AI RESEARCH
XAI and Few-shot-based Hybrid Classification Model for Plant Leaf Disease Prognosis
arXiv CS.LG
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ArXi:2603.06676v1 Announce Type: cross 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.