Open Access

ARTICLE

Enhanced Wheat Disease Detection Using Deep Learning and Explainable AI Techniques

Hussam Qushtom, Ahmad Hasasneh^*, Sari Masri

Department of Natural, Engineering and Technology Sciences, Faculty of Graduate Studies, Arab American University, Ramallah, P.O. Box 240, Palestine

* Corresponding Author: Ahmad Hasasneh. Email:

Computers, Materials & Continua 2025, 84(1), 1379-1395. https://doi.org/10.32604/cmc.2025.061995

Received 07 December 2024; Accepted 23 April 2025; Issue published 09 June 2025

Abstract

This study presents an enhanced convolutional neural network (CNN) model integrated with Explainable Artificial Intelligence (XAI) techniques for accurate prediction and interpretation of wheat crop diseases. The aim is to streamline the detection process while offering transparent insights into the model’s decision-making to support effective disease management. To evaluate the model, a dataset was collected from wheat fields in Kotli, Azad Kashmir, Pakistan, and tested across multiple data splits. The proposed model demonstrates improved stability, faster convergence, and higher classification accuracy. The results show significant improvements in prediction accuracy and stability compared to prior works, achieving up to 100% accuracy in certain configurations. In addition, XAI methods such as Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) were employed to explain the model’s predictions, highlighting the most influential features contributing to classification decisions. The combined use of CNN and XAI offers a dual benefit: strong predictive performance and clear interpretability of outcomes, which is especially critical in real-world agricultural applications. These findings underscore the potential of integrating deep learning models with XAI to advance automated plant disease detection. The study offers a precise, reliable, and interpretable solution for improving wheat production and promoting agricultural sustainability. Future extensions of this work may include scaling the dataset across broader regions and incorporating additional modalities such as environmental data to enhance model robustness and generalization.

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Keywords

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