Performance evaluation of plant leaf disease detection using deep learning models

Abstract

Plant diseases have a serious impact on production, and hence they must be detected and recognised at early stages. Smart firming using deep learning can automatically identify infected crops. We provide extremely effective convolution neural network (CNN) designs for the identification of leaf diseases in this research strategy. For the training and testing phases of this study, a database of potato leaves is produced. To classify the disease from the input photos of the supported training dataset, we employed CNN to extract its characteristics. 1700 photos of potato leaves were used for model training, and then about 600 images were used for testing. To identify citrus diseases, Convolutional Neural Networks, Deep Learning, base learning, and transfer learning were applied. Results from training, testing, and experiments indicate that the suggested architecture has outperformed other current models in terms of ResNet model accuracy, achieving a score of 99.62%.

Plant phenotyping relevance

葉画像から植物病害を推定するCNN手法を開発・比較評価しており、植物の病害状態の取得が研究の中心である。

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