Plant Disease Detection Using a Simple Deep Learning Framework

Abstract

Plant diseases significantly affect agricultural productivity and crop quality, making early detection essential for sustainable farming. This study presents a simple deep learning framework for automated plant disease detection using leaf images. A Convolutional Neural Network (CNN) model was developed and trained on a publicly available plant disease dataset to classify healthy and diseased leaves. Image preprocessing and augmentation techniques were applied to improve model generalization and performance. Experimental results demonstrate that the proposed framework effectively identifies plant diseases with high accuracy while maintaining low computational complexity. The proposed approach can assist farmers and agricultural experts in timely disease diagnosis and crop management.

Plant phenotyping relevance

葉画像から植物の健全・罹病状態を推定するCNN手法を開発しており、植物病害の表現型取得・分類が研究の中心であるため。

Code and data availability

The paper uses the public PlantVillage dataset but provides no author-deposited dataset, code, models, or supplements; no availability statements or public URLs for paper-specific assets are given.

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