An intelligent irrigation system based on the Internet of Things and multimodal Artificial Intelligence.

Abstract

The present invention relates to a smart hybrid irrigation system designed to optimally manage irrigation needs in rural and urban environments, even with intermittent internet connectivity. The system combines environmental sensors (temperature, relative humidity, and soil moisture) with a local processing unit and a cloud platform. In local mode, it uses a machine learning model to estimate reference evapotranspiration (ETO) and predict crop transpiration (ETC) from local data. In cloud mode, it leverages meteorological data from an API and applies the Penman-Monteith equation for accurate ETO and ETC calculations. The system automatically switches between local and cloud processing depending on the internet connection quality and synchronizes the data when the connection is restored.This innovation aims to improve water resource management by reducing consumption and ensuring appropriate irrigation, particularly in regions with limited connectivity.