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

TL;DR

This paper introduces a hybrid few-shot learning model enhanced with explainability techniques for accurate and interpretable classification of plant leaf diseases under limited data conditions, improving agricultural disease diagnosis.

Contribution

The study presents a novel hybrid model combining Siamese and Prototypical Networks with XAI for plant disease classification with limited data, emphasizing interpretability.

Findings

Achieved over 92% accuracy across disease stages

Outperformed baseline few-shot learning models

Provided visual explanations of model decisions

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.