MACHINE LEARNING-DRIVEN PREDICTIVE ANALYTICS FOR OPTIMIZING WATER MANAGEMENT IN SMART IRRIGATION SYSTEMS

Abstract

Modern agriculture relies heavily on water management, and because smart irrigation systems have the ability to maximize crop yields while optimizing water consumption, they have attracted a lot of interest. With predictive analytics, farmers can make data-driven decisions based on both historical and real-time data, which is why predictive analytics is so important to smart irrigation systems. Conventional irrigation techniques frequently depend on manual observations or set timetables, which may not fully reflect the water needs of crops. Furthermore, some current smart irrigation systems allocate water in an inefficient manner because they employ rule-based strategies that only take into account the most fundamental environmental aspects. These approaches might not completely utilize predictive analytics' promise and might not adjust adequately to shifting environmental conditions. In this work, we provide a predictive analytics method that uses machine learning algorithms with temperature and humidity data obtained from Node-MCU to optimize water management in smart irrigation systems. The technology predicts the best irrigation plan for each crop by using the learned machine learning models to estimate future water requirements based on real-time data.