Empowering Smart Irrigation : Predictive Soil Moisture Modeling for IoT - Driven Solutions

Abstract

In recent years, the fields of transportation, environment, business, and agriculture have witnessed revolutionary advancements with the introduction of Internet of Things. Agriculture, in particular, has benefited from the optimization of irrigation water usage through IoT and machine learning technologies. This integration has enabled the development of smart irrigation systems that can efficiently manage and optimize water usage based on real-time data and predictive analytics. The paper describes the use of machine learning strategies for refining irrigation practices through the Fore tend of future soil water content(SWC) within an Internet of Things-enabled smart irrigation system. Data gathered from sensors in the fiel¬¬¬d (which measure air temperature, humidity, SWC, soil temperature) company with Climate pre prophecy information obtained from the cyberspace (webapp) are employed to forecast forthcoming soil water content levels. For the purpose of Measure forthcoming soil water content, a variety of machine learning algorithms are examined, and the GBRTM findings are rather positive. The advised methods may represent an important area of research for irrigation water optimization.