Deep Learning based Automated System for Banana Plant Disease Detection and Classification

Abstract

In India, one of the primary agricultural practices is the production of bananas. A prevalent issue in farming is that the crop has been impacted by multiple illnesses. Disease identification in bananas has been shown to be more difficult in the field because the fruit is prone to various diseases and causes farmers to suffer significant losses. Consequently, this study aimed at developing an automatic system for the early detection and classification of banana plant diseases using deep learning. Three pre-trained convolutional neural network models MobileNet, VGG16, and InceptionV3 are used to classify banana disease images. The banana disease images dataset from the PSFD-Musa Dataset is utilized for training, validation, and testing. The proposed system is developed and checked to classify banana plant disease photographs into one of seven categories. The MobileNet achieved an accuracy of 96.72%, VGG16 an accuracy of 55.68%, and InceptionV3 an accuracy of 63.65%.

Plant phenotyping relevance

バナナ葉などの病徴画像から植物の病害状態を分類する深層学習システムの開発・評価が中心であり、植物表現型（病害状態）の画像ベース取得・推定に該当する。

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