Enhancing Banana Leaf Disease Diagnosis Using Explainable AI on a Simple Convolutional Neural Network

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Bharath A R; Hemalatha N; Sreekumar K M

Enhancing Banana Leaf Disease Diagnosis Using Explainable AI on a Simple Convolutional Neural Network

Authors: Bharath A R, Hemalatha N, Sreekumar K M

Unique Paper ID: 183091
Volume: 12
Issue: no
PageNo: 106-110

Keywords: No Keywords Found

Abstract:
While high-performance deep learning models have been applied to banana leaf disease detection [1][3], their interpretability remains underexplored. In this study, we deliberately use a baseline Convolutional Neural Network (CNN) with moderate accuracy to demonstrate how Explainable AI (XAI) techniques—such as Grad-CAM and SoftMax confidence analysis—can validate and interpret model predictions. We train a Global Average Pooling (GAP)-based CNN on the Banana LSD dataset [4] and observe a test accuracy of 74.7%. While more advanced models have reported higher performance [5], [6], our focus remains on interpretability and practical relevance. By integrating explainability techniques, we demonstrate that even a basic model can provide reliable support for disease diagnosis, especially in agricultural environments where transparency and resource efficiency are essential.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: no
PageNo: 106-110

Enhancing Banana Leaf Disease Diagnosis Using Explainable AI on a Simple Convolutional Neural Network

Available:https://ijirt.org/article?manuscript=183091

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