Predictive map generation and control

1. An agricultural work machine, comprising:

a communication system that receives a prior information map that includes values of an optical characteristic corresponding to different geographic locations in a field;

a geographic position sensor that detects a geographic location of the agricultural work machine;

an in-situ sensor that detects a value of an agricultural characteristic corresponding to the geographic location;

a predictive model generator that generates a predictive characteristic model that models a relationship between the optical characteristic and the agricultural characteristic based on a value of the optical characteristic in the prior information map at the geographic location and the value of the agricultural characteristic detected by the in-situ sensor corresponding to the geographic location; and

a predictive map generator that generates a functional predictive map of the field, that maps predictive values of the agricultural characteristic to the different geographic locations in the field, based on the values of the optical characteristic in the prior information map and based on the predictive characteristic model.

2. The agricultural work machine of claim 1 , wherein the predictive map generator configures the functional predictive map for consumption by a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive map.

3. The agricultural work machine of claim 1 , wherein the in-situ sensor on the agricultural work machine is configured to detect, as the value of the agricultural characteristic, a non-machine characteristic corresponding to the geographic location.

4. The agricultural work machine of claim 1 , wherein the in-situ sensor on the agricultural work machine is configured to detect, as the value of the agricultural characteristic, a machine characteristic corresponding to the geographic location.

5. The agricultural work machine of claim 1 , wherein the prior information map comprises a prior optical characteristic map that maps, as the optical characteristic, vegetative index values to the different geographic locations in the field, and wherein the predictive model generator is configured to identify a relationship between the vegetative index values and the agricultural characteristic based on the value of the agricultural characteristic detected at the geographic location and a vegetative index value, in the prior optical characteristic map, at the geographic location, the predictive characteristic model being configured to receive an input vegetative index value as a model input and generate a predicted agricultural characteristic value as a model output based on the identified relationship.

6. The agricultural work machine of claim 1 , wherein the prior information map comprises a prior optical characteristic map that maps, as the optical characteristic, texture values to the different geographic locations in the field, and wherein the predictive model generator is configured to identify a relationship between the texture values and the agricultural characteristic based on the value of the agricultural characteristic detected at the geographic location and a texture value, in the prior optical characteristic map, at the geographic location, the predictive characteristic model being configured to receive an input texture value as a model input and generate a predicted agricultural characteristic value as a model output based on the identified relationship.

7. The agricultural work machine of claim 6 , wherein at least some of the texture values are indicative of standing, downed or partially downed crop.

8. The agricultural work machine of claim 7 , wherein at least some of the texture values are indicative of a downed crop orientation.

9. The agricultural work machine of claim 6 , wherein at least some of the texture values are indicative of a species of plant.

10. The agricultural work machine of claim 1 , wherein the prior information map comprises a prior optical characteristic map that maps, as the optical characteristic, wavelength values to the different geographic locations in the field, and wherein the predictive model generator is configured to identify a relationship between wavelength and the agricultural characteristic based on the value of the agricultural characteristic detected at the geographic location and a wavelength value, in the prior optical characteristic map, at the geographic location, the predictive characteristic model being configured to receive an input wavelength value as a model input and generate a predicted characteristic value as a model output based on the identified relationship.

11. The agricultural work machine of claim 1 , further comprising an operator interface mechanism that displays the functional predictive map.

12. A computer implemented method of generating a functional predictive map, comprising:

receiving a prior information map, at an agricultural work machine, that indicates values of an optical characteristic corresponding to different geographic locations in a field;

detecting a geographic location of the agricultural work machine;

detecting, with an in-situ sensor, a value of an agricultural characteristic corresponding to the geographic location;

generating a predictive characteristic model that models a relationship between the optical characteristic and the agricultural characteristic; and

controlling a predictive map generator to generate the functional predictive map of the field, that maps predictive values of the agricultural characteristic to the different locations in the field based on the optical characteristic values in the prior information map and the predictive characteristic model.

13. The computer implemented method of claim 12 , and further comprising:

configuring the functional predictive map for a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive map.

14. The computer implemented method of claim 12 , wherein detecting, with the in-situ sensor, the value of the agricultural characteristic comprises detecting a non-machine characteristic corresponding to the geographic location.

15. The computer implemented method of claim 14 , wherein generating a predictive characteristic model comprises:

identifying a relationship between the optical characteristic values and the non-machine characteristic based on the non-machine characteristic detected at the geographic location and the optical characteristic value, in the prior information map, at the geographic location; and

controlling a predictive model generator to generate the predictive characteristic model that receives an input optical characteristic value as a model input and generates a non-machine characteristic value as a model output based on the identified relationship.

16. The computer implemented method of claim 12 , wherein detecting, with the in-situ sensor, a value of the agricultural characteristic comprises detecting a machine characteristic corresponding to the geographic location.

17. The computer implemented method of claim 16 , wherein generating a predictive characteristic model comprises:

identifying a relationship between the optical characteristic values and the machine characteristic based on the machine characteristic detected at the geographic location and the optical characteristic value, in the prior information map, at the geographic location; and

controlling a predictive model generator to generate the predictive characteristic model that receives an input optical characteristic value as a model input and generates a machine characteristic value as a model output based on the identified relationship.

18. The computer implemented method of claim 12 , further comprising:

controlling an operator interface mechanism to present the functional predictive map.

19. An agricultural work machine, comprising:

a communication system that receives a prior optical characteristic map that indicates optical characteristic values corresponding to different geographic locations in a field;

a geographic position sensor that detects a geographic location of the agricultural work machine;

an in-situ sensor that detects a value of an agricultural characteristic corresponding to the geographic location;

a predictive model generator that generates a predictive characteristic model that models a relationship between the optical characteristic and the agricultural characteristic based on an optical characteristic value in the prior optical characteristic map at the geographic location and the value of the agricultural characteristic sensed by the in-situ sensor at the geographic location; and

a predictive map generator that generates a functional predictive map of the field, that maps predictive agricultural characteristic values to the different locations in the field, based on the optical characteristic values in the prior optical characteristic map and the predictive characteristic model.

20. The agricultural work machine of claim 19 , wherein the optical characteristic values comprise wavelength values.

21. An agricultural system, comprising:

a communication system that receives a prior information map that includes values of an optical characteristic corresponding to different geographic locations in a field;

a geographic position sensor that detects a geographic location of an agricultural work machine;

an in-situ sensor that detects a value of an agricultural characteristic corresponding to the geographic location;

a predictive model generator that generates a predictive characteristic model that models a relationship between the optical characteristic and the agricultural characteristic based on a value of the optical characteristic in the prior information map at the geographic location and the value of the agricultural characteristic detected by the in-situ sensor corresponding to the geographic location; and

a predictive map generator that generates a functional predictive map of the field, that maps predictive values of the agricultural characteristic to the different geographic locations in the field, based on the values of the optical characteristic in the prior information map and based on the predictive characteristic model.