Fecha de publicación: -- Fuente: Wipo "precision agriculture" Systems and methods that use geospatial data to train machine learning models to make predictions based on the geospatial data, for example for use in precision agriculture. A database is created that includes agricultural geospatial data acquired from two or more data sources. Using the spatial and temporal extent of response data, a training vector and a training feature matrix are then generated using a data systematic approach to engineer a virtually infinite number of training features derived from the database. A plurality of machine learning models are then trained using the training vector and the training feature matrix, with each one of the trained machine learning models generating a test result. At least one of the trained machine learning models is chosen to create predictions using new data at the spatial and temporal extent of interest. An output is created using the prediction(s) on the new data.