This section is responsible for calculating the optimum amount of water for crop health which is supplied at an hourly rate. We make use of the soil data from the IOT devices and records of weather data from weather APIs and Google Earth Engine. Google Earth Engine allows us to access weather data and climate conditions of a specified location from previous years through various satellites. This greatly helps us in designing algorithms using machine learning principles.
The model from these algorithms predicts the optimum amount of water required by the crop for its healthy growth. Since the entire network from the irrigation end through the backend is automated, no more than the required amount of water is supplied, which fascilitates water management.
We also make use of the Penman-Monteith equation that calculates hourly evapotranspiration rate of a crop based on weather and soil paramters. The hourly loss of water by evapotranspiration is supplied constantly so that cereal crops would never fall short of the minimum amount of water required.