CN-114303587-B - Predictive speed map generation and control system

CN114303587BCN 114303587 BCN114303587 BCN 114303587BCN-114303587-B

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 the 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 predicted agricultural characteristics at different locations in the field based on a relationship between values in the one or more information maps and the agricultural characteristics sensed by the field sensor. The predictive map may be output and used for automated machine control.

Inventors

Nathan R. Van Dick
Barnu Kiran Reddy Pala
Federico pardina malbran
NOEL W. ANDERSON

Assignees

迪尔公司

Dates

Publication Date: 20260508
Application Date: 20210830
Priority Date: 20201009

Claims (10)

1. An agricultural system, comprising: A communication system (206), the communication system (206) receiving an information graph (258), the information graph (258) comprising values of a first agricultural property corresponding to different geographic locations in a field; a geographic position sensor (204), the geographic position sensor (204) detecting a geographic position of the agricultural work machine (100); -an in situ sensor (208), the in situ sensor (208) detecting a value of the second agricultural characteristic corresponding to a geographic location; A predictive model generator that generates a predictive agricultural model that models a relationship between the first agricultural property and the second agricultural property based on a value of the first agricultural property corresponding to the geographic location in the information graph (258) and the value of the second agricultural property corresponding to the geographic location detected by the site sensor (208), and A prediction graph generator that generates a functional predicted machine speed graph of the field, the functional predicted machine speed graph mapping predicted machine speed values at the different geographic locations in the field based on the values of the first agricultural characteristic in the information graph (258) and based on the predicted agricultural model, the predicted machine speed values being indicative of a predicted travel speed of the agricultural work machine.
2. The agricultural system of claim 1, wherein the agricultural system further comprises a control system configured to generate control signals to control a propulsion subsystem on the agricultural work machine based on the predicted machine speed values in the functional predicted machine speed map.
3. The agricultural system of claim 1, wherein the on-site sensor on the agricultural work machine is configured to detect a value of a speed characteristic as the value of the second agricultural characteristic, the value of the speed characteristic being indicative of the travel speed of the agricultural work machine corresponding to the geographic location.
4. The agricultural system of claim 3, wherein the agricultural system further comprises a rate controller configured to generate a feed rate control signal to control a controllable subsystem of the agricultural work machine based on a target feed rate of material through the agricultural work machine, and wherein the in-situ sensor comprises: A sensor configured to generate a sensor signal indicative of an output of the feed rate controller, and A processing system receives and processes the sensor signal to generate the value of the speed characteristic, the value of the speed characteristic being indicative of the travel speed of the agricultural work machine corresponding to the geographic location.
5. The agricultural system of claim 3, wherein the information map comprises a vegetation index map comprising Vegetation Index (VI) values corresponding to the different geographic locations in the field as the values of the first agricultural characteristic, and wherein the predictive agricultural model comprises: a predictive speed model modeling a relationship between the vegetation index and the speed characteristic based on the vegetation index value corresponding to the geographic location in the vegetation index map and the value of the speed characteristic detected by the on-site sensor corresponding to the geographic location.
6. The agricultural system of claim 3, wherein the information map comprises a biomass map comprising biomass values corresponding to the different geographic locations in the field as the value of the first agricultural characteristic, and wherein the predictive agricultural model comprises: A predictive speed model modeling a relationship between the biomass and the speed characteristic based on the biomass value corresponding to the geographic location in the biomass map and the value of the speed characteristic detected by the on-site sensor corresponding to the geographic location.
7. The agricultural system of claim 3, wherein the information map comprises a topography map comprising values of topography characteristics corresponding to different geographic locations in the field as the values of the first agricultural characteristic, and wherein the predictive agricultural model comprises: A predictive speed model modeling a relationship between the terrain characteristic and the speed characteristic based on the value of the terrain characteristic corresponding to the geographic location in the terrain map and the value of the speed characteristic corresponding to the geographic location detected by the on-site sensor.
8. The agricultural system of claim 3, wherein the information map comprises a predicted yield map comprising predicted yield values corresponding to the different geographic locations in the field as the values of the first agricultural characteristic, and wherein the predicted agricultural model comprises: a predicted speed model modeling a relationship between yield and the speed characteristic based on the predicted yield value corresponding to the geographic location in the predicted yield map and the value of the speed characteristic detected by the on-site sensor corresponding to the geographic location.
9. A computer-implemented method of generating a functional predictive agricultural graph, the method comprising: Receiving an information map (258), the information map (258) indicating values of the first agricultural characteristic corresponding to different geographic locations in the field; detecting a geographic location of an agricultural work machine (100); detecting a value of a second agricultural characteristic corresponding to the geographic location with an in-situ sensor (208); Generating a predictive agricultural model modeling a relationship between the first agricultural characteristic and the second agricultural characteristic, and A predictive map generator (212) is controlled to generate a functional predictive machine speed map of the field as the functional predictive agricultural map based on the values of the first agricultural characteristic in the information map (258) and based on the predictive agricultural model, the functional predictive machine speed map mapping predicted machine travel speed values to different geographic locations in the field.
10. An agricultural system, comprising: A communication system (206), the communication system (206) receiving an information map (258), the information map (258) indicating values of agricultural characteristics corresponding to different geographic locations in a field; a geographic position sensor (204), the geographic position sensor (204) detecting a geographic position of the agricultural work machine; An in-situ sensor (208), the in-situ sensor (208) detecting a speed characteristic value of a speed characteristic, the speed characteristic value being indicative of a travel speed of the agricultural work machine corresponding to the geographic location; a predictive model generator that generates a predictive speed model that models a relationship between the agricultural characteristic and the speed characteristic based on the value of the agricultural characteristic corresponding to the geographic location in the information graph (258) and the speed characteristic value of the speed characteristic corresponding to the geographic location detected by the on-site sensor (208), and A prediction graph generator that generates a functional prediction speed graph of the field that maps a prediction speed characteristic value to a different geographic location in the field based on the values of the agricultural characteristic in the information graph (258) and based on the prediction speed model, the prediction speed characteristic value being indicative of a predicted travel speed of the agricultural work machine.

Description

Predictive speed map generation and control system Technical Field The present description relates to agricultural, forestry, construction and turf management machines. Background There are a wide variety of different types of agricultural machines. Some agricultural machines include harvesters such as combine harvesters, sugarcane harvesters, cotton harvesters, self-propelled forage harvesters, and cutter-windrowers. Some harvesters may also be fitted with different types of harvesting tables to harvest different types of crops. Various conditions in the field have many deleterious effects on the harvesting operation. Thus, when such conditions are encountered during a harvesting operation, an operator may attempt to modify the control of the harvester. The discussion above is provided for general background information only and is not intended to be used as an aid in determining the scope of the claimed subject matter. Disclosure of Invention 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 the field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A prediction graph generator generates a prediction graph that predicts predicted agricultural characteristics at different locations in the field based on a relationship between values in the one or more information graphs and the agricultural characteristics sensed by the field sensor. The predictive map may be output and used for automated machine control. This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used for the purpose of limiting the scope of the claimed subject matter. The claimed subject matter is not limited to examples that solve any or all disadvantages noted in the background. Drawings FIG. 1 is a partially pictorial, partially schematic illustration of one example of a combine harvester. Fig. 2 is a block diagram illustrating portions of an agricultural harvester according to some examples of the present disclosure in more detail. Fig. 3A-3B (collectively referred to herein as fig. 3) show a flow chart illustrating an example of the operation of the agricultural harvester in generating the map. Fig. 4 is a block diagram showing one example of a prediction model generator and a prediction map generator. Fig. 5 is a flowchart showing an example of the operation of the agricultural harvester during a harvesting operation in receiving an information map, detecting speed characteristics, and generating a functional prediction speed map for use in controlling the agricultural harvester. FIG. 6 illustrates a block diagram of one example of an agricultural harvester in communication with a remote server environment. Fig. 7 to 9 show examples of mobile devices that may be used in an agricultural harvester. FIG. 10 is a block diagram illustrating one example of a computing environment that may be used in the agricultural harvester and the structures shown in the previous figures. Detailed Description For the purposes of promoting an understanding of the principles of the disclosure, reference will now be made to the examples described herein and illustrated in the drawings and specific language will be used to describe the same. However, it will be understood that it is not intended to limit the scope of the present disclosure. Any alterations and further modifications in the described devices, systems, methods, and any further applications of the principles of the disclosure are contemplated as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that features, components, and/or steps described with respect to one example may be combined with features, components, and/or steps described with respect to other examples of the present disclosure. The present description relates to the use of field data acquired concurrently with agricultural operations, in conjunction with previous data, to generate a predictive map, and more particularly, a predictive velocity map. In some examples, the predicted speed map may be used to control an agricultural work machine, such as an agricultural harvester. As discussed above, the predicted speed map may improve the performance of the agricultural harvester to control the speed of the agricultural harvester as the agricultural harvester engages different conditions in the field. For example, if the crop is still immature, the weed may remain green, thus increasing the moisture content of the biomass encountered by the agricultural harvester. This problem may be exacerbated when the weed m