Predictive AI-Based Solar Irrigation System Using ESP32 for Water Conservation

Abstract

Agricultural production is dependent on a large amount of freshwater resources consumed worldwide, thus managing irrigation efficiently can create sustainable agriculture. Traditional irrigation methods generally rely on fixed schedules with no consideration given to the actual environmental and soil conditions present, resulting in the waste of water. In this study, we have built a Predictive AI Based Solar Irrigation System capable of intelligent water management using the ESP32 microcontroller. This irrigation management system utilizes soil moisture, soil temperature and soil humidity sensors to continuously monitor on-field conditions. This is done by means of a lightweight AI-based algorithm that calculates a dryness score using the data collected from the soil condition sensors and the ESP32 will use this score to predict the appropriate irrigation amount needed and will control the DC pump. The solar energy subsystem supplies the power for the entire system and enables the system to operate independent of the electrical power distribution system. All sensor data and irrigation status are uploaded to the ThingSpeak Cloud Platform which is made visible through the Streamlit dashboard. Our experimental results show significant water savings, improved efficiency of irrigation, and reliable performance in many different weather and climate conditions. Overall, the described system is a low-cost, sustainable, and versatile agribusiness solution for smart agriculture and can be easily scaled up.