AI POWERED DYNAMIC IRRIGATION SCHEDULING USING REAL TIME WEATHER DATA

Abstract

A dynamic irrigation scheduling system that optimizes crop output and water utilization by utilizing real-time weather data and AI is described in this paper. The proposed system encompasses machine learning algorithms, sensors, and Internet of Things devices. We will closely monitor environmental variables such as soil moisture, temperature, humidity, and anticipated rainfall. Based on these inputs, the system will autonomously determine the irrigation water requirements, ensuring that neither an excessive or insufficient amount of water is wasted. This system is designed to adapt to changing weather patterns and crop requirements in order to guarantee that resources are utilized efficiently and agricultural methods are durable. This system is more effective than traditional irrigation methods in intelligent agricultural applications due to its increased decision-making capacity, reduced water consumption, and increased crop productivity, as demonstrated by experimental data.