Implementation of Explainable Ai in Deep Learning Methods for Multiclass Classification of Plant Diseases in Mango Leaves

Abstract

Maintaining optimal yield plays a crucial role in the prosperity of agriculture and in turn the economy of the country. One way to optimize this yield is by early and accurate detection and diagnosis of crop diseases. Traditional methods that involve manual inspection or the like tend to be tedious and often inaccurate. Hence the use of machine learning and convolutional neural networks have proven to be of great advantage in terms of accuracy, reliability, ease of implementation etc. This paper explores various deep learning models such as AlexNet, ResNet, Swin Transformer, Vgg-16, vit model for plant leaf disease detection and classification on a dataset of mango leaves and compares aspects such as accuracy and loss. Further the models have been combined using feature fusion, and their accuracies compared. Finally, a combination of ResNet and AlexNet has been proposed with an impressive accuracy of 99.97%. Further, Grad-CAM (Gradient-weighted Class Activation Mapping) has been implemented to highlight important regions in the leaf images which improves visualization. This can potentially provide an accurate identification and classification of plant diseases based on leaf images.

Plant phenotyping relevance

マンゴー葉画像から植物病害を検出・分類する深層学習手法を比較・融合し、Grad-CAMで病徴領域を可視化しており、植物の病害状態を画像から推定する方法が中心である。

Code and data availability

The paper uses a Kaggle-sourced mango leaf disease dataset and PyTorch code, but no public URL, repository, or availability statement for the dataset, code, or trained models is provided in the supplied blocks.

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