Integrating CBAM and Squeeze-and-Excitation Networks for Accurate Grapevine Leaf Disease Diagnosis.

Abstract

The vine plant holds significant importance beyond grape farming due to its diverse products. Various grape-derived products, such as wine and molasses, highlight the vine plant's role as a valuable agricultural resource. Additionally, traditional cuisines around the world widely utilize grape leaves, contributing to their substantial economic value. However, diseases affecting grape leaves not only harm the plant and its yield but also render the leaves unsuitable for culinary use, leading to considerable economic losses for producers. Detecting diseases on grape leaves is a challenging and time-consuming task when performed manually. Thus, developing a deep learning-based model to automate the classification of grape leaf diseases is of critical importance. This study aims to classify the most common grape leaf diseases grape-scab (grape leaf blister mite) and downy mildew (grapevine downy mildew) alongside healthy leaves using deep learning techniques. Initially, we conducted a basic classification using pre-trained deep learning models. Subsequently, the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation Networks (SE) were integrated into the most successful pre-trained classification model to enhance classification performance. As a result, the classification accuracy improved from 92.73% to 96.36%.

Code and data availability

The paper describes a custom grapevine leaf image dataset (821 images collected from Tokat, Turkey) and a DenseNet121+CBAM+SENet model, but no block contains any public deposit, availability statement, or URL for the dataset, images, code, or trained model. The only URLs present are the ORCID of the author and the CC-B

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