An explainable deep learning framework for few shot crop disease detection in rice and sugarcane using CNN based feature extraction

TL;DR

This paper introduces a deep learning framework for detecting crop diseases in rice and sugarcane using explainable AI, achieving high accuracy with few examples.

Contribution

A novel framework combining CNN-based feature extraction with few-shot learning techniques for efficient and interpretable crop disease detection.

Findings

Prototypical Networks achieved 97.6% accuracy for rice leaf disease detection.

MAML achieved 95.27% accuracy for rice leaf disease detection.

The framework outperformed state-of-the-art methods in disease prediction.

Abstract

Where crop health is essential to global food security. Our focus is on early crop disease detection in the field of agriculture, especially Rice and Sugar cane leaf disease. This prompts researchers to consider quick, automated, cost-effective, precise, and efficient methods of identifying the kinds of diseases utilizing contemporary technologies like image processing, artificial intelligence (AI), and Explainable Artificial Intelligence (XAI). This paper proposes an framework to detect pest infestation for rice and Sugar cane cultivation and suggests an effective framework for rice and Sugar cane disease detection and forecasting that uses image processing to standard, resizing, and normalization rice and Sugar cane images then, using feature extractor using CNN after that we using few-shot learning (FSL) techniques such as like Prototypical Networks and Model-Agnostic Meta-Learning (MAML) learning techniques for superior decision-making in smart farming systems. The experimental findings demonstrated the Accuracy and specificity of the suggested framework in identifying and effectively predicting the kind of disease. According to the results, the suggested framework outperformed the state-of-the-art benchmark algorithms in disease prediction while producing results that were plausible. With Prototypical Networks and MAML for rice leaf disease datasets, it increased by up to 97.6% and 95.27%, respectively. For effective rice disease identification, Prototypical Networks and MAML for Sugar cane leaf disease datasets increased by up to 91.68% and 90.27%, respectively. Interpretable AI-driven insights were further made possible by the combination of proposed system with Grad-CAM Explanation, which improved decision-making transparency.

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