Implementing deep‐learning techniques for accurate fruit disease identification

Abstract

To overcome the problems of manual identification of fruit disease, this work proposes a deep‐learning model to analyse fruit images to detect diseases in the fruit. We are proposing here a convolutional neural network (CNN)‐based model for fruit disease classification. By including many layers, the proposed CNN model extracts numerous features from the fruit, deals with the large data set and finally evaluates it. With the MobileNetv2 model, the disease prediction accuracy for papaya, guava and citrus was 99.4%, 98.8% and 95.8% and the recall values were 99.4%, 98.8% and 93.8%, respectively. With VGG16, the disease prediction accuracy for papaya, guava and citrus was 97.7%, 99.6% and 94.2% and the recall values were 96.5%, 99.6% and 89.2%, respectively. Finally, with DenseNet121, the disease prediction accuracy for papaya, guava and citrus was 99.4%, 97.6% and 99.2%, and the recall values were 98.8%, 97.6% and 99.2%, respectively.

Plant phenotyping relevance

果実画像から病害状態をCNNで推定する手法の提案・評価が中心であり、植物の病徴を直接観測する画像ベース表現型解析に該当する。

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