Explainable AI Based framework for Banana Disease Detection

Abstract

Abstract Due to widespread usage of banana as a staple food crop and susceptibility to numerous illnesses. Bananas require sophisticated detection techniques to support sustainable agricultural practices. Bananas are particularly susceptible to various stem and leaf spot diseases, resulting in significant economic losses within the banana cultivation sector. In this paper, a new XAI framework for banana disease detection and classification is introduced. Our framework uses state-of-the-art AI methods to analyze photos of banana plants. With great precision, it can detect a variety of illnesses like Cordana, Black Sigatoka, Pestalotiopsis, and fusarium wilt. The outcomes show that the framework performs better than current techniques in precisely identifying and categorizing banana diseases. The research employed a Convolutional Neural Networks (CNNs) to detect diseases in banana plants using RGB images of banana leaves. We used pre-trained model called EfficientnetB0 model to evaluate using two datasets BLSD and BDT. For BLSD, the model achieved an accuracy of 99.22%. Next for BDT, on the other hand, demonstrated improved performance with an accuracy of 99.63%.

Plant phenotyping relevance

バナナ葉のRGB画像から病害を検出・分類するCNN/XAIフレームワークが研究の中心であり、植物の病害状態を画像から推定するフェノタイピング手法に該当する。

Code and data availability

The paper uses two public banana disease image datasets (BLSD and BDT), but both are cited prior publications (refs [25] and [26]) rather than assets released by this paper's authors. No author code, trained models, or data deposit with an authors' public URL is mentioned anywhere in the supplied blocks.

No evidence-backed public reproduction asset is currently recorded.