Detection of Apple Plant Diseases using Leaf Images through Convolution Neural Networks

Abstract

Effective identification and classification of plant diseases are critical for maintaining agricultural productivity and ensuring food security. With recent advancements in computer vision, Convolutional Neural Networks (CNNs) have emerged as powerful tools for diagnosing crop diseases. This research presents a novel approach for detecting apple plant diseases through the analysis of leaf images using CNNs. We utilize the PlantVillage dataset, which comprises a diverse collection of healthy and diseased apple leaf images. The dataset undergoes rigorous preprocessing to enhance image quality, followed by training and evaluation of the CNN models. Our approach achieves remarkable results, demonstrating an accuracy of 99.67%, precision of 98.23%, and F1 score of 99.04%. These findings affirm the robustness of our CNN-based model in accurately classifying various apple diseases. This research not only provides a reliable method for early disease detection but also emphasizes its significance in optimizing crop yield and safeguarding food security. By facilitating timely interventions, our proposed framework has the potential to mitigate the impact of diseases in apple orchards and can be extended to broader applications in precision agriculture.

Plant phenotyping relevance

リンゴ葉画像から病害状態をCNNで推定する画像ベースの植物表現型解析手法が研究の中心であり、学習・評価による技術検証も行っている。

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