Interpretable Plant Leaf Disease Detection Using Attention-Enhanced CNN

TL;DR

This paper presents an attention-enhanced CNN model for plant leaf disease detection that achieves high accuracy and offers interpretability through various visualization techniques, advancing explainable AI in agriculture.

Contribution

The study introduces CBAM-VGG16, a novel interpretable CNN with attention modules, improving disease detection accuracy and transparency over existing methods.

Findings

Achieved up to 98.87% accuracy on diverse datasets

Enhanced feature extraction and localization with CBAM modules

Provided interpretability through attention maps and relevance propagation

Abstract

Plant diseases pose a significant threat to global food security, necessitating accurate and interpretable disease detection methods. This study introduces an interpretable attention-guided Convolutional Neural Network (CNN), CBAM-VGG16, for plant leaf disease detection. By integrating Convolution Block Attention Module (CBAM) at each convolutional stage, the model enhances feature extraction and disease localization. Trained on five diverse plant disease datasets, our approach outperforms recent techniques, achieving high accuracy (up to 98.87%) and demonstrating robust generalization. Here, we show the effectiveness of our method through comprehensive evaluation and interpretability analysis using CBAM attention maps, Grad-CAM, Grad-CAM++, and Layer-wise Relevance Propagation (LRP). This study advances the application of explainable AI in agricultural diagnostics, offering a transparent and reliable system for smart farming. The code of our proposed work is available at https://github.com/BS0111/PlantAttentionCBAM.