Map generation and control system

Abstract

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

What is claimed is: 
     
         1 . An agricultural work machine, comprising:
 a crop processing system;   a communication system that receives an information map that includes values of a first agricultural 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 a second agricultural characteristic indicative of a characteristic of harvested material corresponding to the geographic location;   a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive values of the second agricultural characteristic to the different geographic locations in the field based on the values of the first agricultural characteristic in the information map; and   a control system that controls the crop processing system based on the predictive values of the second agricultural characteristic at the different locations in the field and based on the detected geographic location.   
     
     
         2 . The agricultural work machine of  claim 1  and further comprising:
 a predictive model generator that generates a predictive agricultural model that models a relationship between the first agricultural characteristic and the second agricultural characteristic based on a value of the first agricultural characteristic in the information map at the geographic location and a value of the second agricultural characteristic sensed by the in-situ sensor at the geographic location, wherein the predictive map generator generates the functional predictive agricultural map of the field that maps predictive values of the second agricultural characteristic to the different geographic locations in the field based on the values of the first agricultural characteristic in the information map and based on the predictive agricultural model. 
 
     
     
         3 . The agricultural work machine of  claim 1 , wherein the harvested material includes kernels and wherein the in-situ sensor comprises:
 a kernel size sensor that senses a kernel size characteristic of kernels in the agricultural work machine.   
     
     
         4 . The agricultural work machine of  claim 1 , wherein the harvested material includes at least one of ears, heads, and pods (EHP) and wherein the in-situ sensor comprises:
 an EHP characteristic sensor that senses an EHP characteristic indicative of a characteristic of one or more of ears, heads, and pods in the agricultural work machine.   
     
     
         5 . The agricultural work machine of  claim 2 , wherein the harvested material includes kernels and wherein the predictive map generator comprises:
 a kernel size map generator that generates, as the functional predictive agricultural map, a predictive kernel size map that maps, as the predictive values of the second agricultural characteristic, predictive values of size of the kernels to the different geographic locations in the field based on the values of the first agricultural characteristic in the information map and based on the predictive model.   
     
     
         6 . The agricultural work machine of  claim 2 , wherein the harvested material includes at least one of ears, heads, and pods (EHP) and wherein the predictive map generator comprises:
 an EHP characteristic map generator that generates, as the functional predictive agricultural map, a predictive EHP map that maps, as the predictive values of the second agricultural characteristic, predictive values of one or more EHP characteristics to the different geographic locations in the field based on the values of the first agricultural characteristic in the information map and based on the predictive model.   
     
     
         7 . The agricultural work machine of  claim 2 , wherein the communication system receives, as the information map, a first information map that includes the values of the first agricultural characteristic and a second information map that includes values of a third agricultural characteristic corresponding to different locations in the field,
 wherein the predictive model generator generates the predictive agricultural model to model a relationship between the second agricultural characteristic and a combination of the first and third agricultural characteristics based on the value of the second agricultural characteristic sensed by the in-situ sensor at the geographic location and based on the value of the first agricultural characteristic in the first information map at the geographic location and the value of the third agricultural characteristic in the second information map at the geographic location, and   wherein the predictive map generator comprises:
 a combination map generator that generates, as the functional predictive map, a predictive combination map that maps the predictive values of the second agricultural characteristic to the different geographic locations in the field based on the predictive agricultural model and based on the values of the first agricultural characteristic in the first information map and based on the values of the third agricultural characteristic in the second information map. 
   
     
     
         8 . The agricultural work machine of  claim 2 , wherein the communication system receives, as the prior information map, a seed genotype map that includes, as the first agricultural characteristic, a seed genotype, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the seed genotype and the second agricultural characteristic. 
     
     
         9 . The agricultural work machine of  claim 2 , wherein the communication system receives, as the information map, a vegetative index map that includes, as the first agricultural characteristic, a vegetative index characteristic, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the vegetative index characteristic and the second agricultural characteristic. 
     
     
         10 . The agricultural work machine of  claim 2 , wherein the communication system receives, as the information map, a yield map that includes, as the first agricultural characteristic, a predictive yield characteristic, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the predictive yield characteristic and the second agricultural characteristic. 
     
     
         11 . The agricultural work machine of  claim 2 , wherein the communication system receives, as the information map, a biomass map that includes, as the first agricultural characteristic, a biomass characteristic, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the biomass characteristic and the second agricultural characteristic. 
     
     
         12 . A computer implemented method of controlling an agricultural work machine that includes a crop processing system comprising:
 receiving an information map, at the agricultural work machine, that indicates values of a first agricultural 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 second agricultural characteristic indicative of a characteristic of harvested material corresponding to the geographic location;   controlling a predictive map generator to generate a functional predictive agricultural map of the field that maps predictive values of the second agricultural characteristic to the different locations in the field based on the values of the first agricultural characteristic in the information map; and   controlling the crop processing system based on the predictive values of the second agricultural characteristic at the different locations in the field and based on the detected geographic location.   
     
     
         13 . The computer implemented method of  claim 12  and further comprising:
 generating a predictive agricultural model that models a relationship between the first agricultural characteristic and the second agricultural characteristic based on a value of the first agricultural characteristic in the information map at the geographic location and a value of the second agricultural characteristic sensed by the in-situ sensor at the geographic location, wherein controlling the predictive map generator generates the functional predictive agricultural map of the field that maps predictive values of the second agricultural characteristic to the different geographic locations in the field based on the values of the first agricultural characteristic in the information map and based on the predictive agricultural model. 
 
     
     
         14 . The computer implemented method of  claim 12 , wherein the harvested material includes kernels and wherein detecting a second agricultural characteristic comprises:
 detecting a kernel size characteristic of kernels in the agricultural work machine.   
     
     
         15 . The computer implemented method of  claim 12 , wherein the harvested material includes at least one of ears, heads, and pods (EHP) and wherein detecting the second agricultural characteristic comprises:
 detecting an EHP characteristic indicative of a characteristic of one or more of ears, heads, and pods in the agricultural work machine.   
     
     
         16 . The computer implemented method of  claim 13 , wherein the harvested material includes kernels and wherein controlling the predictive map generator comprises:
 controlling a kernel size map generator that generates, as the functional predictive agricultural map, a predictive kernel size map that maps, as the predictive values of the second agricultural characteristic, predictive values of size of the kernels to the different geographic locations in the field based on the values of the first agricultural characteristic in the information map and based on the predictive model.   
     
     
         17 . The computer implemented method of  claim 13 , wherein the harvested material includes at least one of ears, heads and pods (EHP) and wherein controlling the predictive map generator comprises:
 controlling an EHP characteristic map generator that generates, as the functional predictive agricultural map, a predictive EHP map that maps, as the predictive values of the second agricultural characteristic, predictive values of one or more EHP characteristics to the different geographic locations in the field based on the values of the first agricultural characteristic in the prior information map and based on the predictive model.   
     
     
         18 . The computer implemented method of  claim 13  wherein receiving the information map comprises receiving, as the information map, a first information map that includes the values of the first agricultural characteristic and a second information map that includes values of a third agricultural characteristic corresponding to different locations in the field and wherein generating the predictive agricultural model comprises generating the predictive agricultural model to model a relationship between the second agricultural characteristic and a combination of the first and third agricultural characteristics based on the value of the second agricultural characteristic detected by the in-situ sensor at the geographic location and based on the value of the first agricultural characteristic in the first information map at the geographic location and the value of the third agricultural characteristic in the second information map at the geographic location and wherein the controlling the predictive map generator comprises:
 controlling the predictive map generator to generate, as the functional predictive map, a predictive combination map that maps the predictive values of the second agricultural characteristic to the different geographic locations in the field based on the predictive agricultural model and based on the values of the first agricultural characteristic in the first information map and based on the values of the third agricultural characteristic in the second information map. 
 
     
     
         19 . The computer implemented method of  claim 12 , wherein the communication system receives, as the information map, one or more of a seed genotype map that includes, as the first agricultural characteristic, a seed genotype; a vegetative index map that includes, as the first agricultural characteristic, a vegetative index characteristic; a yield map that includes, as the first agricultural characteristic, a predictive yield characteristic; a biomass map that includes, as the first agricultural characteristic, and a biomass characteristic. 
     
     
         20 . An agricultural work machine, comprising:
 a communication system that receives an information map that includes values of a first agricultural 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 a second agricultural characteristic corresponding to the geographic location, the second agricultural characteristic being indicative of one or more of a kernel size of kernels in the agricultural work machine, a characteristic of ears in the agricultural work machine, a characteristic of heads in the agricultural work machine, and a characteristic of pods in the agricultural work machine;   a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive values of the second agricultural characteristic to the different geographic locations in the field based on the values of the first agricultural characteristic in the information map; and   a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map.