IoT-Dependent Intelligent Irrigation System with ML-Dependent Soil Moisture Prediction

IoT and ML-dependent solutions are revolutionizing various facets of human endeavor, including dealings, agriculture, the ecology and transportation. Numerous nations consume excessive amounts of the already scarce fresh water resources. The combination of machine learning and the Internet of Things may be utilized to optimize irrigation water utilization. In an IoT -directed intelligent irrigation system, this study shows how to use ML algorithms to anticipate the future soil moisture in a farm in order to optimize irrigation water consumption. Future soil moisture is estimated utilizing farm data from the installed sensors (atmospheric temperature, atmospheric humidity, soil moisture, soil temperature, radiation), as well as online weather prediction information. The effectiveness of several ML methods for estimating future soil moisture is examined, and the GBRT findings are encouraging. The suggested methods could be a key area of investigation for maximizing irrigation water use.