Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), and remote sensing technologies have transformed agricultural monitoring, precision farming, and climate-resilient decision-making. However, the widespread adoption of AI-driven agricultural systems remains constrained by the black-box nature of advanced predictive models, particularly deep neural networks. Explainable Artificial Intelligence (XAI) has emerged as a critical solution for improving transparency, interpretability, accountability, and trust in AI-based agricultural remote sensing systems. This review provides a comprehensive synthesis of the recent developments in XAI applications within smart agriculture, with emphasis on interpretable remote sensing analytics and sustainable decision-making. The review discusses the evolution of AI in agriculture, major remote sensing platforms, explainability frameworks, and the integration of XAI with satellite imagery, unmanned aerial vehicles (UAVs), Internet of Things (IoT), and geospatial big data. Key agricultural applications, including crop classification, yield prediction, disease detection, soil property assessment, irrigation management, carbon monitoring, and climate adaptation, are critically evaluated. Furthermore, the review compares intrinsic and post hoc explainability methods such as attention mechanisms, saliency maps, and counterfactual explanations. The interpretation of model outputs and reported results from recent studies is discussed to demonstrate how XAI improves model reliability and stakeholder confidence. Challenges related to data heterogeneity, scalability, uncertainty, ethics, fairness, and computational complexity are also analyzed. Finally, future perspectives are presented regarding hybrid explainable frameworks, physics-informed AI, edge computing, digital twins, and trustworthy autonomous agricultural systems. The review emphasizes the central role of XAI in enabling transparent and sustainable agricultural intelligence under rapidly changing climatic and environmental conditions.
Samra et al. (Fri,) studied this question.