A method for predicting field specific crop yield recommendations for a field may be accomplished using a server computer system that is configured and programmed to receive over a digital communication network, electronic digital data representing agricultural data records, including remotely sensed spectral property of plant records and soil moisture records. Using digitally programmed data record aggregation instructions, the computer system is programmed to receive digital data representing including remotely sensed spectral property of plant records and soil moisture records. Using the digitally programmed data record aggregation instructions, the computer system is programmed to aggregate the one or more digital agricultural records to create and store, in computer memory, one or more geo-specific time series over a specified time. Using the digitally programmed data record aggregation instructions, the computer system is programmed to select one or more representative features from the one or more geo-specific time series and create, for each specific geographic area, a covariate matrix in computer memory comprising the representative features selected from the one or more geo-specific time series. Using mixture linear regression instructions, the computer system is programmed to assign a probability value to a component group in a set of parameter component groups, where each component group within the set of parameter component groups includes one or more regression coefficients calculated from a probability distribution and an error term calculated from a probability distribution. Using distribution generation instructions, the computer system is programmed to generate the probability distributions used to determine the one or more regression coefficients and the error term, the probability distribution used to generate the error term is defined with a mean parameter set at zero and a variance parameter set to a field specific bias coefficient.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: using data record aggregation instructions in a server computer system, receiving one or more agricultural data records that represent a type of covariate data value for plants at a specific geo-location at a specific time, wherein the type of covariate data value includes at least one of a remotely sensed spectral property of plant records using a particular spectral bandwidth range, and a soil moisture record; using the data record aggregation instructions, aggregating the one or more agricultural data records to create and store, in computer memory, one or more geo-specific time series over a specified time; using the data record aggregation instructions, selecting one or more representative features from the one or more geo-specific time series and creating, for each specific geographic area, a covariate matrix in computer memory comprising the representative features selected from the one or more geo-specific time series; and using crop yield estimating instructions in the server computer system, determining a field specific crop yield for a specific date by using linear regression instructions to calculate the field specific crop yield from the covariate matrix that represents the specific field, one or more regression coefficients calculated from a probability distribution generated by distribution generation instructions, and an error term calculated from a probability distribution generated by distribution generation instructions where a mean parameter is zero and a variance parameter is a field specific bias coefficient. 2. The method of claim 1, further comprising aggregating the one or more agricultural data records to create one or more geo-specific time series by selecting data values from the one or more agricultural data records based upon a vegetation index, wherein a vegetation index is a combination of one or more wavelength ranges of remotely sensed spectral properties. 3. The method of claim 1, further comprising creating one or more geo-specific time series by applying locally weighted scatterplot smoothing to a subset of the one or more geo-specific time series. 4. The method of claim 1, further comprising selecting one or more representative features from one or more geo-specific time series by selecting a maximum value of the geo-specific time series and a mean value of a selected portion of the geo-specific time series. 5. The method of claim 1, further comprising determining a field specific crop yield for a specific date further by determining a prediction interval associated with the field specific crop yield, wherein the prediction interval is range of values that measures a level of certainty associated with the field specific crop yield. 6. A method comprising: using data record aggregation instructions in a server computer system, receiving one or more agricultural data records that represent a type of covariate data value for plants at a specific geo-location at a specific time, wherein the type of covariate data value includes at least one of a remotely sensed spectral property of plant records using a particular spectral bandwidth range, and a soil moisture record; using the data record aggregation instructions, aggregating the one or more agricultural data records to create and store, in computer memory, one or more geo-specific time series over a specified time; using the data record aggregation instructions, selecting one or more representative features from the one or more geo-specific time series and creating for each specific geographic area a covariate matrix in computer memory comprising the representative features selected from the one or more geo-specific time series; using mixture linear regression instructions in the server computer system, assigning a probability value to a parameter component group in a set of parameter component groups, wherein each component group within the set of parameter component groups comprises: one or more regression coefficients calculated from a probability distribution generated using distribution generation instructions in the server computer system; an error term calculated from a probability distribution generated by distribution generation instructions where a mean parameter is zero and a variance parameter is a field specific bias coefficient; and using crop yield estimating instructions in the server computer system, determining a field specific crop yield for a specific date by using mixture linear regression instructions to calculate the field specific crop yield based upon the covariate matrix and the probability values assigned to each parameter component group in the set of parameter component groups. 7. The method of claim 6, further comprising assigning probability values to a parameter component groups in a set of parameter component groups based upon historical data values related to a specific geo-location of interest. 8. The method of claim 6, further comprising assigning probability values to a parameter component groups in a set of parameter component groups based upon historical data values related to multiple measured crop fields. 9. The method of claim 6, further comprising aggregating the one or more agricultural data records to create one or more geo-specific time series by selecting data values from the one or more agricultural data records based upon a vegetation index, wherein a vegetation index is a combination of one or more wavelength ranges of remotely sensed spectral properties. 10. The method of claim 6, further comprising creating one or more geo-specific time series by applying locally weighted scatterplot smoothing to a subset of the one or more geo-specific time series and selecting median values from for each time interval to create the one or more geo-specific time series. 11. The method of claim 6, further comprising selecting one or more representative features from the one or more geo-specific time series by selecting a maximum value of the geo-specific time series and a mean value of a selected portion of geo-specific time series. 12. The method of claim 6, further comprising determining a field specific crop yield for a specific date further by determining a prediction interval associated with the field specific crop yield, wherein the prediction interval is range of values that measures a level of certainty associated with the field specific crop yield. 13. A method comprising: using data record aggregation instructions in a server computer system, receiving one or more agricultural data records that represent a type of covariate data value for plants at a specific geo-location at a specific time, wherein the type of covariate data value includes at least one of a remotely sensed spectral property of plant records using a particular spectral bandwidth range, and a soil moisture record; using the data record aggregation instructions, aggregating the one or more agricultural data records to create and store, in computer memory, one or more geo-specific time series over a specified time; using the data record aggregation instructions, selecting one or more representative features from the one or more geo-specific time series and creating, for each specific geographic area, a covariate matrix in computer memory comprising the representative features selected from the one or more geo-specific time series; and using crop yield estimating instructions in the server computer system, determining a field specific crop yield for a specific date by using linear regression instructions to calculate the field specific crop yield from the covariate matrix that represents the specific field, one or more regression coefficients calculated from a probability distribution generated by distribution generation instructions, and an error term calculated from a probability distribution generated by distribution generation instructions where a mean parameter is zero and a variance parameter is a field specific bias coefficient. 14. The method of claim 13, further comprising aggregating the one or more agricultural data records to create one or more geo-specific time series by selecting data values from the one or more agricultural data records based upon a vegetation index, wherein a vegetation index is a combination of one or more wavelength ranges of remotely sensed spectral properties. 15. The method of claim 13, further comprising creating one or more geo-specific time series by applying locally weighted scatterplot smoothing to a subset of the one or more geo-specific time series. 16. The method of claim 13, further comprising selecting one or more representative features from one or more geo-specific time series by selecting a maximum value of the geo-specific time series and a mean value of a selected portion of the geo-specific time series. 17. The method of claim 13, further comprising determining a field specific crop yield for a specific date further by determining a prediction interval associated with the field specific crop yield, wherein the prediction interval is range of values that measures a level of certainty associated with the field specific crop yield. 18. A data processing system comprising: a memory; one or more processors coupled to the memory; data record aggregation instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to receive one or more agricultural data records that represent a type of covariate data value for plants at a specific geo-location at a specific time, wherein the type of covariate data value includes at least one of a remotely sensed spectral property of plant records using a particular spectral bandwidth range, and a soil moisture record; the data record aggregation instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to aggregate the one or more agricultural data records to create and store, in computer memory, one or more geo-specific time series over a specified time; the data record aggregation instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to select one or more representative features from the one or more geo-specific time series and creating for each specific geographic area a covariate matrix in computer memory comprising the representative features selected from the one or more geo-specific time series; mixture linear regression instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to assign a probability value to a parameter component group in a set of parameter component groups, wherein each component group within the set of parameter component groups comprises: one or more regression coefficients calculated from a probability distribution generated using distribution generation instructions stored in memory, executed by the one or more processors; an error term calculated from a probability distribution generated by distribution generation instructions stored in memory, executed by the one or more processors, where a mean parameter is zero and a variance parameter is a field specific bias coefficient; and crop yield estimating instructions stored in memory, executed by the one or more processors, and configured to cause the one or more processors to determine a field specific crop yield for a specific date by using mixture linear regression instructions to calculate the field specific crop yield based upon the covariate matrix and the probability values assigned to each parameter component group in the set of parameter component groups. 19. The data processing system of claim 18, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause assignment of probability values to a parameter component group in a set of parameter component groups based upon historical data values related to a specific geo-location of interest. 20. The data processing system of claim 18, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause assignment of probability values to a parameter component group in a set of parameter component groups based upon historical data values related to multiple measured crop fields. 21. The data processing system of claim 18, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause aggregation of the one or more agricultural data records to create one or more geo-specific time series by selecting data values from the one or more agricultural data records based upon a vegetation index, wherein a vegetation index is a combination of one or more wavelength ranges of remotely sensed spectral properties. 22. The data processing system of claim 18, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause creation of one or more geo-specific time series by applying locally weighted scatterplot smoothing to a subset of the one or more geo-specific time series and selecting median values from for each time interval to create the one or more geo-specific time series. 23. The data processing system of claim 18, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause selection of one or more representative features from the one or more geo-specific time series by selecting a maximum value of the geo-specific time series and a mean value of a selected portion of geo-specific time series. 24. The data processing system of claim 18, further comprising instructions stored in memory, executed by the one or more processors, and configured to cause determination of a field specific crop yield for a specific date further by determining a prediction interval associated with the field specific crop yield, wherein the prediction interval is range of values that measures a level of certainty associated with the field specific crop yield.