Discriminative Features Extraction for Plant Disease Classification Using Deep CNN

Abstract

To ensure that plant diseases are well controlled and managed that would reduce crop losses and ultimately improve on food security then diseases need to be identified correctly at the right time. This paper uses a CNN approach for identifying plant diseases using the Plant Village, web-based dataset that has on average 35 classes, with 29281 images, comprising of both healthy and diseased leaves. To improve the model performance further techniques like data augmentation, contrast enhancement, noise reduction techniques were used. The training results of the proposed CNN had a training loss of 0.0808 and a validation loss of 0.3330.The training as well as the validation accuracy achieved were 97.41% as well as 90.34% respectively. Other measures of evaluation of the presented model are precision of 0.9139; recall of 0.9034; the F1 score was 0.9019. Thus, in order to raise the accuracy in classification, other features like color, veins, roughness of the leaf surface etc., were also considered. It was also indicated how much effective the proposed model was for edge computing solutions as compared to other models including DenseNet121, ResNet50, Alex Net and VGG16. This work shows that contemporary agriculture could reliably and dependably utilize deep learning in the detection of plant diseases as a dependable and scalable tool.

Plant phenotyping relevance

植物葉の画像から病害状態をCNNで推定する手法の開発・評価が中心であり、植物表現型としての病害検出に該当する。

Code and data availability

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