Abstract: In an approach for identifying a crop using satellite observation data and crop rotation data, a processor receives an aerial image of one or more agricultural fields in a pre-determined geographical region from an optical satellite. A processor selects a plurality of points from the aerial image. A processor clips the aerial image into one or more smaller images to generate one or more observed signatures. A processor determines there is a change in a spatial distribution of one or more crops. A processor generates an actual signature from the one or more observed signatures. A processor cross-correlates the actual signature against a plurality of historical reference signatures to determine a degree of similarity. A processor ranks one or more results of the cross-correlation. A processor identifies the one or more crops. A processor calculates a first estimate of an amount of the one or more crops.

Abstract: A method of predicting crop yield includes generating, via a processor, a plurality of vectors representative of growing conditions for a current time period and a plurality of vectors representative of growing conditions for a previous time period. The processor compares the plurality of vectors for the current time to the vectors of the previous time periods for corresponding growing conditions and determines which previous vectors are closest to the current vectors. The plurality of previous time periods are each associated with crop yields. Thus, the processor can determine a crop yield for the current time period for a selected crop producing field and crop type based on crop yields for the closest previous time periods.

Abstract: In an approach for determining the date of planting of a crop growing in an agricultural field, a processor receives an aerial image of one or more agricultural fields in a pre-determined geographical region. A processor selects a plurality of points from the aerial image. A processor calculates a Vegetation Index of one or more crops growing at the plurality of points selected. A processor compares the Vegetation Index calculated for the one or more crops growing at the plurality of points selected to the Vegetation Index known for a plurality of historical reference signatures. A processor generates an actual signature. A processor cross-correlates the actual signature against the plurality of historical reference signatures to measure a degree of similarity. A processor identifies the one or more crops growing in the one or more agricultural fields in the pre-determined geographical region from the cross-correlation.

Abstract: In an approach for identifying a crop using satellite observation data and crop rotation data, a processor receives an aerial image of one or more agricultural fields in a pre-determined geographical region from an optical satellite. A processor selects a plurality of points from the aerial image. A processor clips the aerial image into one or more smaller images to generate one or more observed signatures. A processor determines there is a change in a spatial distribution of one or more crops. A processor generates an actual signature from the one or more observed signatures. A processor cross-correlates the actual signature against a plurality of historical reference signatures to determine a degree of similarity. A processor ranks one or more results of the cross-correlation. A processor identifies the one or more crops. A processor calculates a first estimate of an amount of the one or more crops.

Abstract: A method of predicting crop yield includes generating, via a processor, a plurality of vectors representative of growing conditions for a current time period and a plurality of vectors representative of growing conditions for a previous time period. The processor compares the plurality of vectors for the current time to the vectors of the previous time periods for corresponding growing conditions and determines which previous vectors are closest to the current vectors. The plurality of previous time periods are each associated with crop yields. Thus, the processor can determine a crop yield for the current time period for a selected crop producing field and crop type based on crop yields for the closest previous time periods.

Abstract: Temperature and Rainfall modeling for crop management in a geographical space are presented. One example includes obtaining rainfall information and temperature information for a geographic area, wherein the geographic area includes the geographical space. A model representing a relationship between rainfall and ambient temperature for the geographic area is generated based on the obtained rainfall information and temperature information. A temperature threshold for the geographical space is determined based on the generated model, where the temperature threshold is for identifying a crop planting or production condition.

Abstract: Temperature and Rainfall modeling for crop management in a geographical space are presented. One example includes obtaining rainfall information and temperature information for a geographic area, wherein the geographic area includes the geographical space. A model representing a relationship between rainfall and ambient temperature for the geographic area is generated based on the obtained rainfall information and temperature information. A temperature threshold for the geographical space is determined based on the generated model, where the temperature threshold is for identifying a crop planting or production condition.

Abstract: Concepts for crop seeding recommendation for a geographical space are presented. One example comprises determining weather information for the geographical space for a predetermined timeframe prior to a planned planting date. A recommended seed planting amount for the geographical space is determined based on the weather information.

Abstract: Concepts for crop recommendation for a geographical space are presented. One example comprises determining a seed treatment recommendation based on at least one of: location data relating to the geographical space; economic data relating to one or more crop types; treatment data relating to the one or more crop types; and crop data relating to the one or more crop types. A recommended crop type is determined based on the seed treatment recommendation. Weather information for the geographical space for a predetermined timeframe prior to a planned planting date is also determined. A seed planting amount is determined based on the recommended crop type and the weather information.

Abstract: A marketing method and system. The method includes receiving by a computing system total population data. A peer group is selected from the total population data. The peer group is associated with a first marketing action. A list of attributes characterizing the peer group is generated. The attributes are associated with the marketing action, demographic data, and additional purchasing habits of the peer group. A target group of individuals of the total population data is selected. The attributes are associated with the individuals of the target group. Scores associated with the individuals of the target group are generated. The individuals of the target group are ranked based on said scores. A ranking list based is generated. The ranking list includes the individuals of the target group in a ranked order. The ranking list is stored.

Abstract: A marketing method and system. The method includes receiving by a computing system total population data. A peer group is selected from the total population data. The peer group is associated with a first marketing action. A list of attributes characterizing the peer group is generated. The attributes are associated with the marketing action, demographic data, and additional purchasing habits of the peer group. A target group of individuals of the total population data is selected. The attributes are associated with the individuals of the target group. Scores associated with the individuals of the target group are generated. The individuals of the target group are ranked based on said scores. A ranking list based is generated. The ranking list includes the individuals of the target group in a ranked order. The ranking list is stored.

Abstract: A marketing method and system. The method includes receiving by a computing system total population data. A peer group is selected from the total population data. The peer group is associated with a first marketing action. A list of attributes characterizing the peer group is generated. The attributes are associated with the marketing action, demographic data, and additional purchasing habits of the peer group. A target group of individuals of the total population data is selected. The attributes are associated with the individuals of the target group. Scores associated with the individuals of the target group are generated. The individuals of the target group are ranked based on said scores. A ranking list based is generated. The ranking list includes the individuals of the target group in a ranked order. The ranking list is stored.

Abstract: A marketing method and system. The method includes receiving by a computing system total population data. A peer group is selected from the total population data. The peer group is associated with a first marketing action. A list of attributes characterizing the peer group is generated. The attributes are associated with the marketing action, demographic data, and additional purchasing habits of the peer group. A target group of individuals of the total population data is selected. The attributes are associated with the individuals of the target group. Scores associated with the individuals of the target group are generated. The individuals of the target group are ranked based on said scores. A ranking list based is generated. The ranking list includes the individuals of the target group in a ranked order. The ranking list is stored.

Abstract: A technique for enhanced data mining of large scale relational databases is described. User defined functions (UDF) are created by a user and distributed by a managing node of a database to each node of the database. Upon the issuance of a prespecified SQL command, the UDF is executed by each node relative to the data controlled by each node. Specifically, targeted tuples in the data controlled by each node are scored based on criteria contained in the UDF. A new data field is added to each target tuple, and the score is placed therein. The score is then used to determine whether, for example, a customer which is represented by the tuple should be included in an advertising campaign or to tailor a mailing to a customer based on the score contained in the new data field.