Interpretable Plant Leaf Disease Detection Using Attention-Enhanced CNN

Balram Singh, Ram Prakash Sharma, Somnath Dey
12/19/2025
cs.CVcs.AI

Official code: BS0111/PlantAttentionCBAM (0 stars, last commit 2025-12-19).

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.

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BS0111/PlantAttentionCBAMOfficial100%

An explainable CBAM-VGG16 based deep learning framework for plant leaf disease classification and localization, evaluated on five public datasets using multiple visual interpretability techniques.

00PythonDec 15, 202510 months ago

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Cite this paper

@article{singh2025interpretable,
  title  = {Interpretable Plant Leaf Disease Detection Using Attention-Enhanced CNN},
  author = {Balram Singh and Ram Prakash Sharma and Somnath Dey},
  year   = {2025},
  eprint = {2512.17864},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.17864v2}
}

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