Abstract: A method for determining management zones within an agricultural field, the method includes selecting a plurality of remotely sensed images of the agricultural field wherein the plurality of remotely sensed images represent a plurality of growing seasons, each of the plurality of remotely sensed images having a vegetation index associated therewith, generating a refined average image from the plurality of remotely sensed images of the agricultural field, and applying a classification method to define management zones associated with the refined average image.

Abstract: A yield model generates a yield map for an agricultural field. A measurement system generates measured indicators that are a measurement or quantification of crop yield in the agricultural field. An observation system generates observed indicators that are spatial agricultural datasets describing observed characteristics of the agricultural field. To generate the yield map, the yield model generates a field array representing the agricultural field. The yield model generates an input array and a yield array by mapping the observed indicators and measured indicators to cells of the field array, respectively. The yield model determines a yield value for each cell of the yield array not including a mapped indicator using information included in the corresponding cells of the input array. The yield model generates a yield map using the determined yield values and the yield values in the yield array.

Abstract: A method and system for crop health change monitoring is provided. The method includes acquiring a companion image of a crop growing within a field at a first point in time, acquiring a master image of the crop growing within the field at a second point in time, and computing, using a processor, vegetation indices using the master image and the companion image, determining, using the processor, regions of change within the master image using the vegetation indices and generating an alert indicative of a change in crop condition of the crop growing within the field, and communicating the alert indicative of the change in crop condition over a network to a computing device configured to receive the alert.

Abstract: A method for determining management zones within an agricultural field, the method includes selecting a plurality of remotely sensed images of the agricultural field wherein the plurality of remotely sensed images represent a plurality of growing seasons, each of the plurality of remotely sensed images having a vegetation index associated therewith, generating a refined average image from the plurality of remotely sensed images of the agricultural field, and applying a classification method to define management zones associated with the refined average image.

Abstract: A method and system for crop health change monitoring is provided. The method includes acquiring a companion image of a crop growing within a field at a first point in time, acquiring a master image of the crop growing within the field at a second point in time, and computing, using a processor, vegetation indices using the master image and the companion image, determining, using the processor, regions of change within the master image using the vegetation indices and generating an alert indicative of a change in crop condition of the crop growing within the field, and communicating the alert indicative of the change in crop condition over a network to a computing device configured to receive the alert.

Abstract: Embodiments relate to calibrating yield data points. A calibration system receives a plurality of groups of yield data points. Each group is associated with an attribute, such as, a machine identifier, a zone identifier, a localized zone identifier, and/or a moisture identifier. The system calculates a grand aggregate yield based on the yield data points of the plurality of groups. The system calculates a group aggregate yield based on yield data points of each group. The system subtracts the group aggregate yield from each yield data point of the group producing adjusted yield data points. The system adds the grand aggregate yield to each of the adjusted yield data points producing calibrated yield data points.

Abstract: A yield model generates a yield map for an agricultural field. A measurement system generates measured indicators that are a measurement or quantification of crop yield in the agricultural field. An observation system generates observed indicators that are spatial agricultural datasets describing observed characteristics of the agricultural field. To generate the yield map, the yield model generates a field array representing the agricultural field. The yield model generates an input array and a yield array by mapping the observed indicators and measured indicators to cells of the field array, respectively. The yield model determines a yield value for each cell of the yield array not including a mapped indicator using information included in the corresponding cells of the input array. The yield model generates a yield map using the determined yield values and the yield values in the yield array.

Abstract: Embodiments relate to calibrating yield data points. A calibration system receives a plurality of groups of yield data points. Each group is associated with an attribute, such as, a machine identifier, a zone identifier, a localized zone identifier, and/or a moisture identifier. The system calculates a grand aggregate yield based on the yield data points of the plurality of groups. The system calculates a group aggregate yield based on yield data points of each group. The system subtracts the group aggregate yield from each yield data point of the group producing adjusted yield data points. The system adds the grand aggregate yield to each of the adjusted yield data points producing calibrated yield data points.