Enhancing Predictive Maintenance in Smart Agriculture using Explainable Artificial Intelligence

The integration of Artificial Intelligence (AI) in Smart Agricultural Facilities (SAF) enhances efficiency but often lacks transparency, limiting its adoption by farmers. This study introduces a Predictive Maintenance (PdM) model powered by Explainable Artificial Intelligence (XAI) to improve both predictive accuracy and interpretability. The proposed model offers explanations across four key dimensions: data, model, outcome, and end-user, ensuring better understanding and usability for stakeholders. Experimental results demonstrate that the Long Short-Term Memory (LSTM) classifier improves accuracy by 5.81%, while the eXtreme Gradient Boosting (XGBoost) classifier achieves a 7.09% increase in F1 score, 10.66% higher accuracy, and a 4.29% improvement in ROC-AUC. These enhancements lead to more precise maintenance predictions, reducing costs and improving reliability in SAF. Additionally, this study highlights data integrity, global and local model explanations, and counterfactual reasoning to enhance transparency in AI-driven PdM. By emphasizing interpretability beyond conventional accuracy metrics, this research contributes to advancing trustworthy AI applications in agriculture. Future research should explore multi-modal data integration and Human-in-the-Loop (HITL) systems to address ethical concerns such as Fairness, Accountability, and Transparency (FAT) in AI-driven agricultural technologies