Optimizing Agricultural Irrigation through Real-Time Data Prediction

Sustainable agriculture requires the use of effective water management techniques to maximise agricultural production while also preserving resources. Real-time data analytics offer new opportunities to discover this balance by enabling precise irrigation forecasts customised to the specific environmental circumstances at any given moment. This research seeks to provide a novel approach for determining irrigation needs by utilising real time data. The system considers environmental elements such as soil moisture, temperature, and precipitation forecasts. The programme utilises the K-Nearest Neighbours (KNN) algorithm, which is a machine learning technique, to deliver real-time irrigation data to farmers. This data assists farmers in optimising crop output while minimising water wastage. Our method enables farmers to employ Internet of Things (IoT) sensors, cloud computing, and predictive analytics to create precise irrigation systems that enhance the durability and sustainability of agricultural operations.