AI-POWERED SOLAR-DRIVEN SMART IRRIGATION SYSTEMS FOR CLIMATE-RESILIENT SMALLHOLDER FARMING IN NIGERIA

Abstract

Water use efficiency remains a major challenge in agricultural production, particularly in regions where irrigation practices are largely manual and dependent on unpredictable environmental conditions. This study developed an artificial intelligence powered, solar driven smart irrigation system designed to optimize water application using key environmental variables, namely soil moisture, temperature, and solar radiation. The system integrates sensors, a microcontroller, and a machine learning model to monitor environmental conditions and automatically determine appropriate irrigation levels. An experimental research design was adopted, and simulated data were used to evaluate system performance under varying environmental conditions. The results showed that the system effectively adjusted water application based on changes in soil moisture, temperature, and solar radiation. Lower soil moisture levels and higher temperature conditions resulted in increased irrigation, while higher soil moisture levels led to reduced or no water application. Performance evaluation revealed that the proposed system significantly reduced water usage compared to traditional irrigation methods. The system achieved notable water savings and improved efficiency, particularly under conditions of high soil moisture where unnecessary irrigation was eliminated. These findings demonstrate the effectiveness of integrating artificial intelligence with renewable energy for sustainable irrigation management. The study concludes that AI powered smart irrigation systems provide a reliable and efficient solution for improving water use efficiency and agricultural productivity. The system is particularly suitable for smallholder farmers in Nigeria and other developing regions facing water and energy challenges.