The Role of Deep Learning in Enhancing Crop Sustainability: A Study on AlexNet’s Application in Detecting Bean Leaf Disease

Abstract

Background: Agriculture has always been the source of global food security but is challenged by crop diseases. Beans, a vital protein source, are particularly vulnerable to the diseases whch may occur due to varous pathogens ncludng bacteria fung etc. Accurate disease identification has become critical in order to maintain increasing demands of growing population. Methods: This study utilized the AlexNet convolutional neural network (CNN) to classify bean leaf images into three classes (two disease classes i.e Angular Leaf Spot and Rust and one Healthy class). An open dataset containing leaf mages of beans belonging to all the three classes was used to train the model. The mages were first preprocessed and resized to 224x224 pixels for optimal model performance. The AlexNet model was trained for 25 epochs using cross-entropy loss, ReLU activation, max-pooling and dropout regularization. Result: An accuracy of 95.4% was achieved while the validation accuracy was 78.2%. Other performance metrics, such as precision, recall and F1-score, highlighted strengths in identifying healthy leaves, with an overall accuracy of 82.81%. However, some misclassifications occurred between disease classes due to visual similarities. The results demonstrate AlexNet’s potential for automated plant disease detection, providing a scalable solution for enhancing agricultural practices and food security. Further optimization and integration with field applications are recommended for improved accuracy and usability.

Plant phenotyping relevance

豆葉画像から健全・病害状態をCNNで推定する画像ベースの植物表現型手法が研究の中心であり、分類性能も評価しているため。

Code and data availability

The paper uses an open bean leaf image dataset and an AlexNet model, but provides no public URL, deposit, or availability statement for the dataset, code, or trained model. The data availability statement explicitly says 'Not Applicable', and all URLs in the article are citations to prior work, not paper-specific repro

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