Optimisation of Image Processing Technique for Potato Disease Detection

Abstract

Abstract This study investigates the use of deep learning models, Visual Geometry Group 16-layer network (VGG16), AlexNet and a custom Convolutional Neural Network (CNN), for classifying potato leaf images into categories of Early Blight, Late Blight, and Healthy leaves. The dataset, comprising 3,293 images, combined locally sourced images from Anand Agricultural University (AAU), Gujarat, India, and images from the Plant Village (PV) repository. Various configurations were tested, including batch sizes of 32 and 64 and training epochs of 30 and 60. Results indicate that the custom CNN achieved the highest performance, with an accuracy of 98.8% and a low loss of 0.055, surpassing both VGG16 and AlexNet. Notably, the custom CNN required only 128,387 trainable parameters, significantly fewer than VGG16 (138 million) and AlexNet (58 million), highlighting its efficiency. This efficiency demonstrates the custom CNN’s optimized architecture, enabling high classification performance with lower computational demands.

Plant phenotyping relevance

ジャガイモ葉画像から病害状態を推定する画像処理・深層学習手法の最適化と性能比較が研究の中心であり、植物病害フェノタイピング手法に該当する。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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