Helio Harvest: A Dual-Mode Solar Energy and Rainwater Collection System with ML-Based Water Quality

Abstract: The scarcity of water and energy continues to be a major obstacle for sustainable agriculture, necessitating integrated and cost-effective solutions. This project introduces a hybrid framework that incorporates rainwater harvesting, solar energy utilisation, and intelligent automation to optimise resource use in agricultural fields. The cube-structured solar panel system is designed to maximise space efficiency by simulta- neously harvesting rainwater and generating renewable energy. The collected water is filtered and stored in designated tanks, while additional runoff is diverted for future use. A machine learning model predicts water quality in real-time to ensure its safe and effective utilisation by analysing parameters such as pH, turbidity, conductivity, and microbial content. These predictions power an Arduino-based control system that automatically routes water to the field for irrigation or heating for domestic and sterilisation purposes using solar energy. By integrating harvest- ing, prediction and automation, the system increases agricultural sustainability and reduces dependence on conventional resources, and also promises to be highly scalable for both rural and urban applications.

Keywords: Rainwater Harvesting, Solar Energy, Machine Learning, Water Quality Prediction, IoT, Arduino, Sustainable Agriculture