System and method for estimating crop yield for an agricultural harvester using a machine-learned model
In one aspect, a computing system for estimating crop yields for agricultural harvesters. The computing system includes one or more processors, and one or more non-transitory computer-readable media that collectively store a machine-learned yield estimation model configured to receive data associated with one or more operation-related conditions for an agricultural harvester and process the data to determine a yield-related parameter indicative of a crop yield for the agricultural harvester. In addition, the computer-readable media stores instructions that, when executed by the one or more processors, configure the computing system to perform operations, the operations comprising: obtaining the data associated with one or more operation-related conditions; inputting the data into the machine-learned yield estimation model; and receiving a value for the yield-related parameter as an output of the machine-learned yield estimation model.
1 . A computing system for estimating crop yields for agricultural harvesters, the computing system comprising:
one or more processors; and
one or more non-transitory computer-readable media that collectively store:
a machine-learned yield estimation model configured to receive data associated with one or more operation-related conditions for an agricultural harvester and process the data to determine a yield-related parameter indicative of a crop yield for the agricultural harvester, wherein the yield-related parameter comprises a mass flow rate through a portion of the agricultural harvester; and
instructions that, when executed by the one or more processors, configure the computing system to perform operations, the operations comprising:
obtaining the data associated with one or more operation-related conditions;
inputting the data into the machine-learned yield estimation model; and
receiving a value for the yield-related parameter as an output of the machine-learned yield estimation model.
2 . The computing system of claim 1 , wherein:
the computing system is physically located on the agricultural harvester; and
obtaining the data associated with one or more operation-related conditions comprises obtaining data associated with the one or more operation-related conditions from a source physically located on the agricultural harvester.
3 . The computing system of claim 1 , wherein:
the computing system is physically located on the agricultural harvester; and
obtaining the data associated with one or more operation-related conditions comprises obtaining data associated with the one or more operation-related conditions from a source located remote from the agricultural harvester.
4 . The computing system of claim 1 , wherein the operations further comprise initiating a control action for the agricultural harvester based at least in part on the yield-related parameter.
5 . The computing system of claim 4 , wherein initiating the control action comprises at least one of:
causing data associated with the yield-related parameter to be presented to an operator via a user interface of the agricultural harvester;
generating a yield map based at least in part on the yield-related parameter; or automatically controlling an operation of a component of the agricultural harvester based at least in part on the yield-related parameter.
6 . The computing system of claim 1 , wherein the portion of the agricultural harvester is an elevator assembly of the agricultural harvester.
7 . The computing system of claim 1 , wherein obtaining the data associated with one or more operation-related conditions comprises obtaining sensor data from one or more sensors physically located on the agricultural harvester.
8 . The computing system of claim 7 , wherein the sensor data is indicative of at least one of:
a position or change in position of one or more components of the agricultural harvester;
a ground speed of the agricultural harvester;
an operational speed of one or more components of the agricultural harvester;
a fluid pressure of hydraulic fluid within the agricultural harvester; or
a load applied to one or more components of the agricultural harvester.
9 . A computer-implemented method for estimating crop yield, the computer-implemented method comprising:
obtaining, by a computing system comprising one or more computing devices, data associated with one or more operation-related conditions for an agricultural harvester;
inputting, by the computing system, the data into a machine-learned yield estimation model configured to receive and process the data to determine a yield-related parameter indicative of a crop yield for the agricultural harvester, wherein the yield-related parameter comprises a mass flow rate through a portion of the agricultural harvester;
receiving, by the computing system, a value for the yield-related parameter as an output of the machine-learned yield estimation model; and
initiating, by the computing system, a control action for the agricultural harvester based at least in part on the yield-related parameter.
10 . The computer-implemented method of claim 9 , wherein:
the computing system is physically located on the agricultural harvester; and
obtaining the data associated with one or more operation-related conditions comprises obtaining data associated with the one or more operation-related conditions from a source physically located on the agricultural harvester.
11 . The computer-implemented method of claim 9 , wherein:
the computing system is physically located on the agricultural harvester; and
obtaining the data associated with one or more operation-related conditions comprises obtaining data associated with the one or more operation-related conditions from a source located remote from the agricultural harvester.
12 . The computer-implemented method of claim 9 , wherein initiating the control action comprises causing data associated with the yield-related parameter to be presented to an operator via a user interface of the agricultural harvester.
13 . The computer-implemented method of claim 9 , wherein initiating the control action comprises generating a yield map based at least in part on the yield-related parameter.
14 . The computer-implemented method of claim 9 , wherein initiating the control action comprises automatically controlling an operation of a component of the agricultural harvester based at least in part on the yield-related parameter.
15 . The computer-implement method of claim 9 , wherein the portion of the agricultural harvester is an elevator assembly of the agricultural harvester.
16 . The computer-implement method of claim 9 , wherein obtaining the data associated with one or more operation-related conditions comprises obtaining sensor data from one or more sensors physically located on the agricultural harvester.
17 . The computer-implement method of claim 16 , wherein the sensor data is indicative of at least one of:
a position or change in position of one or more components of the agricultural harvester;
a ground speed of the agricultural harvester;
an operational speed of one or more components of the agricultural harvester;
a fluid pressure of hydraulic fluid within the agricultural harvester; or a load applied to one or more components of the agricultural harvester.
18 . An agricultural harvester, comprising:
a frame;
a material processing system supported relative to the frame and being configured to process a flow of harvested materials; and
a controller comprising one or more processors and one or more non-transitory computer-readable media that collectively store:
a machine-learned yield estimation model configured to receive data associated with one or more operation-related conditions for the agricultural harvester and process the data to determine a yield-related parameter, the yield-related parameter comprising a mass flow rate of the harvested materials through a portion of the agricultural harvester; and
instructions that, when executed by the one or more processors, configure the controller to perform operations, the operations comprising:
obtaining the data associated with one or more operation-related conditions;
inputting the data into the machine-learned yield estimation model; and
receiving a value for the yield-related parameter as an output of the machine-learned yield estimation model.
19 . The agricultural harvester of claim 18 , wherein the operations further comprise initiating a control action for the agricultural harvester based at least in part on the value for the yield-related parameter.
20 . The agricultural harvester of claim 18 , further comprising one or more sensors physically located on the agricultural harvester,
wherein obtaining the data associated with one or more operation-related conditions comprises obtaining sensor data from the one or more sensors; and
wherein the sensor data is indicative of at least one of:
a position or change in position of one or more components of the agricultural harvester;
a ground speed of the agricultural harvester;
an operational speed of one or more components of the agricultural harvester;
a fluid pressure of hydraulic fluid within the agricultural harvester; or
a load applied to one or more components of the agricultural harvester.