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Item Embargo AloeGreen: Smart IOT-Based System for Aloe Vera(Institute of Electrical and Electronics Engineers Inc., 2026-05-21) Megasooriya, E; Rajapaksha, H; Rajapaksha, H; Bandara, A; Krishara, J; Wijendra, DAgriculture plays a vital role in food security, yet Aloe vera cultivation remains vulnerable to environmental variability, nutrient imbalance, disease occurrence, and unstable market conditions. This study presented AloeGreen, a crop-specific AI-IoT smart agriculture framework designed to support Aloe vera cultivation through integrated sensing, forecasting, and decision-support modules. The system combined real-time IoT-based field monitoring with machine learning models for yield prediction, environmental forecasting, disease detection, fertilizer recommendation, and price forecasting. A key contribution of the study was a forecast-informed yield prediction strategy in which short-term environmental forecasts were incorporated into the yield estimation pipeline to support future-aware decision-making. In addition, domain-specific agronomic features, including water stress and heat stress indices, were introduced to better represent Aloe vera growth conditions. For the yield prediction module, the cleaned hourly cultivation dataset contained 1,048,330 observations after removing missing critical fields and duplicates. Experimental results showed that XGBoost achieved the best yield prediction performance with an RMSE of $\mathbf{1 0. 0 2}$ and an $\mathbf{R}^{\mathbf{2}}$ of $\mathbf{0. 8 9 2}$, while the environmental forecasting module achieved strong performance for temperature and humidity prediction, although rainfall prediction remained comparatively weaker. The disease detection module achieved balanced classification performance of approximately 77% accuracy, and Random Forest performed best in both price forecasting and fertilizer recommendation tasks. Overall, the findings showed that integrating IoT sensing with intelligent analytics in a unified Aloe vera cultivation platform can improve decision support, reduce uncertainty, and contribute to more sustainable smart agriculture practices.