Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities

Smart agricultural facilities integrate advanced technologies such as IoT sensors, robotics, and automated machinery to optimize crop production and resource management. However, the complexity and scale of these systems necessitate efficient maintenance to prevent equipment failure, minimize downtime, and ensure high productivity. Traditional maintenance strategies like reactive or time-based approaches are no longer sufficient. This paper proposes an Explainable Artificial Intelligence (XAI) model for predictive maintenance in smart agriculture. The model leverages machine learning algorithms to predict potential equipment failures by analyzing sensor data, usage patterns, and environmental conditions. Crucially, it incorporates explainability mechanisms—such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations)—to provide transparent insights into model predictions, enhancing trust and enabling informed decision-making by farm operators. We validate our approach using real-world data from greenhouse machinery and irrigation systems, demonstrating improved fault prediction accuracy and interpretability over black-box models. This work represents a step toward smarter, safer, and more transparent maintenance strategies in precision agriculture.