Explainable AI Model for Automatic Irrigation System using IOT

This paper presents an explainable artificial intelli-gence (XAI) model for automatic irrigation systems using Inter-net of Things (IoT) technology. The proposed system integrates soil moisture sensors, temperature sensors, and humidity sensors with a microcontroller to make data-driven irrigation decisions. A Random Forest classifier i s employed top redict irrigation requirements based on environmental parameters, with SHAP (SHapley Additive exPlanations) values providing transparency and interpretability of model decisions. The system demonstrates a 35 % reduction in water usage and a 20 % increase in crop yield compared to traditional irrigation methods. The integration of explainability enhances user trust and system adoption, making advanced agricultural technology accessible to farmers with varying technical expertise. Experimental results show that the Random Forest model outperforms XGBoost and SVM in accuracy (75%), precision (100% for non-irrigation decisions), and robustness for irrigation management.