Patent Publication Number: US-2023139169-A1

Title: System and method for estimating crop yield for an agricultural harvester using a machine-learned model

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is based upon and claims the right of priority to Brazilian Patent Application No. BR 10 2021 021948 3, filed Oct. 31, 2021, the disclosure of which is hereby incorporated by reference herein in its entirety for all purposes. 
     FIELD OF THE INVENTION 
     The present disclosure relates generally to agricultural harvesters, such as sugarcane harvesters, and, more particularly, to systems and methods for estimating crop yield of an agricultural harvester using a machine-learned model. 
     BACKGROUND OF THE INVENTION 
     Typically, agricultural harvesters include an assembly of processing equipment for processing harvested crop materials. For instance, within a sugarcane harvester, severed sugarcane stalks are conveyed via a feed roller assembly to a chopper assembly that cuts or chops the sugarcane stalks into pieces or billets (e.g., 6 inch cane sections). The processed crop material discharged from the chopper assembly is then directed as a stream of billets and debris into a primary extractor, within which the airborne debris (e.g., dust, dirt, leaves, etc.) is separated from the sugarcane billets. The separated/cleaned billets then fall into an elevator assembly for delivery to an external storage device. 
     During operation of the harvester, it is typically desirable to monitor the crop yield as the machine goes through the field. For sugarcane harvesters, existing yield monitoring systems rely upon a sensorized plate positioned within the elevator assembly to estimate the crop yield based on the load sensed thereby as the sugarcane passes over the plate. While such systems are equipped to provide accurate yield data, the various components of the system are quite expensive, thereby rendering the system cost-prohibitive for some users. Moreover, the sensorized plates typically require a significant amount of maintenance, including the time require to remove any dirt, mud, or other materials that have accumulated between the plate and the elevator. 
     Accordingly, systems and methods for estimating the crop yield for an agricultural harvester that address one or more issues associated with existing systems/methods would be welcomed in the technology. 
     BRIEF DESCRIPTION OF THE INVENTION 
     Aspects and advantages of the invention will be set forth in part in the following description, or may be obvious from the description, or may be learned through practice of the invention. 
     In one aspect, the present subject matter is directed to 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. 
     In another aspect, the present subject matter is directed to a computer-implemented method for estimating crop yield. The computer-implemented method includes 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; 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 value for the yield-related parameter. 
     In a further aspect, the present subject matter is directed to an agricultural harvester that includes a frame and a material processing system supported relative to the frame, with the material processing system being configured to process a flow of harvested materials. The harvester also includes 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 associated with the harvested materials being directed through the agricultural harvester. The computer readable media also stores 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. 
     These and other features, aspects and advantages of the present invention will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A full and enabling disclosure of the present invention, including the best mode thereof, directed to one of ordinary skill in the art, is set forth in the specification, which makes reference to the appended figures, in which: 
         FIG.  1    illustrates a simplified, side view of one embodiment of an agricultural harvester in accordance with aspects of the present subject matter; 
         FIG.  2    illustrates a side view of one embodiment of a portion of a material processing system of an agricultural harvester in accordance with aspects of the present subject matter, particularly illustrating one embodiment of a feed roller assembly and a chopper assembly of the material processing system; 
         FIGS.  3 A and  3 B  illustrate a detail view of one embodiment of a top roller of a feed roller assembly of an agricultural harvester in accordance with aspects of the present subject matter, particularly illustrating the top roller in a lowered position and in a raised position, respectively; 
         FIG.  4    illustrates a schematic view of one embodiment of a computing system for estimating crop yield in accordance with aspects of the present subject matter; 
         FIG.  5    illustrates a schematic view of another embodiment of a computing system for estimating crop yield in accordance with aspects of the present subject matter; 
         FIG.  6    illustrates a schematic view of one embodiment of an exemplary flow diagram for training a machine-learned model; and 
         FIG.  7    illustrates a flow diagram of one embodiment of a method for estimating a crop yield for an agricultural harvester in accordance with aspects of the present subject matter. 
     
    
    
     Repeat use of reference characters in the present specification and drawings is intended to represent the same or analogous features or elements of the present technology. 
     DETAILED DESCRIPTION OF THE INVENTION 
     Reference now will be made in detail to embodiments of the invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the invention, not limitation of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents. 
     In general, the present subject matter is directed to systems and methods estimating crop yields for agricultural harvesters. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned yield estimation model to determine a value for a yield-related parameter indicative of the crop yield for an agricultural harvester based at least in part on input data associated with one or more operation-related conditions for the harvester. For example, the machine-learned yield estimation model can be configured to receive input data and to process the input data to determine a numerical value for the yield-related parameter, such as a numerical value of the mass flow rate of harvested materials through the harvester. 
     In particular, in one example, a computing system can obtain input data from one or more input devices that is associated with one or more operation-related conditions for an agricultural harvester. For instance, the input device(s) may include one or more onboard sensors configured to monitor one or more parameters and/or conditions associated with the harvester and/or the operation being performed therewith, one or more positioning device(s) for generating position data associated with the location of the harvester, one or more user interfaces for allowing operator inputs to be provided to the system, one or more other internal data sources associated with the harvester, one or more external data sources, and/or the like. The computing system can input the data generated or collected by the input device(s) into a machine-learned yield estimation model and, in response, receive a value for the desired yield-related parameter as an output of the model. 
     Further, the systems and methods of the present disclosure can initiate one or more control actions based on the estimated yield-related parameter. For instance, the computing system may be configured to provide the operator with a notification or other communication related to the yield-related parameter. Additionally, the computing system may be configured to store the yield-related data for subsequent use and/or compile the yield-related data to allow for the generation of a yield map. Moreover, the computing system may also be configured to automatically control the operation of one or more components of the harvester based on the estimated yield-related parameter. Thus, in certain embodiments, the systems and methods of the present disclosure can enable improved real-time control of an agricultural harvester that measures and accounts for current crop yields during the performance of a harvesting operation. 
     Through the use of a machine-learned yield estimation model, the systems and methods of the present disclosure can produce yield estimates that exhibit significant accuracy while avoiding many of the issues associated with existing yield monitoring systems (e.g., high costs and substantial downtime). For instance, the input data described herein may, in certain embodiments, be provided from sensors or systems that already exists on the machine, thereby eliminating the need to install expensive, high maintenance sensor systems. Moreover, the accurate crop yield estimates can enable improved and/or more precise control of the harvester, thereby leading to superior agricultural outcomes. 
     Referring now to the drawings,  FIG.  1    illustrates a side view of one embodiment of an agricultural harvester  10  in accordance with aspects of the present subject matter. As shown, the harvester  10  is configured as a sugarcane harvester. However, in other embodiments, the harvester  10  may correspond to any other suitable agricultural harvester known in the art. 
     As shown in  FIG.  1   , the harvester  10  includes a frame  12 , a pair of front wheels  14 , a pair of rear wheels  16 , and an operator&#39;s cab  18 . The harvester  10  may also include a primary source of power (e.g., an engine mounted on the frame  12 ) which powers one or both pairs of the wheels  14 ,  16  via a transmission (not shown). Alternatively, the harvester  10  may be a track-driven harvester and, thus, may include tracks driven by the engine as opposed to the illustrated wheels  14 ,  16 . The engine may also drive a hydraulic fluid pump (not shown) configured to generate pressurized hydraulic fluid for powering various hydraulic components of the harvester  10 . 
     The harvester  10  may also include a material processing system  19  incorporating various components, assemblies, and/or sub-assemblies of the harvester  10  for cutting, processing, cleaning, and discharging sugarcane as the cane is harvested from an agricultural field  20 . For instance, the material processing system  19  may include a topper assembly  22  positioned at the front end of the harvester  10  to intercept sugarcane as the harvester  10  is moved in the forward direction. As shown, the topper assembly  22  may include both a gathering disk  24  and a cutting disk  26 . The gathering disk  24  may be configured to gather the sugarcane stalks so that the cutting disk  26  may be used to cut off the top of each stalk. As is generally understood, the height of the topper assembly  22  may be adjustable via a pair of arms  28  hydraulically raised and lowered, as desired, by the operator. 
     The material processing system  19  may further include a crop divider  30  that extends upwardly and rearwardly from the field  20 . In general, the crop divider  30  may include two spiral feed rollers  32 . Each feed roller  32  may include a ground shoe  34  at its lower end to assist the crop divider  30  in gathering the sugarcane stalks for harvesting. Moreover, as shown in  FIG.  1   , the material processing system  19  may include a knock-down roller  36  positioned near the front wheels  14  and a fin roller  38  positioned behind the knock-down roller  36 . As the knock-down roller  36  is rotated, the sugarcane stalks being harvested are knocked down while the crop divider  30  gathers the stalks from agricultural field  20 . Further, as shown in  FIG.  1   , the fin roller  38  may include a plurality of intermittently mounted fins  40  that assist in forcing the sugarcane stalks downwardly. As the fin roller  38  is rotated during the harvest, the sugarcane stalks that have been knocked down by the knock-down roller  36  are separated and further knocked down by the fin roller  38  as the harvester  10  continues to be moved in the forward direction relative to the field  20 . 
     Referring still to  FIG.  1   , the material processing system  19  of the harvester  10  may also include a base cutter assembly  42  positioned behind the fin roller  38 . As is generally understood, the base cutter assembly  42  may include blades (not shown) for severing the sugarcane stalks as the cane is being harvested. The blades, located on the periphery of the assembly  42 , may be rotated by a hydraulic motor (not shown) powered by the vehicle&#39;s hydraulic system. Additionally, in several embodiments, the blades may be angled downwardly to sever the base of the sugarcane as the cane is knocked down by the fin roller  38 . 
     Moreover, the material processing system  19  may include a feed roller assembly  44  located downstream of the base cutter assembly  42  for moving the severed stalks of sugarcane from base cutter assembly  42  along the processing path of the material processing system  19 . As shown in  FIG.  1   , the feed roller assembly  44  may include a plurality of bottom rollers  46  and a plurality of opposed, top pinch rollers  48 . The various bottom and top rollers  46 ,  48  may be used to pinch the harvested sugarcane during transport. As the sugarcane is transported through the feed roller assembly  44 , debris (e.g., rocks, dirt, and/or the like) may be allowed to fall through bottom rollers  46  onto the field  20 . 
     In addition, the material processing system  19  may include a chopper assembly  50  located at the downstream end of the feed roller assembly  44  (e.g., adjacent to the rearward-most bottom and top rollers  46 ,  48 ). In general, the chopper assembly  50  may be used to cut or chop the severed sugarcane stalks into pieces or “billets”  51 , which may be, for example, six (6) inches long. The billets  51  may then be propelled towards an elevator assembly  52  of the material processing system  19  for delivery to an external receiver or storage device (not shown). 
     As is generally understood, pieces of debris  53  (e.g., dust, dirt, leaves, etc.) separated from the sugarcane billets  51  may be expelled from the harvester  10  through a primary extractor  54  of the material processing system  19 , which is located immediately behind the chopper assembly  50  and is oriented to direct the debris  53  outwardly from the harvester  10 . Additionally, an extractor fan  56  may be mounted within the primary extractor  54  for generating a suction force or vacuum sufficient to pick up the debris  53  and force the debris  53  through the primary extractor  54 . The separated or cleaned billets  51 , heavier than the debris  53  being expelled through the extractor  54 , may then fall downward to the elevator assembly  52 . 
     As shown in  FIG.  1   , the elevator assembly  52  may include an elevator housing  58  and an elevator  60  extending within the elevator housing  58  between a lower, proximal end  62  and an upper, distal end  64 . In general, the elevator  60  may include a looped chain  66  and a plurality of flights or paddles  68  attached to and evenly spaced on the chain  66 . The paddles  68  may be configured to hold the sugarcane billets  51  on the elevator  60  as the billets are elevated along a top span of the elevator  60  defined between its proximal and distal ends  62 ,  64 . Additionally, the elevator  60  may include lower and upper sprockets  72 ,  74  positioned at its proximal and distal ends  62 ,  64 , respectively. As shown in  FIG.  1   , an elevator motor  76  may be coupled to one of the sprockets (e.g., the upper sprocket  74 ) for driving the chain  66 , thereby allowing the chain  66  and the paddles  68  to travel in an endless loop between the proximal and distal ends  62 ,  64  of the elevator  60 . 
     Moreover, in some embodiments, pieces of debris  53  (e.g., dust, dirt, leaves, etc.) separated from the elevated sugarcane billets  51  may be expelled from the harvester  10  through a secondary extractor  78  of the material processing system  19  coupled to the rear end of the elevator housing  58 . For example, the debris  53  expelled by the secondary extractor  78  may be debris remaining after the billets  51  are cleaned and debris  53  expelled by the primary extractor  54 . As shown in  FIG.  1   , the secondary extractor  78  may be located adjacent to the distal end  64  of the elevator  60  and may be oriented to direct the debris  53  outwardly from the harvester  10 . Additionally, an extractor fan  80  may be mounted at the base of the secondary extractor  78  for generating a suction force or vacuum sufficient to pick up the debris  53  and force the debris  53  through the secondary extractor  78 . The separated, cleaned billets  51 , heavier than the debris  53  expelled through the extractor  78 , may then fall from the distal end  64  of the elevator  60 . Typically, the billets  51  may fall downwardly through an elevator discharge opening  82  of the elevator assembly  52  into an external storage device (not shown), such as a sugarcane billet cart. 
     During operation, the harvester  10  is traversed across the agricultural field  20  for harvesting sugarcane. After the height of the topper assembly  22  is adjusted via the arms  28 , the gathering disk  24  on the topper assembly  22  may function to gather the sugarcane stalks as the harvester  10  proceeds across the field  20 , while the cutter disk  26  severs the leafy tops of the sugarcane stalks for disposal along either side of harvester  10 . As the stalks enter the crop divider  30 , the ground shoes  34  may set the operating width to determine the quantity of sugarcane entering the throat of the harvester  10 . The spiral feed rollers  32  then gather the stalks into the throat to allow the knock-down roller  36  to bend the stalks downwardly in conjunction with the action of the fin roller  38 . Once the stalks are angled downwardly as shown in  FIG.  1   , the base cutter assembly  42  may then sever the base of the stalks from field  20 . The severed stalks are then, by movement of the harvester  10 , directed to the feed roller assembly  44 . 
     The severed sugarcane stalks are conveyed rearwardly by the bottom and top rollers  46 ,  48 , which compress the stalks, make them more uniform, and shake loose debris to pass through the bottom rollers  46  to the field  20 . At the downstream end of the feed roller assembly  44 , the chopper assembly  50  cuts or chops the compressed sugarcane stalks into pieces or billets  51  (e.g., 6 inch cane sections). The processed crop material discharged from the chopper assembly  50  is then directed as a stream of billets  51  and debris  53  into the primary extractor  54 . The airborne debris  53  (e.g., dust, dirt, leaves, etc.) separated from the sugarcane billets is then extracted through the primary extractor  54  using suction created by the extractor fan  56 . The separated/cleaned billets  51  then fall downwardly through an elevator hopper  86  into the elevator assembly  52  and travel upwardly via the elevator  60  from its proximal end  62  to its distal end  64 . During normal operation, once the billets  51  reach the distal end  64  of the elevator  60 , the billets  51  fall through the elevator discharge opening  82  to an external storage device. If provided, the secondary extractor  78  (with the aid of the extractor fan  80 ) blows out trash/debris  53  from harvester  10 , similar to the primary extractor  54 . 
     It should be appreciated that the harvester  10  may also include various onboard sensors for monitoring one or more operating parameters or conditions of the harvester  10 . For instance, the harvester  10  may include or be associated with various different speed sensors  90  for monitoring the speed of the harvester  10 , itself, and/or the operating speed of one or more components of the harvester  10 . Specifically, in several embodiments, the speed sensors  90  may be used to detect or monitor various different speed-related parameters associated with the harvester  10 , including, but not limited to, the ground speed of the harvester  10 , the engine speed of the harvester&#39;s engine (e.g., engine RPM), the elevator speed of the elevator assembly  52 , the rotational speed of the blades of the base cutter assembly  42 , the rotational speed of the chopper assembly  50 , the rotational speed of the rollers  46 ,  48  of the feed roller assembly  44 , the fan speed associated with the primary extractor  54  and/or the secondary extractor  78 , and/or any other suitable operating speeds associated with the harvester  10 . For example, as shown in  FIG.  1   , a first speed sensor  90  is provided in operative association with the elevator assembly  52  (e.g., a rotational speed sensor provided in association with the elevator motor  76 ) to allow the elevator speed to be continuous monitored, while a second speed sensor  90  (e.g., a wheel speed sensor or a GPS-enabled device) may be provided in operative association with another component of the harvester  10  (e.g., the wheels  14 ,  16  and/or cab  18 ) to allow the ground speed of the harvester  10  to be continuously monitored. 
     Additionally, in several embodiments, the harvester  10  may include or incorporate one or more position sensors  92  used to monitor one or more corresponding position-related parameters associated with the harvester  10 . Position-related parameters that may be monitored via the position sensor(s)  92  include, but are not limited to, the cutting height of the base cutter assembly  42 , the relative positioning of the bottom and top rollers  46 ,  48  of the feed roller assembly  44  (e.g., as will be described below with reference to  FIG.  2   ), the vertical travel or position of the chassis or frame  12  of the harvester  10 , and/or any other suitable position-related parameters associated with the harvester  10 . For instance, as shown in  FIG.  1   , a position sensor  92  may be mounted to the harvester&#39;s frame  12  to monitor the vertical position or travel of the chassis relative to a given reference point. 
     Moreover, in several embodiments, the harvester  10  may include or incorporate one or more pressure sensors  94  used to monitor one or more corresponding pressure-related parameters associated with the harvester  10 . For instance, pressure-related parameters that may be monitored via the pressure sensor(s)  94  include, but are not limited to, the fluid pressures associated with the hydraulic fluid supplied to one or more hydraulic components of the harvester  10 , such as the hydraulic motor(s) rotationally driving the base cutter assembly  42  (e.g., the base cutter pressure), the hydraulic motor(s) rotationally driving the chopper assembly  50 , and/or any other suitable pressure-related parameters associated with the harvester  10 . For instance, as shown in  FIG.  1   , a pressure sensor  94  may be provided in operative association with the base cutter assembly  42  to monitor the base cutter pressure. 
     It should be appreciated that the harvester  10  may also include various other sensors or sensing devices. In one embodiment, the harvester  10  may include or incorporate one or more load sensors  96  (e.g., one or more load cells or sensorized load plates) used to monitor one or more corresponding load-related parameters associated with the harvester  10 . For instance, as shown in  FIG.  1   , one or more load sensors  96  may be provided in operative association with the elevator assembly  52  to allow the weight or mass flow rate of the harvested materials being directed through the elevator to be monitored. Additionally, in one embodiment, the harvester  10  may include or incorporate one or more vision-based or wave-based sensors  98  (e.g., one or more cameras, radar sensors, ultrasound sensors, LIDAR devices, etc.) used to capture sensor data indicative of one or more observable parameters associated with the harvester  10 , such as by providing a camera or LIDAR device to allow the potential upcoming crop mass within the field to be estimated based on the received vision-based data or by providing an internally installed camera or radar device to allow sensor data to be captured that is associated with the mass flow of the harvested materials through the material processing system  19 . For instance, as shown in  FIG.  1   , a forward looking vision-based sensor  98  may be installed on the cab  18  with a field of view directed in front of the harvester  10  to allow images or other vision-based data to be captured that provides an indication of the upcoming crop mass within the field. 
     Referring now to  FIG.  2   , a side view of a portion of a material processing system of an agricultural harvester is illustrated in accordance with aspects of the present subject matter, particularly showing a side view of one embodiment of the feed roller assembly  44  and chopper assembly  50  of the material processing system  19  described above with reference to  FIG.  1   . 
     As shown in  FIG.  2   , the feed roller assembly  44  extends between a first end  44 A and a second end  44 B, with the first end  44 A of the feed roller assembly  44  being adjacent the base cutter assembly  42  and the second end  44 B of the feed roller assembly  44  being adjacent the chopper assembly  50 . As such, the first end  44 A of the feed roller assembly  44  is configured to receive harvested materials (e.g., severed sugarcane stalks) from the base cutter assembly  42  and to convey the flow of harvested materials along a flow path FP defined between the bottom and top rollers  46 ,  48  to the chopper assembly  50  at the second end  44 B of the feed roller assembly  44 . While the feed roller assembly  44  is shown as having six bottom rollers  46  and five top rollers  48 , it should be appreciated that the feed roller assembly  44  may have any other suitable number of bottom and/or top rollers  46 ,  48 . 
     Due to variations in the volume of harvested materials being processed by the material processing system  19 , the flow of harvested materials through the feed roller assembly  44  will inherently vary in thickness. As such, one set of the rollers of the feed roller assembly  44  may be configured as floating rollers (with the other set of rollers being configured as fixed or non-floating rollers) such that the spacing between the bottom and top rollers  46 ,  48  is variable to account for changes in the volume of the harvested materials being directed through the feed roller assembly  44 . For instance, in one embodiment, each of the top rollers  48  is movable within a respective slot  100 . As particularly shown in  FIGS.  3 A and  3 B , each slot  100  may extend between a first slot end  100 A and a second slot end  100 B. When the top roller  48  abuts against the first slot end  100 A, the top roller  48  is in a lowest position, such that the top roller  48  is spaced by a first distance D1 from the respective bottom roller  46 . When the top roller  48  abuts against the second slot end  100 B, the top roller  48  is in a highest position, such that the top roller  48  is spaced by a second distance D2 from the respective bottom roller  46 . In one embodiment, the first distance D1 is the closest that the top roller  48  may be from the adjacent bottom roller  46  and the second distance D2 is the furthest that the top roller  48  may be from the adjacent bottom roller  46 . In some embodiments, the top rollers  48  are pivotable about a respective pivot joint  102  to move within the slot  100  between the first and second slot ends  100 A,  100 B. For instance, the top roller  48  may be pivoted about the pivot joint  102  between a first angular position, corresponding to the first distance D1, and a second angular position, corresponding to the second distance D2. However, in other embodiments, the top rollers  48  may be configured to move within the slot in any other suitable way. Alternatively, the top rollers  48  may be fixed or non-floating and the bottom rollers  46  may, instead, be movable to allow the spacing between the bottom and top rollers  46 ,  48  to be varied. 
     Additionally, as shown in  FIG.  2   , the chopper assembly  50  may generally include an outer housing  120  and one or more chopper drums  122  rotatably supported within the chopper housing  120 . As is generally understood, the chopper drums  122  are configured to be rotatably driven within the housing  120  such that chopper elements  124  extending outwardly from each drum  122  (e.g., blades) cut or chop the harvested materials received from the feed roller assembly  44 , thereby generating a stream of processed harvested materials (e.g., including both billets  51  and debris  53 ) that is discharged from the chopper assembly  50  via an outlet of the housing  120 . Additionally, as shown in  FIG.  2   , a hydraulic motor(s)  126  is provided in association with the chopper drums  122  for rotationally driving the drums  122 . The hydraulic motor(s)  126  is, in turn, fluidly coupled to a hydraulic pump  128  of the vehicle&#39;s hydraulic system (e.g., via an associated hydraulic circuit  130 —shown in dashed lines) such that pressurized hydraulic fluid can be delivered from the pump  128  to rotationally drive the motor(s)  126 . 
       FIG.  2    also illustrates various examples of sensors that may be used to monitor one or more parameters or conditions associated with the harvester  10 . For instance, as indicated above, one or more position sensors  92  may be used to monitor one or more position-related parameters associated with the harvester  10 , such as by providing a position sensor(s)  92  in association with the feed roller assembly  44  for detecting variations in the spacing between the bottom and top rollers  46 ,  48 . Specifically, in the illustrated embodiment, one or more position sensors  92  may be provided for detecting the displacement of one or more respective top rollers  48  of the feed roller assembly  44 , including, for example, the magnitude and/or rate of the displacement. For instance, as shown in  FIG.  2   , a position sensor  92  is provided in operative association with the furthest downstream top roller  48  of the feed roller assembly  44  to detect the displacement of the roller  48  relative to the adjacent bottom roller  46  as harvested materials are directed through the feed roller assembly  44 . In an alternative embodiment in which the bottom rollers  46  are movable and the top rollers  48  are fixed or non-floating, the position sensor(s)  92  may, instead, be configured to detect the displacement of one or more of the bottom rollers  46  as harvested materials are directed through the feed roller assembly  44 . 
     Additionally, as indicative above, one or more pressure sensors  94  may be used to monitor one or more pressure-related parameters associated with the harvester  10 , such as by providing a pressure sensor(s)  94  to monitor the fluid pressure associated with the hydraulic motor(s)  126  configured to rotationally drive the chopper drums  122  of the chopper assembly  50 . For instance, as shown in  FIG.  2   , a pressure sensor  94  is provided in fluid communication with the hydraulic circuit  130  coupling the motor  126  to the pump  128  to monitor the fluid pressure of the hydraulic fluid being suppled thereto. 
     Moreover, as indicative above, one or more speed sensors  90  may be used to monitor one or more speed-related parameters associated with the harvester  10 , such as by providing one or more speed sensors  90  to monitor the rotational speed of the feeder rollers  46 ,  48  and/or the chopper drums  122 . For instance, as shown in  FIG.  2   , a speed sensor  90  may be provided in association with the chopper assembly  50  to monitor the rotational speed of the chopper drums  122 , such as by installing the sensor  90  in association with the motor  126  driving the drums  122 . 
     As indicated above, it is generally desirable to monitor a yield-related parameter of an agricultural harvester (e.g., a mass flow rate through the harvester) to allow the operator to gather data associated with the crop yield and evaluate the performance of the harvester. In addition, the yield-related data may also be used to automate certain functions or control actions associated with the harvester, such as to automatically adjust one or more operational settings of one or more harvester components to improve the efficiency and/or performance thereof. 
     As will be described below, the yield-related parameter of the harvester (e.g., a mass flow rate through the harvester) may be estimated or determined using a machine-learned model that has been trained or otherwise developed to output the yield-related parameter based on a correlation between such parameter and various inputs into the model. For instance, in several embodiments, the inputs into the machine-learned model may include data associated with one or more “operation-related” conditions, which can, include, but are not limited to, operational parameters and settings of the harvester (e.g., sensed or calculated operating parameters or operator-selected settings), vehicle commands for the harvester, vehicle configuration parameters, application-related parameters, field-related parameters, and/or the like. For instance, operation-related condition data may include, but is not limited to, data associated with any one or a combination of engine speed, ground speed, elevator speed, base cutter height, base cutter pressure, chopper speed, chopper pressure, floating roller position or displacement, the vertical position or travel of the chassis or frame, the fan speed associated with the primary and/or secondary extractor, hydraulic motor usage, foilage proportion, base cutter direction (forward or reverse), raising or lowering of the topper assembly, raising or lowering of the suspension, the model/type of the chopper assembly, the size of the elevator assembly, tire/track parameters, the region within which the harvester is operating, farm-specific parameters, time-related parameters (day/night), humidity data, field NDVI data, yield prediction data, soil analysis data, and/or the like. Such data may be, for example: based directly or indirectly on sensor data received from onboard sensors; calculated or determined by the harvester&#39;s computing system based on data accessible to such system (e.g., including internally derived or externally derived data); received from the operator (e.g., via a user interface); received from an external source (e.g., a remote server or separate computing device); and/or the like. 
     Referring now to  FIGS.  4  and  5   , schematic views of embodiments of a computing system  200  are illustrated in accordance with aspects of the present subject matter. In general, the system  200  will be described herein with reference to the harvester  10  described above with reference to  FIGS.  1 - 3 B . However, it should be appreciated that the disclosed system  200  may generally be utilized with harvesters having any suitable harvester configuration. 
     In several embodiments, the system  200  may include a controller  202  and various other components configured to be communicatively coupled to and/or controlled by the controller  202 , such as various input devices  204  and/or various components of the harvester  10 . In some embodiments, the controller  202  is physically coupled to the harvester  10 . In other embodiments, the controller  202  is not physically coupled to the harvester  10  (e.g., the controller  202  may be remotely located from the harvester  10 ) and instead may communicate with the harvester  10  over a wireless network. 
     As will be described in greater detail below, the controller  202  may be configured to leverage a machine-learned model  228  to determine one or more yield-related parameters for an agricultural harvester (e.g., a mass flow rate through the harvester) based on input data that is related, for instance, to one or more operation-related conditions associated with the harvester. In particular,  FIG.  4    illustrates a computing environment in which the controller  202  can operate to determine the yield-related parameter based on input data  218  received, for example, from one or more input devices  204  and, further, to initiate one or more control actions associated with a harvester, such as by controlling one or more electronically controlled components  240  of the harvester (e.g., the engine, transmission, hydraulic system components, material processing system components, etc.) based on the yield-related data  220 . That is,  FIG.  4    illustrates a computing environment in which the controller  202  is actively used in conjunction with a harvester (e.g., during operation of the harvester within a field). As will be discussed further below,  FIG.  5    depicts a computing environment in which the controller  202  can communicate over a network  280  with a machine learning computing system  250  to train and/or receive a machine-learned model  228 . Thus,  FIG.  5    illustrates operation of the controller  202  to train a machine-learned model  228  and/or to receive a trained machine-learned model  228  from a machine learning computing system  250  (e.g.,  FIG.  5    shows the “training stage”) while  FIG.  4    illustrates operation of the controller  202  to use the machine-learned model  228  to actively determine a yield-related parameter(s) for the harvester (e.g.,  FIG.  4    shows the “inference stage”). 
     Referring first to  FIG.  4   , in general, the controller  202  may correspond to any suitable processor-based device(s), such as a computing device or any combination of computing devices. Thus, as shown in  FIG.  4   , the controller  202  may generally include one or more processor(s)  210  and associated memory devices  212  configured to perform a variety of computer-implemented functions (e.g., performing the methods, steps, algorithms, calculations and the like disclosed herein). As used herein, the term “processor” refers not only to integrated circuits referred to in the art as being included in a computer, but also refers to a controller, a microcontroller, a microcomputer, a programmable logic controller (PLC), an application specific integrated circuit, and other programmable circuits. Additionally, the memory  212  may generally comprise memory element(s) including, but not limited to, computer readable medium (e.g., random access memory (RAM)), computer readable non-volatile medium (e.g., a flash memory), a floppy disk, a compact disc-read only memory (CD-ROM), a magneto-optical disk (MOD), a digital versatile disc (DVD) and/or other suitable memory elements. Such memory  212  may generally be configured to store information accessible to the processor(s)  210 , including data  214  that can be retrieved, manipulated, created and/or stored by the processor(s)  210  and instructions  216  that can be executed by the processor(s)  210 . 
     In several embodiments, the data  214  may be stored in one or more databases. For example, the memory  212  may include an input database  218  for storing input data received from the input device(s)  204 . For example, the input device(s) may include one or more sensors  242  configured to monitor one or more parameters and/or conditions associated with the harvester  10  and/or the operation being performed therewith (e.g., including one or more of the various sensors  90 ,  92 ,  94 ,  96 ,  98  described above), one or more positioning device(s)  243  for generating position data associated with the location of the harvester  10 , one or more user interfaces  244  for allowing operator inputs to be provided to the controller  202  (e.g., buttons, knobs, dials, levers, joysticks, touch screens, and/or the like), one or more other internal data sources  245  associated with the harvester  10  (e.g., other devices, databases, etc.), one or more external data sources  246  (e.g., a remote computing device or sever, including, for instance, the machine-learning computing system  250  of  FIG.  5   ), and/or any other suitable input device(s)  204 . The data received from the input device(s)  204  may, for example, stored within the input database  218  for subsequent processing and/or analysis. 
     In several embodiments, the controller  202  may be configured to receive data from the input device(s)  204  that is associated with one or more “operation-related” conditions. The operation-related condition data may, for example, be: based directly or indirectly on sensor data received from the sensors  242  and/or the location data received from the positioning device(s)  243 ; calculated or determined by the controller  202  based on any data accessible to the system  200  (e.g., including data accessed, received, or transmitted from internal data sources  245  and/or external data sources  246 ); received from the operator (e.g., via the user interface); and/or the like. As indicated above, operation-related conditions may include, but are not limited to, operational parameters and/or settings of the harvester (e.g., sensed or calculated operational parameters or operator-selected settings), vehicle commands for the harvester, vehicle configuration parameters, application-related parameters, field-related parameters, and/or the like. For instance, examples of operation-related conditions include, but are not limited to, engine speed, ground speed, elevator speed, base cutter height, base cutter pressure, chopper speed, chopper pressure, floating roller position or displacement, the vertical position or travel of the chassis or frame, the fan speed associated with the primary and/or secondary extractor, hydraulic motor usage, foilage proportion, base cutter direction (forward or reverse), raising or lowering of the topper assembly, raising or lowering of the suspension, the model/type of the chopper assembly, the size of the elevator assembly, tire/track parameters, the region within which the harvester is operating, farm-specific parameters, time-related parameters (day/night), humidity data, field NDVI data, yield prediction data, soil analysis data, and/or the like. 
     It should be appreciated that, in addition to being considered an input device(s) that allows an operator to provide inputs to the controller  202 , the user interface  244  may also function as an output device. Specifically, the user interface  244  may be configured to allow the controller  202  to provide feedback to the operator (e.g., visual feedback via a display or other presentation device, audio feedback via a speaker or other audio output device, and/or the like). 
     Additionally, as shown in  FIG.  4   , the memory  212  may include a yield-related database  220  for storing information or data associated with the yield-related parameter(s) for the harvester  10 . For example, as indicated above, based on the input data received from the input device(s)  204 , the controller  202  may be configured to estimate or calculate one or more values for yield-related parameters associated with the harvester, such as a value(s) for the mass flow rate of the harvested materials through the harvester  10 . The yield-related parameter value(s) estimated or calculated by the controller  202  may then be stored within the yield-related database  220  for subsequent processing and/or analysis. 
     Moreover, in several embodiments, the memory  212  may also include a location database  222  storing location information about the harvester  10  and/or information about the field being processed (e.g., a field map). Such location database  222  may, for example, correspond to a separate database or may form part of the input database  218 . As shown in  FIG.  4   , the controller  202  may be communicatively coupled to a positioning device(s)  243  installed on or within the harvester  10 . For example, in one embodiment, the positioning device(s)  243  may be configured to determine the exact location of the harvester  10  using a satellite navigation position system (e.g., a GPS system, a Galileo positioning system, the Global Navigation satellite system (GLONASS), the BeiDou Satellite Navigation and Positioning system, and/or the like). In such an embodiment, the location determined by the positioning device(s)  243  may be transmitted to the controller  202  (e.g., in the form of coordinates) and subsequently stored within the location database  222  for subsequent processing and/or analysis. 
     Additionally, in several embodiments, the location data stored within the location database  222  may also be correlated to all or a portion of the input data stored within the input database  218 . For instance, in one embodiment, the location coordinates derived from the positioning device(s)  243  and the data received from the input device(s)  204  may both be time-stamped. In such an embodiment, the time-stamped data may allow the data received from the input device(s)  204  to be matched or correlated to a corresponding set of location coordinates received from the positioning device(s)  243 , thereby allowing the precise location of the portion of the field associated with the input data to be known (or at least capable of calculation) by the controller  202 . 
     Moreover, by matching the input data to a corresponding set of location coordinates, the controller  202  may also be configured to generate or update a corresponding field map associated with the field being processed. For example, in instances in which the controller  202  already includes a field map stored within its memory  212  that includes location coordinates associated with various points across the field, the input data received from the input device(s)  204  may be mapped or correlated to a given location within the field map. Alternatively, based on the location data and the associated image data, the controller  202  may be configured to generate a field map for the field that includes the geo-located input data associated therewith. 
     Likewise, any yield-related parameter derived from a particular set of input data (e.g., a set of input data received at a given time or within a given time period) can also be matched to a corresponding set of location coordinates. For example, the particular location data associated with a particular set of input data can simply be inherited by any yield-related data produced on the basis of or otherwise derived from such set of input data  118 . Thus, based on the location data and the associated yield-related data, the controller  202  may be configured to generate a field map for the field that describes, for each analyzed portion of the field, one or more corresponding yield-related parameter values, such as one or more mass flow rate values. Such a map can be consulted to identify discrepancies in or other characteristics of the yield-related parameter at or among various granular locations within the field. 
     Referring still to  FIG.  4   , in several embodiments, the instructions  216  stored within the memory  212  of the controller  202  may be executed by the processor(s)  210  to implement a data analysis module  226 . In general, the data analysis module  226  may be configured to analyze the input data to determine the yield-related parameter. In particular, as will be discussed further below, the data analysis module  226  can cooperatively operate with or otherwise leverage a machine-learned model  228  to analyze the input data  118  to determine the yield-related parameter. As an example, the data analysis module  226  can perform some or all of method  300  of  FIG.  7   . 
     Moreover, as shown in  FIG.  4   , the instructions  216  stored within the memory  212  of the controller  202  may also be executed by the processor(s)  210  to implement a machine-learned model  228 . In particular, the machine-learned model  228  may be a machine-learned yield estimation model. The machine-learned yield estimation model  228  can be configured to receive input data and to process the data to determine one or more yield-related parameters associated with the current harvesting operation being performed by the harvester  10 . 
     Referring still to  FIG.  4   , the instructions  216  stored within the memory  212  of the controller  202  may also be executed by the processor(s)  110  to implement a control module  229 . In general, the control module  229  may be configured to adjust the operation of the harvester  10  by controlling one or more components  240  of the harvester  10 . Specifically, in several embodiments, the control module  229  may be configured to automatically control the operation of one or more harvester components  240  based at least in part on the yield-related parameter determined as a function of the input data. Thus, the system  200  can reactively manage various operational parameters of the harvester  10  based on the value(s) of the yield-related parameter that is output, for instance, from the machine-learned yield estimation model  228 . 
     For instance, as indicated above, in one embodiment, the yield-related parameter may correspond to the mass flow rate of the harvested materials through the harvester  10 . In such an embodiment, if the mass flow rate is higher than expected, the operational settings of one or more components  240  of the harvester  10  may, for example, be automatically adjusted to accommodate the increased mass flow through system. Similarly, if the mass flow rate is lower than expected, the operational settings of one or more components  240  of the harvester  10  may, for example, be automatically adjusted to accommodate the reduced mass flow through system. For instance, the controller  202  may be configured to automatically adjust the ground speed of the harvester  10  (e.g., by automatically controlling the operation of the engine, transmission, and/or braking system of the harvester  10 ), the fan speed associated with one or more both extractors  54 ,  78  (e.g., by automatically controlling the operation of the associated fan  56 ,  80 ), the elevator speed (e.g., by automatically controlling the operation of the elevator motor  76 ), and/or any other suitable operational settings to accommodate variations in the mass flow through the system. 
     In addition to such automatic control of the harvester operation, the controller  202  may also be configured to initiate one or more other control actions associated with or related to the yield-related parameter determined using the machine-learned model. For instance, in several embodiments, the controller  202  may automatically control the operation of the user interface  244  to provide an operator notification associated with the determined yield-related parameter. Specifically, in one embodiment, the controller  202  may control the operation of the user interface  244  in a manner that causes data associated with the determined yield-related parameter to be presented to the operator of the harvester  10 , such as by presenting raw or processed data associated with the yield-related parameter including numerical values, graphs, maps, and/or any other suitable visual indicators. 
     Additionally, in some embodiments, the control action initiated by the controller  202  may be associated with the generation of a yield map based at least in part on the values for the yield-related parameter output from the machine-learned model. For instance, as indicated above, the location coordinates derived from the positioning device(s)  243  and the yield-related data may both be time-stamped. In such an embodiment, the time-stamped data may allow each yield-related parameter value or datapoint to be matched or correlated to a corresponding set of location coordinates received from the positioning device(s)  243 , thereby allowing the precise location of the portion of the field associated with the value/datapoint to be determined by the controller  202 . The resulting yield map may, for example, simply correspond to a data table that maps or correlates each yield-related datapoint to an associated field location. Alternatively, the yield map may be presented as a geo-spatial mapping of the yield-related data, such as a heat map that indicates the variability in the yield-related parameter across the field. 
     Moreover, as shown in  FIG.  4   , the controller  202  may also include a communications interface  232  to provide a means for the controller  202  to communicate with any of the various other system components described herein. For instance, one or more communicative links or interfaces  234  (e.g., one or more data buses and/or wireless connections) may be provided between the communications interface  232  and the input device(s)  204  to allow data transmitted from the input device(s)  204  to be received by the controller  202 . Additionally, as shown in  FIG.  3   , one or more communicative links or interfaces  238  (e.g., one or more data buses and/or wireless connections) may be provided between the communications interface  232  and one or more electronically controlled components  240  of the harvester  10  to allow the controller  202  to control the operation of such system components. 
     Referring now to  FIG.  5   , according to an aspect of the present disclosure, the controller  202  can store or include one or more machine-learned models  228 . In particular, the machine-learned model  228  may be a machine-learned yield estimation model. The machine-learned yield estimation model  228  can be configured to receive input data and to process the input data to determine one or more yield-related parameters associated with the harvester  10 . 
     As on example, the yield estimation model can correspond to a linear machine-learned model. For instance, in one embodiment, the yield estimation model may be or include a linear regression model. A linear regression model may be used to intake the input data from the input device(s)  204  and provide a continuous, numeric output value for the yield-related parameter. Linear regression models may rely on various different techniques, such as ordinary least squares, ridge regression, lasso, gradient descent, and/or the like. However, in other embodiments, the yield estimation model may be or include any other suitable linear machine-learned model. 
     Alternatively, the yield estimation model may correspond to a non-linear machine-learned model. For instance, in one embodiment, the yield estimation model may be or include a neural network such as, for example, a convolutional neural network. Example neural networks include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, transformer neural networks (or any other models that perform self-attention), or other forms of neural networks. Neural networks can include multiple connected layers of neurons and networks with one or more hidden layers, which can be referred to as “deep” neural networks. Typically, at least some of the neurons in a neural network include non-linear activation functions. 
     As further examples, the yield estimation model can be or can otherwise include various other machine-learned models, such as a support vector machine; one or more decision-tree based models (e.g., random forest models); a Bayes classifier; a K-nearest neighbor classifier; and/or other types of models including both linear models and non-linear models. 
     In some embodiments, the controller  202  can receive the one or more machine-learned models  228  from the machine learning computing system  250  over network  280  and can store the one or more machine-learned models  228  in the memory  212 . The controller  202  can then use or otherwise run the one or more machine-learned models  228  (e.g., by processor(s)  210 ). 
     The machine learning computing system  250  includes one or more processors  252  and a memory  254 . The one or more processors  252  can be any suitable processing device such as described with reference to processor(s)  210 . The memory  254  can include any suitable storage device such as described with reference to memory  212 . 
     The memory  254  can store information that can be accessed by the one or more processors  252 . For instance, the memory  254  (e.g., one or more non-transitory computer-readable storage mediums, memory devices) can store data  256  that can be obtained, received, accessed, written, manipulated, created, and/or stored. In some embodiments, the machine learning computing system  250  can obtain data from one or more memory device(s) that are remote from the system  250 . 
     The memory  254  can also store computer-readable instructions  258  that can be executed by the one or more processors  252 . The instructions  258  can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions  258  can be executed in logically and/or virtually separate threads on processor(s)  252 . 
     For example, the memory  254  can store instructions  258  that when executed by the one or more processors  252  cause the one or more processors  252  to perform any of the operations and/or functions described herein. 
     In some embodiments, the machine learning computing system  250  includes one or more server computing devices. If the machine learning computing system  250  includes multiple server computing devices, such server computing devices can operate according to various computing architectures, including, for example, sequential computing architectures, parallel computing architectures, or some combination thereof. 
     In addition or alternatively to the model(s)  228  at the controller  202 , the machine learning computing system  250  can include one or more machine-learned models  251 . For example, the models  251  can be the same as described above with reference to the model(s)  228 . 
     In some embodiments, the machine learning computing system  250  can communicate with the controller  202  according to a client-server relationship. For example, the machine learning computing system  250  can implement the machine-learned models  251  to provide a web-based service to the controller  202 . For example, the web-based service can provide data analysis for determining yield-related parameters as a service. 
     Thus, machine-learned models  228  can be located and used at the controller  202  and/or machine-learned models  251  can be located and used at the machine learning computing system  250 . 
     In some embodiments, the machine learning computing system  250  and/or the controller  202  can train the machine-learned models  228  and/or  251  through use of a model trainer  260 . The model trainer  260  can train the machine-learned models  228  and/or  251  using one or more training or learning algorithms. One example training technique is backwards propagation of errors (“backpropagation”). Gradient-based (e.g., gradient-descent) or other training techniques can be used. 
     In some embodiments, the model trainer  260  can perform supervised training techniques using a set of training data  262 . For example, the training data  262  can include input data from the input device(s)  204  that is associated with a known value for the target parameter (i.e., the yield-related parameter). For instance, input data associated with the training dataset may be continuously collected, generated, and/or received while the yield-related parameter is being monitored via a separate yield monitoring means to provide matching or correlation datasets between the input data and the yield-related data. In other embodiments, the model trainer  260  can perform unsupervised training techniques. The model trainer  260  can perform a number of generalization techniques to improve the generalization capability of the models being trained. Generalization techniques include weight decays, dropouts, or other techniques. The model trainer  260  can be implemented in hardware, software, firmware, or combinations thereof. 
     Thus, in some embodiments, the models can be trained at a centralized computing system (e.g., at “the factory”) and then distributed to (e.g., transferred to for storage by) specific controllers. Additionally or alternatively, the models can be trained (or re-trained) based on additional training data generated by users of the system. This process may be referred to as “personalization” of the models and may allow users to further train the models to provide improved (e.g., more accurate) predictions for unique field and/or machine conditions experienced by such users. 
     The network(s)  280  can be any type of network or combination of networks that allows for communication between devices. In some embodiments, the network(s) can include one or more of a local area network, wide area network, the Internet, secure network, cellular network, mesh network, peer-to-peer communication link and/or some combination thereof and can include any number of wired or wireless links. Communication over the network(s)  280  can be accomplished, for instance, via a communications interface using any type of protocol, protection scheme, encoding, format, packaging, etc. 
     The machine learning computing system  250  may also include a communications interface  264  to communicate with any of the various other system components described herein. 
       FIGS.  4  and  5    illustrate example computing systems that can be used to implement the present disclosure. Other computing systems can be used as well. For example, in some embodiments, the controller  202  can include the model trainer  260  and the training dataset  262 . In such embodiments, the machine-learned models  228  can be both trained and used locally at the controller  202 . As another example, in some embodiments, the controller  202  is not connected to other computing systems. 
     Referring now to  FIG.  6   , a schematic view illustrating an exemplary flow diagram for training a machine-learned model, such as the machine-learned yield estimation models  228 ,  251  described above, is illustrated in accordance with aspects of the present subject matter. As indicated above, the model(s)  228 ,  251  can be trained by a model trainer  260  that uses training data  262  and performs any suitable supervised and/or unsupervised training techniques. In several embodiments, as shown in  FIG.  6   , the model(s)  228 ,  251  may be trained using one or more training datasets including input data  290  that is associated with a known value for the target parameter  292  (i.e., the yield-related parameter). For instance, in one embodiment, the input data  290  associated with the training dataset may be continuously collected, generated, and/or received (e.g., via the input device(s)  204 ) while both an agricultural harvester is performing a harvesting operation within the field and the target yield-related parameter  292  is being monitored via a separate yield monitoring means (e.g., by using a conventional yield monitoring system that relies upon a sensorized load plate within the elevator assembly to monitor, for example, the mass flow rate through the elevator). 
     By analyzing the input data  290  in combination with the known or target values  292  for the yield-related parameter derived from the separate yield monitoring means, suitable correlations may be established between the input data (including certain subsets of the input data) and the yield-related parameter to develop a machine-learned model that can accurately predict the yield-related parameter based on new datasets including the same type of input data. For instance, in one implementation, suitable correlations may be established between the yield-related parameter and various operation-related conditions associated with or included within the input data, such as various sensed, calculated, and/or known parameters, settings, machine configurations, and/or operational statuses associated with the harvester (e.g., engine speed, ground speed, elevator speed, base cutter height, base cutter pressure, chopper speed, chopper pressure, floating roller position or displacement, the vertical position or travel of the chassis or frame, the fan speed associated with the primary and/or secondary extractor, hydraulic motor usage, base cutter direction (forward or reverse), whether the topper assembly or suspension is being currently raised or lowered, the model/type of the chopper assembly, the size of the elevator assembly, tire/track parameters, and/or the like). As indicated above, in addition to using such harvester-based, operation-related conditions to establish the desired correlations (or as an alternative thereto), suitable correlations may also be established between the yield-related parameter and various other operation-related conditions, such as field-based or application-based operation-related conditions (e.g., conditions specific to the region within which the harvester is operating, farm-specific parameters, time-related parameters (day/night), humidity data, field NDVI data, yield prediction data, soil analysis data, and/or the like). 
     As shown in  FIG.  6   , once the machine-learned model has been trained, new datasets  294  can be input into the model to allow the model to predict or determine new estimated values  296  for the target yield-related parameter. For instance, upon training the model, the input data collected, generated, and/or received during a subsequent harvesting operation can be input into the model to provide yield-related data associated with such harvesting operation. Specifically, in one embodiment, the model may be used to predict or determine values for the yield-related parameter at a given frequency (e.g., the frequency at which new input data is being received) to allow such parameter to be continuously monitored as the harvesting operation is being conducted. As indicated above, such yield-related data may then be used by the computing system  200  to generate an associated field map (e.g., a yield map), to present yield information to the operator (e.g., via the user interface  244 ), to automatically control the operation of the harvester  10 , and/or to execute any other suitable control actions. 
     Referring now to  FIG.  7   , a flow diagram of one embodiment of a method  300  for estimating crop yield for an agricultural harvester is illustrated in accordance with aspects of the present subject matter. In general, the method  300  will be described herein with reference to the agricultural harvester  10  and related components described with reference to  FIGS.  1 - 3 B , and the various components of the system  200  described with reference to  FIGS.  4  and  5   . However, it should be appreciated that the disclosed method  300  may be implemented with harvesters having any other suitable configurations and/or within systems having any other suitable system configuration. In addition, although  FIG.  7    depicts steps performed in a particular order for purposes of illustration and discussion, the methods discussed herein are not limited to any particular order or arrangement. One skilled in the art, using the disclosures provided herein, will appreciate that various steps of the method disclosed herein can be omitted, rearranged, combined, and/or adapted in various ways without deviating from the scope of the present disclosure. 
     As shown in  FIG.  5   , at ( 302 ), the method  300  may include obtaining data associated with one or more operation-related conditions for an agricultural harvester. For instance, as described above, the controller  202  may be communicatively coupled to one or more input devices  204  configured to collect or generate data associated with one or more operation-related conditions for an agricultural harvester, thereby allowing the data collected or generated by such device(s) to be transmitted to the controller  202 . As indicated above, operation-related conditions can include, but are not limited to, operational parameters and settings of the harvester (e.g., sensed or calculated operational parameters or operator-selected settings), vehicle commands for the harvester, vehicle configuration parameters, application-related parameters, field-related parameters, and/or the like. For instance, operation-related condition data may include, but is not limited to, data associated with any one or a combination of the engine speed, ground speed, elevator speed, base cutter height, base cutter pressure, chopper speed, chopper pressure, floating roller position or displacement, the vertical position or travel of the chassis or frame, the fan speed associated with the primary and/or secondary extractor, hydraulic motor usage, foilage proportion, base cutter direction (forward or reverse), raising or lowering of the topper assembly, raising or lowering of the suspension, the model/type of the chopper assembly, the size of the elevator assembly, tire/track parameters, the region within which the harvester is operating, farm-specific parameters, time-related parameters (day/night), humidity data, field NDVI data, yield prediction data, soil analysis data, and/or the like. 
     In some embodiments, the input data may correspond to a dataset collected or generated at a given time, such as by including instantaneously sensed or calculated operating parameters of the harvester  10  as the harvester  10  is performing a harvesting operation within a field. Thus, in some embodiments, the method  300  can be performed iteratively for each new input dataset as such dataset is received. For example, the method  300  can be performed iteratively in real-time as new data is received from the input devices  204  while harvester  10  is moved throughout the field. As an example, the method  300  can be performed iteratively in real-time as new sensor data is received from the sensors  242  that are physically located on the harvester  10 . 
     Additionally, at ( 304 ), the method  300  may include inputting 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. Specifically, as indicated above, the controller  202  may be configured to leverage a machine-learned model that is configured to receive and process input data associated with one or more operation-related conditions for the agricultural harvester to determine a yield-related parameter indicative of the crop yield for the harvester. For instance, in several embodiments, the machine-learned model may be configured to determine the mass flow rate of the harvested materials being directed through a portion of the harvester based on the data input into the model. 
     In some embodiments, the inputted data can correspond to or otherwise include an entirety of the input dataset, such that all of the input data received from the input devices  204  is analyzed. In other embodiments, the inputted data can correspond to or otherwise include only a portion or subset of the input data received from the input devices  204 . Using only a subset of the image data can enable reductions in processing time and requirements. 
     Additionally, at ( 306 ), the method  300  may include receiving a value for the yield-related parameter as an output of the machine-learned yield estimation model. Specifically, the machine-learned model may be configured to output a numerical value for the yield-related parameter based on the data input into the model, such as by outputting a numerical value for the mass flow rate of the harvested materials being directed through the harvester 
     Referring still to  FIG.  7   , at ( 308 ), the method  300  may include initiating a control action for the agricultural harvester based at least in part on the value for the yield-related parameter. For example, as indicated above, the controller  202  may be configured to initiate any number of control actions in association with the determined yield-related parameter, including, but not limited to, presenting data associated with the yield-related parameter to the operator via the associated user interface  244 , generating a yield map based at least in part on the determined yield-related parameter and/or automatically controlling the operation of a component of the harvester  10  based at least in part on the determined yield-related parameter. 
     It is to be understood that the steps of the method  300  are performed by the computing system  200  upon loading and executing software code or instructions which are tangibly stored on a tangible computer readable medium, such as on a magnetic medium, e.g., a computer hard drive, an optical medium, e.g., an optical disk, solid-state memory, e.g., flash memory, or other storage media known in the art. Thus, any of the functionality performed by the computing system  200  described herein, such as the method  300 , is implemented in software code or instructions which are tangibly stored on a tangible computer readable medium. The computing system  200  loads the software code or instructions via a direct interface with the computer readable medium or via a wired and/or wireless network. Upon loading and executing such software code or instructions by the computing system  200 , the computing system  200  may perform any of the functionality of the computing system  200  described herein, including any steps of the method  300  described herein. 
     The term “software code” or “code” used herein refers to any instructions or set of instructions that influence the operation of a computer or computing system. They may exist in a computer-executable form, such as machine code, which is the set of instructions and data directly executed by a computer&#39;s central processing unit or by a computing system, a human-understandable form, such as source code, which may be compiled in order to be executed by a computer&#39;s central processing unit or by a computing system, or an intermediate form, such as object code, which is produced by a compiler. As used herein, the term “software code” or “code” also includes any human-understandable computer instructions or set of instructions, e.g., a script, that may be executed on the fly with the aid of an interpreter executed by a computer&#39;s central processing unit or by a computing system. 
     This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they include structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.