Plant Disease Classification Using Convolutional Neural Networks

Abstract

The agricultural sector faces significant losses due to plant diseases, particularly in major crops such as potatoes, tomatoes, and bell peppers. This paper presents a machine learning-based approach to classify diseases in these crops using leaf images. A Convolutional Neural Network (CNN) model was constructed and trained on datasets of healthy leaf images and diseased leaf images from potato, tomato, and bell pepper plants. The model successfully classifies diseases such as Bacterial Spot (for bell peppers), Early Blight, Late Blight, Mosaic Virus, Leaf Mold (for tomatoes), and with a classification accuracy of 93%, this system provides early detection, helping farmers take timely action to reduce disease impact and increase crop yield.

Plant phenotyping relevance

葉画像から植物病害を分類するCNN手法が研究の中心であり、植物の病害状態を直接推定するため、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 describes a CNN for plant disease classification on leaf images, but provides no public dataset link, no code/model availability statement, and no supplement with paper-specific assets. The dataset is only described generically ('The dataset comprised labeled images for each plant and its corresponding'), and

No evidence-backed public reproduction asset is currently recorded.