Improving Deep Learning-based Plant Disease Classification with Attention Mechanism

Abstract

Abstract In recent years, deep learning-based plant disease classification has been widely developed. However, it is challenging to collect sufficient annotated image data to effectively train deep learning models for plant disease recognition. The attention mechanism in deep learning assists the model to focus on the informative data segments and extract the discriminative features of inputs to enhance training performance. This paper investigates the Convolutional Block Attention Module (CBAM) to improve classification with CNNs, which is a lightweight attention module that can be plugged into any CNN architecture with negligible overhead. Specifically, CBAM is applied to the output feature map of CNNs to highlight important local regions and extract more discriminative features. Well-known CNN models (i.e. EfficientNetB0, MobileNetV2, ResNet50, InceptionV3, and VGG19) were applied to do transfer learning for plant disease classification and then fine-tuned by a publicly available plant disease dataset of foliar diseases in pear trees called DiaMOS Plant. Amongst others, this dataset contains 3006 images of leaves affected by different stress symptoms. Among the tested CNNs, EfficientNetB0 has shown the best performance. EfficientNetB0+CBAM has outperformed EfficientNetB0 and obtained 86.89% classification accuracy. Experimental results show the effectiveness of the attention mechanism to improve the recognition accuracy of pre-trained CNNs when there are few training data.

Plant phenotyping relevance

植物病徴を画像から分類する深層学習手法の改良と評価が研究の中心であり、植物の病害状態を直接推定する画像ベース表現型手法に該当する。

Code and data availability

The paper uses the publicly available DiaMOS Plant dataset, but that dataset is cited prior work (Fenu and Malloci 2021) rather than an asset deposited by this paper's authors. No author analysis code, scripts, trained models, or data availability statements with public URLs appear in the supplied blocks.

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