XAI and Few-shot-based Hybrid Classification Model for Plant Leaf Disease Prognosis

2026-03-03

AI Summary

This study addresses the challenge of accurately identifying disease stages in maize, rice, and wheat leaves under conditions of scarce labeled data. To this end, it proposes a novel few-shot learning (FSL) framework that integrates explainable artificial intelligence (XAI) with episodic training based on Siamese and Prototypical Networks to effectively learn discriminative disease features. The approach incorporates Grad-CAM to visualize critical decision-making regions, thereby enhancing model transparency. Evaluated on a newly curated few-shot dataset, the method achieves over 92% performance across accuracy, precision, recall, and F1-score, significantly outperforming existing FSL baselines and enabling both high-accuracy and interpretable classification of crop disease stages.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Performing a timely and accurate identification of crop diseases is vital to maintain agricultural productivity and food security. The current work presents a hybrid few-shot learning model that integrates Explainable Artificial Intelligence (XAI) and Few-Shot Learning (FSL) to address the challenge of identifying and classifying the stages of disease of the diseases of maize, rice, and wheat leaves under limited annotated data conditions. The proposed model integrates Siamese and Prototypical Networks within an episodic training paradigm to effectively learn discriminative disease features from a few examples. To ensure model transparency and trustworthiness, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed for visualizing key decision regions in the leaf images, offering interpretable insights into the classification process. Experimental evaluations on custom few-shot datasets developed in the study prove that the model consistently achieves high accuracy, precision, recall, and F1-scores, frequently exceeding 92% across various disease stages. Comparative analyses against baseline FSL models further confirm the superior performance and explainability of the proposed approach. The framework offers a promising solution for real-world, data-constrained agricultural disease monitoring applications.

Problem

• plant leaf disease
• few-shot learning
• disease classification
• limited annotated data
• crop disease identification

Innovation

• Explainable AI
• Few-Shot Learning
• Grad-CAM
• Siamese Network
• Prototypical Network