Abstract—A novel approach to irrigation modeling is presented: the locale specific machine learning of soil moisture data. The merits of this new patent pending technique are clear when compared to existing methods, such as the AquaCrop program created by the Food and Agricultural Organization (FAO). From a case study on the comparative performance of AquaCrop and machine learning in the extrapolative modeling of soil moisture, AquaCrop performed with a mean squared error of 0.00165 whereas the machine learning received 0.00013, an order of magnitude lower. In addition, a novel algorithm, the ConserWater™ algorithm, has been created for the purpose of machine learning soil moisture with accuracy and efficiency. The performance of the algorithm is very superior when compared to other popular machine learning techniques, as applied to soil moisture. Finally, to allow this technology to reach agriculturalists at the grassroots level, the entire world has been machine learned and the resultant models have been encapsulated into a lightweight easy-to-use smartphone application.
Keywords— ConserWater™, irrigation, machine learning, soil moisture.
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