Mulberry leaf disease detection by CNN-ViT with XAI integration

Hasan, Mohammad Asif, Haque, Fariha ORCID: https://orcid.org/0009-0000-8886-4943, Sarker, Hasan ORCID: https://orcid.org/0000-0002-7305-649X, Abdullah, Rafae, Roy, Tonmoy ORCID: https://orcid.org/0000-0002-0757-5523, Taaha, Nishat ORCID: https://orcid.org/0009-0002-1768-1255, Arafat, Yeasin, Patwary, Abdul Karim ORCID: https://orcid.org/0009-0001-8259-0600, Ahsan, Mominul and Haider, Julfikar ORCID: https://orcid.org/0000-0001-7010-8285 (2025) Mulberry leaf disease detection by CNN-ViT with XAI integration. PLOS One, 20 (6). e0325188. ISSN 1932-6203

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Abstract

Mulberry leaf disease detection is vital for maintaining the health and productivity of mulberry crops. In this paper, a novel approach was proposed by integrating explainable artificial intelligence (XAI) techniques with a convolutional neural network (CNN) and vision transformer (ViT) for effective mulberry leaf disease classification with three disease classes. Initially, in this proposed CNN-ViT model, features are extracted using a customized CNN architecture, and then the extracted features are fed into ViT for leaf disease classification in a more streamlined approach. The CNN-ViT model achieved promising results with a projection dimension of 64, utilizing 8 heads and 8 transformer layers, yielding an accuracy of 95.60% with notable precision of 94.75%, recalls of 92.40%, and F1-scores of 93.45%. The proposed method also took 0.0017 seconds to predict an individual image. The accuracy of the proposed method was comparable to that of other state-of-the-art (SOTA) methods reported in the literature. Finally, Grad-CAM was utilized for detecting precise region of interest for diseased leaves, leaf spots, and leaf rust, providing interpretability and insights into the model’s decision-making process. This comprehensive approach demonstrates the effectiveness of explainable artificial intelligence (XAI) integration in the CNN-ViT model for mulberry leaf disease detection, paving the way for improved agricultural disease management strategies.