Document Type
Article
Journal/Book Title/Conference
PLoS One
Author ORCID Identifier
Fariha Haque https://orcid.org/0009-0000-8886-4943
Hasan Sarker https://orcid.org/0000-0002-7305-649X
Tonmoy Roy https://orcid.org/0000-0002-0757-5523
Nishat Taaha https://orcid.org/0009-0002-1768-1255
Abdul Karim Patwary https://orcid.org/0009-0001-8259-0600
Julfikar Haider https://orcid.org/0000-0001-7010-8285
Volume
20
Issue
6
Publisher
Public Library of Science
Publication Date
6-4-2025
Journal Article Version
Version of Record
First Page
1
Last Page
29
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
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.
Recommended Citation
Hasan MA, Haque F, Sarker H, Abdullah R, Roy T, Taaha N, et al. (2025) Mulberry leaf disease detection by CNN-ViT with XAI integration. PLoS One 20(6): e0325188. https://doi.org/10.1371/journal.pone.0325188
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Educational Assessment, Evaluation, and Research Commons, Instructional Media Design Commons, Library and Information Science Commons, Plant Sciences Commons