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    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, D
    Agriculture 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.
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    Predictive Modelling of Egg Production Yields on Farms based on Environmental Factors
    (Institute of Electrical and Electronics Engineers Inc., 2025) Nawod G.A.D; Rathnayake R.M.D.A.; Dodangoda P.N; Deshitha N.A.M.P; Vidanaralage A.J; Vidanaralage A.J
    This research presents an integrated smart farming system aimed at optimizing egg yield on poultry farms by leveraging artificial intelligence (AI), Internet of Things (IoT), and environmental sensing technologies. The system is structured around four core components - Animal Stress Monitoring, Temperature Control and Predator Detection, Humidity and Ventilation Management, and AI-Driven Smart Lighting Optimization each contributing to real-time environmental adaptation and accurate egg production prediction. Animal stress is assessed using physiological and environmental metrics (e.g., heart rate, body temperature, feed/water intake), with predictions generated via an XGBoost model trained on 3000+ real farm entries. Temperature and security are managed through a hybrid system combining DHT11/DHT22-based climate control with YOLO-based computer vision for predator detection. The humidity and ventilation module incorporates Bi-LSTM and XGBoost models to predict and regulate airflow and moisture levels based on real-time sensor inputs. The lighting optimization component dynamically adjusts LED spectrum and intensity using LSTM-based forecasting models, operating via ESP32 and MQTT-enabled architecture to simulate ideal lighting conditions. These components are unified through a.NET-based backend and a mobile-friendly dashboard, enabling low-latency decision support and seamless farm management. The system's modularity, edge deployment capabilities, and adaptability to local conditions make it an innovative and scalable approach for enhancing egg yield, poultry welfare, and farm automation.