PREDICTIVE ANALYTICS FOR OPTIMAL WATER MANAGEMENT IN SMART IRRIGATION SYSTEM USING NODE-MCU DATA FOR SMART IRRIGATION

Abstract

Water management is a crucial aspect of modern agriculture, and the adoption of smart irrigation systems has gained significant attention due to its potential to optimize water usage while maximizing crop yields. Predictive analytics plays a vital role in smart irrigation systems, enabling farmers to make data-driven decisions based on real-time and historical data.  Traditional irrigation methods often rely on fixed schedules or manual observations, which may not accurately represent the actual water requirements of crops. Additionally, some existing smart irrigation systems use rule-based approaches that consider only basic environmental factors, potentially leading to suboptimal water allocation. These methods may not adapt well to changing environmental conditions and may not fully exploit the potential of predictive analytics.  In this study, we propose a predictive analytics approach for optimal water management in smart irrigation systems using machine learning algorithms with temperature, humidity data acquired from Node-MCU. The trained machine learning models are used to forecast future water requirements based on real-time data, allowing the system to predict the optimal irrigation schedule for each crop.