Fuente: Wipo "precision agriculture" PRECISION AGRICULTURE: OPTIMIZING CROP YIELD THROUGH AIA method for the development of a device with one or more processors that receive first information about an emergence region, including imagery data of the emergence area, and on the basis of the first information calculate an estimated commodity quantity value. A computer-implemented method entails receiving data by a device's processor, including first and second data. The first data is obtained from many sensor devices situated on one or more farms, while the second data is obtained from one or more devices outside the one or more farms. The orchard data learnt model and the satellite model are combined in a precision agriculture management model that accurately predicts the output conditions. The vigor and/or crop production of agricultural plants are increased under virtually non-existent pathogen pressure, and the gain in vigor is characterized by the need for less fertilizer. To generate a covariate matrix for each geographical area, representative features are chosen from each series of aggregated weather index data. Based on the covariate matrix for the particular geographic area, a linear regression model is used to determine the crop yield for the area. The method calculates a specific state agricultural yield from the covariate matrix for a specific year. It does this using linear regression.FIG.1