AUTHOR=Jannat Meftahul , Uddin Md Shahab , Hasan Mohammad Asif , Alam Md Saimun , Paul Avijit , Chowdhury Muhammad E. H. , Haider Julfikar 

TITLE=Real-time jute leaf disease classification using an explainable lightweight CNN via a supervised and semi-supervised self-training approach

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1647177

DOI=10.3389/fpls.2025.1647177

ISSN=1664-462X

ABSTRACT=IntroductionTimely detection of jute leaf diseases is vital for sustaining crop health and farmer livelihoods. Existing deep learning approaches often rely on large, annotated datasets, which are costly and time-consuming to produce.Methods and resultsTo address this challenge, a lightweight convolutional neural network integrated with a semi-supervised learning self-training framework was proposed to enable accurate classification with minimal labeled data. The model combines modified depthwise separable convolutions, an enhanced squeeze-and-excite block, and a modified mobile inverted bottleneck convolution block, achieving strong representational power with only 2.24M parameters (8.54 MB). On a self-collected dataset of jute leaf images across three classes (Cescospora leaf spot, golden mosaic, and healthy leaf), the proposed model achieved a best accuracy of 98.95% under the supervised training with training, testing and validation split of 80:10:10. Remarkably, the model also attained a best accuracy of 97.89% in the semi-supervised learning (SSL) setting with only 10% labeled and 90% unlabeled data, demonstrating that near-supervised performance can be maintained while substantially reducing the dependency on costly labeled datasets. The application of explainable AI method such as Grad-CAM provided interpretable visualizations of diseased regions, and deployment as a Flask-based web application demonstrated practical, real-time usability in resource-constrained agricultural environments.ConclusionThese results highlight the novelty of combining SSL with a lightweight CNN to deliver near-supervised performance, improved interpretability, and real-world applicability while substantially reducing the dependence on expert-labeled data.