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

## TL;DR

A new lightweight AI model accurately identifies jute leaf diseases in real-time using minimal labeled data and provides interpretable results.

## Contribution

A novel combination of a lightweight CNN with semi-supervised learning to reduce reliance on labeled data while maintaining high accuracy.

## Key findings

- The model achieved 98.95% accuracy with supervised training using 80:10:10 data split.
- It maintained 97.89% accuracy in semi-supervised learning with only 10% labeled data.
- The model was deployed as a Flask-based web application for real-time use in resource-limited settings.

## Abstract

Timely 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.

To 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.

These 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.