Explainable Artificial Intelligence for Plant Disease Detection: A Scoping Review of Models, Interpretability, and Real-World Challenges

Abstract

The application of artificial intelligence (AI) in plant disease detection has rapidly advanced, enabling accurate, timely, and automated diagnosis from image data. Deep learning models, particularly convolutional neural networks (CNNs) and hybrid architectures, have demonstrated high classification performance, often exceeding 95% accuracy under controlled conditions. However, the lack of model interpretability remains a major barrier to real-world adoption in agricultural settings. To address this challenge, explainable artificial intelligence (XAI) techniques have been increasingly integrated to enhance transparency and user trust. This scoping review systematically examines recent studies (2021–2025) on AI-based plant disease detection with a focus on model architectures, explainability methods, datasets, and practical applicability. A total of 17 studies were analyzed to identify key trends and research gaps. The findings indicate that Grad-CAM, LIME, and SHAP are the most commonly used XAI techniques, providing visual and feature-level explanations that support model interpretability. Despite these advancements, significant challenges persist, including high computational cost, lack of standardized evaluation frameworks, and inconsistencies in explanation quality. Furthermore, the reliance on limited and curated datasets restricts model generalization under real-world conditions. The review highlights a critical gap between high-performing experimental models and their practical deployment in agriculture. Future research should focus on developing diverse datasets, lightweight and interpretable models, and standardized evaluation strategies to enable robust, transparent, and scalable AI systems for sustainable agriculture.