Vanam Gopinath 02 7(1) Artificial Intelligence AI in Smart Agricultural Facilities SAF often lacks explainability hindering farmers from taking full advantage of their capabilities This study tackles this gap by introducing a model that combines eXplainable Artificial Intelligence XAI with Predictive Maintenance PdM The model aims to provide both predictive insights and explanations across four key dimensions namely data model outcome and enduser This approach marks a shift in agricultural AI reshaping how these technologies are understood and applied The model outperforms related studies showing quantifiable improvements Specifically the LongShortTerm Memory LSTM classifier shows a 581 rise in accuracy The eXtreme Gradient Boosting XGBoost classifier exhibits a 709 higher F1 score 1066 increased accuracy and a 429 increase in Receiver Operating CharacteristicArea Under the Curve ROCAUC These results could lead to more precise maintenance predictions in realworld settings This study also provides insights into data purity global and local explanations and counterfactual scenarios for PdM in SAF It advances AI by emphasising the importance of explainability beyond traditional accuracy metrics The results confirm the superiority of the proposed model marking a significant contribution to PdM in SAF Moreover this study promotes the understanding of AI in agriculture emphasising explainability dimensions Future research directions are advocated including multimodal data integration and implementing HumanintheLoop HITL systems aimed at improving the effectiveness of AI and addressing ethical concerns such as Fairness Accountability and Transparency FAT in agricultural AI applications Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026