Investigating attention mechanisms for plant disease identification in challenging environments.

Abstract

There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field images present challenges due to complex backgrounds, similarities between different disease symptoms, and the need to detect multiple diseases simultaneously. These obstacles hinder the development of a reliable classification model. The attention mechanisms emerge as a critical factor in enhancing the robustness of classification models by selectively focusing on relevant regions or features within infected regions in an image. This paper provides details about various types of attention mechanisms and explores the utilization of these techniques for the machine learning solutions created by researchers for image segmentation, feature extraction, object detection, and classification for efficient plant disease identification. Experiments are conducted on three models: MobileNetV2, EfficientNetV2, and ShuffleNetV2, to assess the effectiveness of attention modules. For this, Squeeze and Excitation layers, the Convolutional Block Attention Module, and transformer modules have been integrated into these models, and their performance has been evaluated using different metrics. The outcomes show that adding attention modules enhances the original models' functionality.

Plant phenotyping relevance

植物病害の画像から病害状態を推定する画像解析手法が中心で、注意機構を組み込んだ複数モデルの性能評価も実施しているため、植物フェノタイピング手法として採用する。

Code and data availability

The paper's experiments used a publicly available plant leaf disease image dataset deposited in Mendeley Data, explicitly linked in the data availability statement. No author analysis code or trained model checkpoints are disclosed.