Predictive Maintenance in Smart Farming Using Explainable AI: A SHAP-Centric Methodology

The objective of our proposed smart agriculture predictive maintenance deals with the important requirement of clear and effective predictive maintenance in smart farming through explainable artificial intelligence (XAI) integration. Conventional "black-box" AI models, though precise, are not interpretable enough to enable farmers to trust and utilise predictive maintenance systems effectively. The present project creates an internet-based platform for farmers where they can provide text-based databases of sensor inputs and equipment log records and, using machine learning models, in particular Random Forest and XG Boost, predict the maintenance requirement. The idea here is in utilizing SHAP (Shapley Additive explanations) that can give neat, human-readable explanations regarding predictions and a detailed description about factors that led to maintenance notifications. Through improved transparency, this paper seeks to boost the adoption of AI-powered predictive maintenance, reduce machinery downtime, and maximise resource use in smart agriculture.The proposed model is adaptable to various types of agricultural machinery, provided a relevant dataset is available, and the system can be easily extended into real-world applications to assist farmers through app-based deployment.The proposed model is adaptable to various types of agricultural machinery, provided a relevant dataset is available, and the system can be easily extended into real-world applications to assist farmers through app-based deployment.