Low-cost smart irrigation solution for efficient water use and requirement prediction

The agricultural sector in India consumes a substantial amount of water annually, with precipitation primarily concentrated during the monsoon season and irrigation needs varying significantly throughout the year. To address these challenges, this study presents a fully automated, intelligent irrigation control system that integrates low-cost sensors (temperature, humidity, soil moisture, and illumination) with a microcontroller within an Internet of Things framework. The system effectively regulates irrigation and provides precise seasonal and short-term water requirement forecasts using computationally efficient data analytics. Featuring a user-friendly graphical interface, the prototype was developed and tested in both scaled alpha and beta environments. Designed with sustainability, scalability, and international applicability in mind, the system demonstrates its ability to adapt to seasonal changes and achieves 94.3% prediction accuracy for real-time environmental monitoring through machine learning-based water demand forecasting. The results confirm its capability to manage water flow via automated pump control and deliver accurate forecasts, highlighting its potential to enhance water management in agriculture.