： Zhou, Changjian;Song, Jia;Xiang, Wensheng;Zhou, Changjian;Zhao, Zhenyuan;Chen, Wenzhuo;Feng, Yuquan;Xiang, Wensheng;Zhao, Zhenyuan;Chen, Wenzhuo;Feng, Yuquan;Xiang, Wensheng

作者机构：

关键词： fruits; attention mechanism; machine learning; one-hot encoding

期刊名称：JOURNAL OF AGRICULTURAL ENGINEERING （ 影响因子：2.5； 五年影响因子：2.8 ）

ISSN： 1974-7071

年卷期： 2024 年 55 卷 4 期

页码：

收录情况： SCI

摘要： Recent studies suggest that plant disease identification via machine learning approach is vital for preventing the spread of diseases. Identifying multiple diseases simultaneous on a single leaf is one of the most irritating issues in agricultural production. However, the existing approaches are difficult to meet the requirements of production practice in accuracy or interpretability. Here, we present residual attention based multi-label learning framework (RAMDI), a method for predicting apple leaf diseases in natural environment. Built upon an attention based multi-label learning framework, the channel and spatial attention mechanisms are investigated and embedded in residual network for multi-label disease prediction, which takes advantage of channel-wise and spatial-wise attention weights. Experimental results indicate that the RAMDI achieves 0.976 accuracy, 0.986 F-score, and 0.979 mAPs, outperforms the existing state-of-the-art apple leaf disease identification models. RAMDI not only predicts multi-disease on a single leaf simultaneously, but also reveals the interpretability among positive predictions that contribute most to identify the key features that are significant for the leaf diseases. This method achieves the following two achievements. Firstly, it provides a solution for detecting multiple diseases on a single leaf. Secondly, this approach gains an interpretable understanding for apple leaf disease identification.

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