Patent Publication Number: US-10769771-B2

Title: Measuring crop residue from imagery using a machine-learned semantic segmentation model

Description:
FIELD OF THE INVENTION 
     The present subject matter relates generally to measuring crop residue in a field and, more particularly, to measuring crop residue in a field from imagery of the field using a machine-learned semantic segmentation model. 
     BACKGROUND OF THE INVENTION 
     Crop residue generally refers to the vegetation (e.g., straw, chaff, husks, cobs) remaining on the soil surface following the performance of a given agricultural operation, such as a harvesting operation or a tillage operation. For various reasons, it is important to maintain a given amount of crop residue within a field following an agricultural operation. Specifically, crop residue remaining within the field can help in maintaining the content of organic matter within the soil and can also serve to protect the soil from wind and water erosion. However, in some cases, leaving an excessive amount of crop residue within a field can have a negative effect on the soil&#39;s productivity potential, such as by slowing down the warming of the soil at planting time and/or by slowing down seed germination. As such, the ability to monitor and/or adjust the amount of crop residue remaining within a field can be very important to maintaining a healthy, productive field, particularly when it comes to performing tillage operations. 
     In this regard, vision-based systems have been developed that attempt to estimate crop residue coverage from images captured of the field. However, such vision-based systems suffer from various drawbacks or disadvantages, particularly with reference to the accuracy of the crop residue estimates provided through the use of computer-aided image processing techniques. 
     Accordingly, an improved vision-based system that estimates crop residue data with improved accuracy 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. 
     One example aspect of the present disclosure is directed to a computing system. The computing system includes one or more processors and one or more non-transitory computer-readable media that collectively store a machine-learned semantic segmentation model configured to receive imagery and to process the imagery to semantically segment the imagery. The one or more non-transitory computer-readable media collectively store instructions that, when executed by the one or more processors, configure the computing system to perform operations. The operations include obtaining image data that depicts a portion of a field. The image data includes a plurality of pixels. The operations include inputting the image data into the machine-learned semantic segmentation model. The operations include receiving a semantic segmentation of the image data as an output of the machine-learned semantic segmentation model. The semantic segmentation respectively labels each of the plurality of pixels as a residue pixel or a non-residue pixel. The operations include determining a crop residue value for the image data based at least in part on the semantic segmentation of the image data. 
     Another example aspect of the present disclosure is directed to a computer-implemented method. The method includes obtaining, by a computing system that includes one or more computing devices, image data that depicts a portion of a field. The image data includes a plurality of pixels. The method includes inputting, by the computing system, the image data into a machine-learned semantic segmentation model configured to receive imagery and to process the imagery to semantically segment the imagery. The method includes receiving, by the computing system, a semantic segmentation of the image data as an output of the machine-learned semantic segmentation model. The semantic segmentation respectively labels each of the plurality of pixels as a residue pixel or a non-residue pixel. The method includes determining, by the computing system, a crop residue value for the image data based at least in part on the semantic segmentation of the image data. 
     Another example aspect of the present disclosure is directed to an agricultural work vehicle or implement that includes one or more imaging devices and a controller. The controller includes one or more processors and one or more non-transitory computer-readable media that collectively store a machine-learned semantic segmentation model configured to receive imagery and to process the imagery to semantically segment the imagery. The one or more non-transitory computer-readable media collectively store instructions that, when executed by the one or more processors, configure the controller to perform operations. The operations include obtaining image data that depicts a portion of a field. The image data includes a plurality of pixels. The operations include inputting the image data into the machine-learned semantic segmentation model. The operations include receiving a semantic segmentation of the image data as an output of the machine-learned semantic segmentation model. The semantic segmentation respectively labels each of the plurality of pixels as a residue pixel or a non-residue pixel. The operations include determining a crop residue value for the image data based at least in part on the semantic segmentation of the image data. 
     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 perspective view of one embodiment of a work vehicle towing an implement in accordance with aspects of the present subject matter; 
         FIG. 2  illustrates a perspective view of the implement shown in  FIG. 1 ; 
         FIG. 3  illustrates a schematic view of one embodiment of a computing system in accordance with aspects of the present subject matter; 
         FIG. 4  illustrates a schematic view of one embodiment of a computing system in accordance with aspects of the present subject matter; 
         FIG. 5  illustrates a flow diagram of one embodiment of a method for measuring crop residue in a field in accordance with aspects of the present subject matter. 
         FIG. 6  illustrates a flow diagram of one embodiment of a method for determining a crop residue parameter value in accordance with aspects of the present subject matter. 
     
    
    
     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 that measure crop residue in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned semantic segmentation model to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. In particular, the machine-learned semantic segmentation model can be configured to receive imagery and to process the imagery to semantically segment the imagery. 
     In one example, a computing system can receive image data that depicts a portion of a field. For example, the image data can be captured by a camera positioned in a (at least partially) downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field. The image data can include a plurality of pixels. The computing system can input the image data into the machine-learned semantic segmentation model and, in response, receive a semantic segmentation of the image data as an output of the machine-learned semantic segmentation model. The semantic segmentation can respectively label each of the plurality of pixels as a residue pixel or a non-residue pixel. The computing system can determine a crop residue parameter value for the image data based at least in part on the semantic segmentation of the image data. For example, the computing system can determine a weighted average of the respective labels assigned to the plurality of pixels. For example, each label can be weighted according to an approximate ground area of the pixel for which such label was provided. 
     Further, the systems and methods of the present disclosure can control an operation of a work vehicle and/or implement based on the crop residue parameter value. For example, the relative positioning, penetration depth, down force, and/or any other operational parameters associated with one or more ground-engaging tools can be modified based on the crop residue parameter value, thereby modifying the amount of crop residue within the field towards a target condition. Thus, the systems and methods of the present disclosure can enable improved real-time control that measures and accounts for existing crop residue conditions during field operations. 
     Through the use of a machine-learned semantic segmentation model, the systems and methods of the present disclosure can produce crop residue estimates that exhibit greater accuracy. These more accurate estimates of crop residue can enable improved and/or more precise control of the work vehicle and/or implement to obtain a desired crop residue condition within a field and, as a result, lead to superior agricultural outcomes. 
     Furthermore, although aspects of the present disclosure are discussed primarily with respect to measurement of crop residue parameters, the systems and methods of the present disclosure can be generalized or extended to measurement of other physical characteristics of a field. For example, aspects of the present disclosure such as a machine-learned segmentation model and/or weighting based on approximate ground area can also be applied to determination of the presence and/or size of soil clods. For example, the machine-learned segmentation model can be trained on different training data so that it recognizes (e.g., segments) soil clods rather than crop residue. 
     Referring now to drawings,  FIGS. 1 and 2  illustrate perspective views of one embodiment of a work vehicle  10  and an associated agricultural implement  12  in accordance with aspects of the present subject matter. Specifically,  FIG. 1  illustrates a perspective view of the work vehicle  10  towing the implement  12  (e.g., across a field). Additionally,  FIG. 2  illustrates a perspective view of the implement  12  shown in  FIG. 1 . As shown in the illustrated embodiment, the work vehicle  10  is configured as an agricultural tractor. However, in other embodiments, the work vehicle  10  may be configured as any other suitable agricultural vehicle. 
     As particularly shown in  FIG. 1 , the work vehicle  10  includes a pair of front track assemblies  14 , a pair of rear track assemblies  16  and a frame or chassis  18  coupled to and supported by the track assemblies  14 ,  16 . An operator&#39;s cab  20  may be supported by a portion of the chassis  18  and may house various input devices for permitting an operator to control the operation of one or more components of the work vehicle  10  and/or one or more components of the implement  12 . Additionally, as is generally understood, the work vehicle  10  may include an engine  22  ( FIG. 3 ) and a transmission  24  ( FIG. 3 ) mounted on the chassis  18 . The transmission  24  may be operably coupled to the engine  22  and may provide variably adjusted gear ratios for transferring engine power to the track assemblies  14 ,  16  via a drive axle assembly (not shown) (or via axles if multiple drive axles are employed). 
     Moreover, as shown in  FIGS. 1 and 2 , the implement  12  may generally include a carriage frame assembly  30  configured to be towed by the work vehicle via a pull hitch or tow bar  32  in a travel direction of the vehicle (e.g., as indicated by arrow  34 ). The carriage frame assembly  30  may be configured to support a plurality of ground-engaging tools, such as a plurality of shanks, disk blades, leveling blades, basket assemblies, and/or the like. In several embodiments, the various ground-engaging tools may be configured to perform a tillage operation across the field along which the implement  12  is being towed. 
     As particularly shown in  FIG. 2 , the carriage frame assembly  30  may include aft extending carrier frame members  36  coupled to the tow bar  32 . In addition, reinforcing gusset plates  38  may be used to strengthen the connection between the tow bar  32  and the carrier frame members  36 . In several embodiments, the carriage frame assembly  30  may generally function to support a central frame  40 , a forward frame  42  positioned forward of the central frame  40  in the direction of travel  34  of the work vehicle  10 , and an aft frame  44  positioned aft of the central frame  40  in the direction of travel  34  of the work vehicle  10 . As shown in  FIG. 2 , in one embodiment, the central frame  40  may correspond to a shank frame configured to support a plurality of ground-engaging shanks  46 . In such an embodiment, the shanks  46  may be configured to till the soil as the implement  12  is towed across the field. However, in other embodiments, the central frame  40  may be configured to support any other suitable ground-engaging tools. 
     Additionally, as shown in  FIG. 2 , in one embodiment, the forward frame  42  may correspond to a disk frame configured to support various gangs or sets  48  of disk blades  50 . In such an embodiment, each disk blade  50  may, for example, include both a concave side (not shown) and a convex side (not shown). In addition, the various gangs  48  of disk blades  50  may be oriented at an angle relative to the travel direction  34  of the work vehicle  10  to promote more effective tilling of the soil. However, in other embodiments, the forward frame  42  may be configured to support any other suitable ground-engaging tools. 
     As another example, ground-engaging tools can include harrows which can include, for example, a number of tines or spikes, which are configured to level or otherwise flatten any windrows or ridges in the soil. The implement  12  may include any suitable number of harrows. In fact, some embodiments of the implement  12  may not include any harrows. 
     In some embodiments, the implement  12  may optionally include one or more additional ground-engaging tools, such as one or more basket assemblies or rotary firming wheels. The baskets may be configured to reduce the number of clods in the soil and/or firm the soil over which the implement  12  travels. Each basket may be configured to be pivotally coupled to one of the frames  40 ,  42 ,  44 , or other components of the implement  12 . It should be appreciated that the implement  12  may include any suitable number of baskets. In fact, some embodiments of the implement  12  may not include any baskets. Example basket assemblies are shown at  54 , as described further below. 
     Moreover, similar to the central and forward frames  40 ,  42 , the aft frame  44  may also be configured to support a plurality of ground-engaging tools. For instance, in the illustrated embodiment, the aft frame is configured to support a plurality of leveling blades  52  and rolling (or crumbler) basket assemblies  54 . However, in other embodiments, any other suitable ground-engaging tools may be coupled to and supported by the aft frame  44 , such as a plurality of closing disks. 
     In addition, the implement  12  may also include any number of suitable actuators (e.g., hydraulic cylinders) for adjusting the relative positioning, penetration depth, and/or down force associated with the various ground-engaging tools (e.g., ground-engaging tools  46 ,  50 ,  52 ,  54 ). For instance, the implement  12  may include one or more first actuators  56  coupled to the central frame  40  for raising or lowering the central frame  40  relative to the ground, thereby allowing the penetration depth and/or the down pressure of the shanks  46  to be adjusted. Similarly, the implement  12  may include one or more second actuators  58  coupled to the disk forward frame  42  to adjust the penetration depth and/or the down pressure of the disk blades  50 . Moreover, the implement  12  may include one or more third actuators  60  coupled to the aft frame  44  to allow the aft frame  44  to be moved relative to the central frame  40 , thereby allowing the relevant operating parameters of the ground-engaging tools  52 ,  54  supported by the aft frame  44  (e.g., the down pressure and/or the penetration depth) to be adjusted. 
     It should be appreciated that the configuration of the work vehicle  10  described above and shown in  FIG. 1  is provided only to place the present subject matter in an exemplary field of use. Thus, it should be appreciated that the present subject matter may be readily adaptable to any manner of work vehicle configuration. For example, in an alternative embodiment, a separate frame or chassis may be provided to which the engine, transmission, and drive axle assembly are coupled, a configuration common in smaller tractors. Still other configurations may use an articulated chassis to steer the work vehicle  10 , or rely on tires/wheels in lieu of the track assemblies  14 ,  16 . 
     It should also be appreciated that the configuration of the implement  12  described above and shown in  FIGS. 1 and 2  is only provided for exemplary purposes. Thus, it should be appreciated that the present subject matter may be readily adaptable to any manner of implement configuration. For example, as indicated above, each frame section of the implement  12  may be configured to support any suitable type of ground-engaging tools, such as by installing closing disks on the aft frame  44  of the implement  12  or other modifications. Additionally, in one embodiment, the implement  10  may include a central frame section and one or more wing sections pivotally coupled to the central frame section, such as along opposed sides of the central frame section. 
     Additionally, in accordance with aspects of the present subject matter, the work vehicle  10  and/or the implement  12  may include one or more imaging devices coupled thereto and/or supported thereon for capturing images or other image data associated with the field as an operation is being performed via the implement  12 . Specifically, in several embodiments, the imaging device(s) may be provided in operative association with the work vehicle  10  and/or the implement  12  such that the imaging device(s) has a field of view directed towards a portion(s) of the field disposed in front of, behind, and/or underneath some portion of the work vehicle  10  and/or implement  12  such as, for example, alongside one or both of the sides of the work vehicle  10  and/or the implement  12  as the implement  12  is being towed across the field. As such, the imaging device(s) may capture images from the tractor  10  and/or implement  12  of one or more portion(s) of the field being passed by the tractor  10  and/or implement  12 . 
     In general, the imaging device(s) may correspond to any suitable device(s) configured to capture images or other image data of the field that allow the field&#39;s soil to be distinguished from the crop residue remaining on top of the soil. For instance, in several embodiments, the imaging device(s) may correspond to any suitable camera(s), such as single-spectrum camera or a multi-spectrum camera configured to capture images, for example, in the visible light range and/or infrared spectral range. Additionally, in a particular embodiment, the camera(s) may correspond to a single lens camera configured to capture two-dimensional images or a stereo camera(s) having two or more lenses with a separate image sensor for each lens to allow the camera(s) to capture stereographic or three-dimensional images. Alternatively, the imaging device(s) may correspond to any other suitable image capture device(s) and/or vision system(s) that is capable of capturing “images” or other image-like data that allow the crop residue existing on the soil to be distinguished from the soil. For example, the imaging device(s) may correspond to or include radio detection and ranging (RADAR) sensors and/or light detection and ranging (LIDAR) sensors. 
     It should be appreciated that work vehicle  10  and/or implement  12  may include any number of imaging device(s)  104  provided at any suitable location that allows images of the field to be captured as the vehicle  10  and implement  12  traverse through the field. For instance,  FIGS. 1 and 2  illustrate examples of various locations for mounting one or more imaging device(s) for capturing images of the field. Specifically, as shown in  FIG. 1 , in one embodiment, one or more imaging devices  104 A may be coupled to the front of the work vehicle  10  such that the imaging device(s)  104 A has a field of view  106  that allows it to capture images of an adjacent area or portion of the field disposed in front of the work vehicle  10 . For instance, the field of view  106  of the imaging device(s)  104 A may be directed outwardly from the front of the work vehicle  10  along a plane or reference line that extends generally parallel to the travel direction  34  of the work vehicle  10 . In addition to such imaging device(s)  104 A (or as an alternative thereto), one or more imaging devices  104 B may also be coupled to one of the sides of the work vehicle  10  such that the imaging device(s)  104 B has a field of view  106  that allows it to capture images of an adjacent area or portion of the field disposed along such side of the work vehicle  10 . For instance, the field of view  106  of the imaging device(s)  104 B may be directed outwardly from the side of the work vehicle  10  along a plane or reference line that extends generally perpendicular to the travel direction  34  of the work vehicle  10 . 
     Similarly, as shown in  FIG. 2 , in one embodiment, one or more imaging devices  104 C may be coupled to the rear of the implement  12  such that the imaging device(s)  104 C has a field of view  106  that allows it to capture images of an adjacent area or portion of the field disposed aft of the implement. For instance, the field of view  106  of the imaging device(s)  104 C may be directed outwardly from the rear of the implement  12  along a plane or reference line that extends generally parallel to the travel direction  34  of the work vehicle  10 . In addition to such imaging device(s)  104 C (or as an alternative thereto), one or more imaging devices  104 D may also be coupled to one of the sides of the implement  12  such that the imaging device(s)  104 D has a field of view  106  that allows it to capture images of an adjacent area or portion of the field disposed along such side of the implement  12 . For instance, the field of view  106  of the imaging device  104 D may be directed outwardly from the side of the implement  12  along a plane or reference line that extends generally perpendicular to the travel direction  34  of the work vehicle  10 . 
     It should be appreciated that, in alternative embodiments, the imaging device(s)  104  may be installed at any other suitable location that allows the device(s) to capture images of an adjacent portion of the field, such as by installing an imaging device(s) at or adjacent to the aft end of the work vehicle  10  and/or at or adjacent to the forward end of the implement  12 . It should also be appreciated that, in several embodiments, the imaging devices  104  may be specifically installed at locations on the work vehicle  10  and/or the implement  12  to allow images to be captured of the field both before and after the performance of a field operation by the implement  12 . For instance, by installing the imaging device  104 A at the forward end of the work vehicle  10  and the imaging device  104 C at the aft end of the implement  12 , the forward imaging device  104 A may capture images of the field prior to performance of the field operation while the aft imaging device  104 C may capture images of the same portions of the field following the performance of the field operation. Such before and after images may be analyzed, for example, to evaluate the effectiveness of the operation being performed within the field, such as by allowing the disclosed system to evaluate the amount of crop residue existing within the field both before and after the tillage operation. 
     Referring now to  FIGS. 3 and 4 , schematic views of embodiments of a computing system  100  are illustrated in accordance with aspects of the present subject matter. In general, the system  100  will be described herein with reference to the work vehicle  10  and the implement  12  described above with reference to  FIGS. 1 and 2 . However, it should be appreciated that the disclosed system  100  may generally be utilized with work vehicles having any suitable vehicle configuration and/or implements have any suitable implement configuration. 
     In several embodiments, the system  100  may include a controller  102  and various other components configured to be communicatively coupled to and/or controlled by the controller  102 , such as one or more imaging devices  104  and/or various components of the work vehicle  10  and/or the implement  12 . In some embodiments, the controller  102  is physically coupled to the work vehicle  10  and/or the implement  12 . In other embodiments, the controller  102  is not physically coupled to the work vehicle  10  and/or the implement  12  (e.g., the controller  102  may be remotely located from the work vehicle  10  and/or the implement  12 ) and instead may communicate with the work vehicle  10  and/or the implement  12  over a wireless network. 
     As will be described in greater detail below, the controller  102  may be configured to leverage a machine-learned model  128  to determine a crop residue parameter value for a portion of a field based at least in part on imagery of such portion of the field captured by one or more imaging devices  104 . In particular,  FIG. 3  illustrates a computing environment in which the controller  102  can operate to determine crop residue data  120  for at least a portion of a field based on image data  118  newly received from one or more imaging devices  104  and, further, to control one or more components of a work vehicle and/or implement (e.g., engine  22 , transmission  24 , control valve(s)  130 , etc.) based on the crop residue data  120 . That is,  FIG. 3  illustrates a computing environment in which the controller  102  is actively used in conjunction with a work vehicle and/or implement (e.g., during operation of the work vehicle and/or implement within a field). As will be discussed further below,  FIG. 4  depicts a computing environment in which the controller  102  can communicate over a network  180  with a machine learning computing system  150  to train and/or receive a machine-learned model  128 . Thus,  FIG. 4  illustrates operation of the controller  102  to train a machine-learned model  128  and/or to receive a trained machine-learned model  128  from a machine learning computing system  150  (e.g.,  FIG. 4  shows the “training stage”) while  FIG. 3  illustrates operation of the controller  102  to use the machine-learned model  128  to actively determine crop residue parameter values based on obtained imagery of a field (e.g.,  FIG. 3  shows the “inference stage”). 
     Referring first to  FIG. 3 , in general, the controller  102  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. 3 , the controller  102  may generally include one or more processor(s)  110  and associated memory devices  112  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  112  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  112  may generally be configured to store information accessible to the processor(s)  110 , including data  114  that can be retrieved, manipulated, created and/or stored by the processor(s)  110  and instructions  116  that can be executed by the processor(s)  110 . 
     In several embodiments, the data  114  may be stored in one or more databases. For example, the memory  112  may include an image database  118  for storing image data received from the imaging device(s)  104 . For example, the imaging device(s)  104  may be configured to continuously or periodically capture images of adjacent portion(s) of the field as an operation is being performed with the field. In such an embodiment, the images transmitted to the controller  102  from the imaging device(s)  104  may be stored within the image database  118  for subsequent processing and/or analysis. It should be appreciated that, as used herein, the term image data may include any suitable type of data received from the imaging device(s)  104  that allows for the crop residue coverage of a field to be analyzed, including photographs and other image-related data (e.g., scan data and/or the like). 
     Additionally, as shown in  FIG. 3 , the memory  12  may include a crop residue database  120  for storing information related to crop residue parameters for the field being processed. For example, as indicated above, based on the image data received from the imaging device(s)  104 , the controller  102  may be configured to estimate or calculate one or more values for one or more crop residue parameters associated with the field, such as a value(s) for the percent crop residue coverage for an imaged portion(s) of the field (and/or a value(s) for the average percent crop residue coverage for the field). The crop residue parameter(s) estimated or calculated by the controller  102  may then be stored within the crop residue database  120  for subsequent processing and/or analysis. 
     Moreover, in several embodiments, the memory  12  may also include a location database  122  storing location information about the work vehicle/implement  10 ,  12  and/or information about the field being processed (e.g., a field map). Specifically, as shown in  FIG. 3 , the controller  102  may be communicatively coupled to a positioning device(s)  124  installed on or within the work vehicle  10  and/or on or within the implement  12 . For example, in one embodiment, the positioning device(s)  124  may be configured to determine the exact location of the work vehicle  10  and/or the implement  12  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)  124  may be transmitted to the controller  102  (e.g., in the form of coordinates) and subsequently stored within the location database  122  for subsequent processing and/or analysis. 
     Additionally, in several embodiments, the location data stored within the location database  122  may also be correlated to the image data stored within the image database  118 . For instance, in one embodiment, the location coordinates derived from the positioning device(s)  124  and the image(s) captured by the imaging device(s)  104  may both be time-stamped. In such an embodiment, the time-stamped data may allow each image captured by the imaging device(s)  104  to be matched or correlated to a corresponding set of location coordinates received from the positioning device(s)  124 , thereby allowing the precise location of the portion of the field depicted within a given image to be known (or at least capable of calculation) by the controller  102 . 
     Moreover, by matching each image to a corresponding set of location coordinates, the controller  102  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  102  already includes a field map stored within its memory  112  that includes location coordinates associated with various points across the field, each image captured by the imaging device(s)  104  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  102  may be configured to generate a field map for the field that includes the geo-located images associated therewith. 
     Likewise, any crop residue data  120  derived from a particular set of image data (e.g., frame of imagery) can also be matched to a corresponding set of location coordinates. For example, the particular location data  122  associated with a particular set of image data  118  can simply be inherited by any crop residue data  120  produced on the basis of or otherwise derived from such set of image data  118 . Thus, based on the location data and the associated crop residue data, the controller  102  may be configured to generate a field map for the field that describes, for each analyzed portion of the field, one or more corresponding crop residue parameter values. Such a map can be consulted to identify discrepancies in or other characteristics of the crop residue at or among various granular locations within the field. 
     Referring still to  FIG. 3 , in several embodiments, the instructions  116  stored within the memory  112  of the controller  102  may be executed by the processor(s)  110  to implement an image analysis module  126 . In general, the image analysis module  126  may be configured to analyze the image data  118  to determine the crop residue data  120 . In particular, as will be discussed further below, the image analysis module  126  can cooperatively operate with or otherwise leverage a machine-learned model  128  to analyze the image data  118  to determine the crop residue data  120 . As an example, the image analysis module  126  can perform some or all of method  200  of  FIG. 5  and/or method  300  of  FIG. 6 . 
     Moreover, as shown in  FIG. 3 , the instructions  116  stored within the memory  112  of the controller  102  may also be executed by the processor(s)  110  to implement a machine-learned model  128 . In particular, the machine-learned model  128  may be a machine-learned semantic segmentation model. The machine-learned semantic segmentation model  128  can be configured to receive imagery and to process the imagery to semantically segment the imagery. 
     Referring still to  FIG. 3 , the instructions  116  stored within the memory  112  of the controller  102  may also be executed by the processor(s)  110  to implement a control module  129 . In general, the control module  129  may be configured to adjust the operation of the work vehicle  10  and/or the implement  12  by controlling one or more components of the implement/vehicle  12 ,  10 . Specifically, in several embodiments, when the crop residue parameter values determined by the image analysis module  126  differ from target or desired values, the control module  129  may be configured to adjust the operation of the work vehicle  10  and/or the implement  12  in a manner designed to modify the crop residue outcome of the operation of the work vehicle  10  and/or the implement  12 . For instance, when it is desired to have a percent crop residue coverage of 30%, the control module  129  may be configured to adjust the operation of the work vehicle  10  and/or the implement  12  so as to increase or decrease the amount of crop residue remaining in the field when the estimated percent crop residue coverage for a given imaged portion of the field (or an average estimated percent crop residue coverage across multiple imaged portions of the field) differs from the target percentage. 
     In one example, one or more imaging devices  104  can be forward-looking image devices that collect imagery of upcoming portions of the field. The image analysis module  126  can analyze the imagery to determine crop residue parameter values for such upcoming portions of the field. The control module  129  can adjust the operation of the work vehicle  10  and/or the implement  12  based on the crop residue parameter values for such upcoming portions of the field. Thus, the system  100  can proactively manage various operational parameters of the work vehicle  10  and/or the implement  12  to account for upcoming crop residue conditions in upcoming portions of the field. For example, if an upcoming portion of the field has a larger-than-average crop residue percentage, then the controller  102  can, in anticipation of reaching such section, modify the operational parameters to account for such larger-than-average crop residue and vice versa for portions with less-than-average crop residue. 
     In another example which may be additional or alternative to the example provided above, one or more imaging devices  104  can be rearward-looking image devices that collect imagery of receding portions of the field that the work vehicle  10  and/or implement  12  has recently operated upon. The image analysis module  126  can analyze the imagery to determine crop residue parameter value(s) for such receding portions of the field. The control module  129  can adjust the operation of the work vehicle  10  and/or the implement  12  based on the crop residue parameter values for such receding portions of the field. Thus, the system  100  can reactively manage various operational parameters of the work vehicle  10  and/or the implement  12  based on observed outcomes associated with current settings of such operational parameters. That is, the system  100  can observe the outcome of its current settings and can adjust the settings if the outcome does not match a target outcome. 
     It should be appreciated that the controller  102  may be configured to implement various different control actions to adjust the operation of the work vehicle  10  and/or the implement  12  in a manner that increases or decreases the amount of crop residue remaining in the field. In one embodiment, the controller  102  may be configured to increase or decrease the operational or ground speed of the implement  12  to affect an increase or decrease in the crop residue coverage. For instance, as shown in  FIG. 3 , the controller  102  may be communicatively coupled to both the engine  22  and the transmission  24  of the work vehicle  10 . In such an embodiment, the controller  102  may be configured to adjust the operation of the engine  22  and/or the transmission  24  in a manner that increases or decreases the ground speed of the work vehicle  10  and, thus, the ground speed of the implement  12 , such as by transmitting suitable control signals for controlling an engine or speed governor (not shown) associated with the engine  22  and/or transmitting suitable control signals for controlling the engagement/disengagement of one or more clutches (not shown) provided in operative association with the transmission  24 . 
     In some embodiments, the implement  12  can communicate with the work vehicle  10  to request or command a particular ground speed and/or particular increase or decrease in ground speed from the work vehicle  10 . For example, the implement  12  can include or otherwise leverage an ISOBUS Class 3 system to control the speed of the work vehicle  10 . 
     Increasing the ground speed of the vehicle  10  and/or the implement  12  may result in a relative increase in the amount of crop residue remaining in the field (e.g., relative to the amount remaining absent such increase in ground speed). Likewise, decreasing the ground speed of the vehicle  10  and/or the implement  12  may result in a relative decrease in the amount of crop residue remaining in the field (e.g., relative to the amount remaining absent such decrease in ground speed). 
     In addition to the adjusting the ground speed of the vehicle/implement  10 ,  12  (or as an alternative thereto), the controller  102  may also be configured to adjust an operating parameter associated with the ground-engaging tools of the implement  12 . For instance, as shown in  FIG. 3 , the controller  102  may be communicatively coupled to one or more valves  130  configured to regulate the supply of fluid (e.g., hydraulic fluid or air) to one or more corresponding actuators  56 ,  58 ,  60  of the implement  12 . In such an embodiment, by regulating the supply of fluid to the actuator(s)  56 ,  58 ,  60 , the controller  102  may automatically adjust the relative positioning, penetration depth, down force, and/or any other suitable operating parameter associated with the ground-engaging tools of the implement  12 . Increasing the penetration depth or down force of the ground-engaging tools may result in a relative decrease in the amount of crop residue remaining in the field (e.g., relative to the amount remaining absent such increase in penetration depth or down force). Likewise, decreasing the penetration depth or down force of the ground-engaging tools may result in a relative increase in the amount of crop residue remaining in the field (e.g., relative to the amount remaining absent such decrease in penetration depth or down force). 
     Moreover, as shown in  FIG. 3 , the controller  102  may also include a communications interface  132  to provide a means for the controller  102  to communicate with any of the various other system components described herein. For instance, one or more communicative links or interfaces  134  (e.g., one or more data buses) may be provided between the communications interface  132  and the imaging device(s)  104  to allow images transmitted from the imaging device(s)  104  to be received by the controller  102 . Similarly, one or more communicative links or interfaces  136  (e.g., one or more data buses) may be provided between the communications interface  132  and the positioning device(s)  124  to allow the location information generated by the positioning device(s)  124  to be received by the controller  102 . Additionally, as shown in  FIG. 3 , one or more communicative links or interfaces  138  (e.g., one or more data buses) may be provided between the communications interface  132  and the engine  22 , the transmission  24 , the control valves  130 , and/or the like to allow the controller  102  to control the operation of such system components. 
     It should be appreciated that the controller  102  (e.g., the image analysis module  126 ) may be configured to perform the above-referenced analysis for multiple imaged sections of the field. Each section can be analyzed individually or multiple sections can be analyzed in a batch (e.g., by concatenating imagery depicting such multiple sections). 
     Referring now to  FIG. 4 , according to an aspect of the present disclosure, the controller  102  can store or include one or more machine-learned models  128 . In particular, the machine-learned model  128  may be a machine-learned semantic segmentation model. The machine-learned semantic segmentation model  128  can be configured to receive imagery and to process the imagery to semantically segment the imagery. 
     As examples, the semantic segmentation model can be or can otherwise include various machine-learned models such as one or more artificial neural networks (“neural networks”). Example neural networks include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or other forms of neural networks. Neural networks can include multiple connected layers of neurons and networks with one or more hidden layers can be referred to as “deep” neural networks. Typically, at least some of the neurons in a neural network include non-linear activation functions. 
     In one particular example, the semantic segmentation model can include a neural network (e.g., convolutional neural network) followed by a conditional random field. The conditional random field can take into account neighboring information that helps to take into account data probabilities. For example, neighboring pixels in an image may be related. As one example, if a first pixel depicts the ground, a neighbor pixel may have a higher probability of also depicting the ground. A conditional random field can take into account neighbor labels in this way. In some embodiments of this particular example, the output of the neural network (e.g., convolutional neural network) that is provided as input to the conditional random field can be an embedding that is provided within a numerical embedding dimensional space. Alternatively, the output of the neural network that is provided that is provided as input to the conditional random field can be an explicit semantic segmentation label. In some embodiments, the output of the neural network that is provided as input to the conditional random field can be provided on a pixel by pixel basis (e.g., an embedding or label for each pixel). In other embodiments, the output of the neural network that is provided as input to the conditional random field can be provided according to a different dimensional space (e.g., not necessarily provided on a pixel by pixel basis). 
     As other examples, the semantic segmentation model can be or otherwise include a regression model (e.g., logistic regression classifier); a support vector machine; one or more decision-tree based models (e.g., random forest models); a Bayes classifier; a K-nearest neighbor classifier; a texton-based classifier; and/or other types of models including both linear models and non-linear models. 
     In some embodiments, the machine-learned semantic segmentation model can be a binary segmentation model configured to output, for each pixel, a binary label that labels such image data as residue or non-residue. In other embodiments, the machine-learned semantic segmentation model can be a multi-label segmentation model configured to output, for each pixel, a multi-label label that labels such pixel as non-residue or as one of a plurality of different types of residue (e.g., “wheat residue” vs. “corn residue” vs. “soybean residue” vs. “soil”, etc.). In another example, the label may indicate whether the residue has been crimped or otherwise processed to help it break down. Other labeling schemes can be used in addition or alternatively to these example schemes. 
     In some embodiments, the machine-learned semantic segmentation model can further provide, for each pixel, a numerical value descriptive of a degree to which it is believed that the pixel should be labeled with the corresponding label. In some instances, the numerical values provided by the machine-learned semantic segmentation model can be referred to as “confidence scores” that are indicative of a respective confidence associated with assignment of a particular label to a pixel. In some embodiments, the confidence scores can be compared to one or more thresholds to render a discrete categorical labeling. In some embodiments, only a certain number of labels (e.g., one) with the relatively largest confidence scores can be selected to render a discrete categorical prediction. 
     In some embodiments, the controller  102  can receive the one or more machine-learned models  128  from the machine learning computing system  150  over network  180  and can store the one or more machine-learned models  128  in the memory  112 . The controller  102  can then use or otherwise run the one or more machine-learned models  128  (e.g., by processor(s)  110 ). 
     The machine learning computing system  150  includes one or more processors  152  and a memory  154 . The one or more processors  152  can be any suitable processing device such as described with reference to processor(s)  110 . The memory  154  can include any suitable storage device such as described with reference to memory  112 . 
     The memory  154  can store information that can be accessed by the one or more processors  152 . For instance, the memory  154  (e.g., one or more non-transitory computer-readable storage mediums, memory devices) can store data  156  that can be obtained, received, accessed, written, manipulated, created, and/or stored. In some embodiments, the machine learning computing system  150  can obtain data from one or more memory device(s) that are remote from the system  150 . 
     The memory  154  can also store computer-readable instructions  158  that can be executed by the one or more processors  152 . The instructions  158  can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions  158  can be executed in logically and/or virtually separate threads on processor(s)  152 . 
     For example, the memory  154  can store instructions  158  that when executed by the one or more processors  152  cause the one or more processors  152  to perform any of the operations and/or functions described herein. 
     In some embodiments, the machine learning computing system  150  includes one or more server computing devices. If the machine learning computing system  150  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)  128  at the controller  102 , the machine learning computing system  150  can include one or more machine-learned models  140 . For example, the models  140  can be the same as described above with reference to the model(s)  128 . 
     In some embodiments, the machine learning computing system  150  can communicate with the controller  102  according to a client-server relationship. For example, the machine learning computing system  150  can implement the machine-learned models  140  to provide a web service to the controller  102 . For example, the web service can provide image analysis for crop residue determination as a service. 
     Thus, machine-learned models  128  can be located and used at the controller  102  and/or machine-learned models  140  can be located and used at the machine learning computing system  150 . 
     In some embodiments, the machine learning computing system  150  and/or the controller  102  can train the machine-learned models  128  and/or  140  through use of a model trainer  160 . The model trainer  160  can train the machine-learned models  128  and/or  140  using one or more training or learning algorithms. One example training technique is backwards propagation of errors (“backpropagation”). Gradient-based or other training techniques can be used. 
     In some embodiments, the model trainer  160  can perform supervised training techniques using a set of labeled training data  162 . For example, the labeled training data  162  can include image pixels, where each image pixel has been labeled (e.g., manually by an expert and/or manually by a user of the models) with a “correct” or ground-truth label. Thus, each training example can include an image and a corresponding ground-truth semantic segmentation for the image. In some instances, a semantic segmentation can be referred to as a semantic segmentation mask. 
     The labels used for the training data  162  can match any of the example labelling schemes described herein, including binary labels (e.g., residue or not residue), multi-label labels (e.g., wheat residue, corn residue, not residue, etc.), or other labelling schemes. In other embodiments, the model trainer  160  can perform unsupervised training techniques using a set of unlabeled training data  162 . The model trainer  160  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  160  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 the user. This process may be referred to as “personalization” of the models and may allow the user to further train the models to provide improved (e.g., more accurate) predictions for unique field conditions experienced by the user. 
     The network(s)  180  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)  180  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  150  may also include a communications interface  164  to communicate with any of the various other system components described herein. 
       FIGS. 3 and 4  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  102  can include the model trainer  160  and the training dataset  162 . In such embodiments, the machine-learned models  128  can be both trained and used locally at the controller  102 . As another example, in some embodiments, the controller  102  is not connected to other computing systems. 
     Referring now to  FIG. 5 , a flow diagram of one embodiment of a method  200  for controlling agricultural machinery based on image data of a field is illustrated in accordance with aspects of the present subject matter. In general, the method  200  will be described herein with reference to the work vehicle  10  and the implement  12  shown in  FIGS. 1 and 2 , as well as the various system components shown in  FIGS. 3 and/or 4 . However, it should be appreciated that the disclosed method  200  may be implemented with work vehicles and/or implements having any other suitable configurations and/or within systems having any other suitable system configuration. In addition, although  FIG. 5  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 methods 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 ( 202 ), the method  200  may include obtaining image data that depicts a portion of a field. For example, as indicated above, the controller  102  may be coupled to one or more imaging devices  104  configured to capture images of various portions of the field. 
     The image data obtained at  202  can include a plurality of pixels. In some embodiments, the plurality of pixels correspond to or otherwise include an entirety of the image data, such that all of the image data is analyzed. In other embodiments, the plurality of pixels can correspond to or otherwise include only a portion or subset of the image data. Using pixels that correspond to only a subset of the image data can enable reductions in processing time and requirements. 
     In some embodiments, the image data obtained at ( 202 ) can include a single image frame. Thus, in some embodiments, the method  200  can be performed iteratively for each new image frame as such image frame is received. For example, method  200  can be performed iteratively in real-time as new images are received from the imaging devices  104  while the imaging devices  104  are moved throughout the field (e.g., as a result of being physically coupled to the vehicle  10  or implement  12  which is being operated in the field). 
     In other embodiments, the image data obtained at ( 202 ) can include a plurality of image frames. For example, the plurality of image frames can be concatenated or otherwise combined and processed as a single batch (e.g., by way of a single performance of method  200  over the batch). 
     Referring still to  FIG. 5 , at ( 204 ), the method  200  may include preconditioning the image data. For example, the image analysis module  126  of the controller  102  may be configured to precondition the image data. 
     In some embodiments, preconditioning the image data can include performing histogram equalization (e.g., for brightness balance). In some embodiments, preconditioning the image data can include performing camera calibration (e.g., to rectify the image data so that lens distortion does not have as significant an effect). In some embodiments, preconditioning the image data can include enhancing the image to announce a specific feature. For example, the enhanced feature can be a soil and/or crop-specific feature. In some embodiments, preconditioning the image data can include changing the contrast, applying one or more filters, changing the reflectance or frequency wavelengths, and/or other processing operations. In some embodiments, the preconditioning performed at ( 204 ) can be specific to the particular segmentation model being used. 
     At ( 206 ), the method  200  may include inputting the image data into a machine-learned semantic segmentation model. For instance, as indicated above, the image analysis module  126  of the controller  102  may be configured to input the image data into machine-learned semantic segmentation model  128 . 
     At ( 208 ), the method  200  may include receiving a semantic segmentation of the image data as an output of the machine-learned semantic segmentation model. The semantic segmentation can provide a respective label for each of the plurality of pixels that labels the pixel as a residue pixel or a non-residue pixel. For example, as indicated above, the image analysis module  126  of the controller  102  may be configured to receive a respective label for each pixel as an output of the machine-learned semantic segmentation model  128 . As one example, in some embodiments, each pixel i can be labeled as residue (l i =1) or non-residue (l i =0). Alternatively, the label can be in the range l i ∈[0,1]. 
     Thus, as described above, in some embodiments, the label provided for each pixel at ( 208 ) can be a binary label that labels the pixel as residue or non-residue. However, in other embodiments, the model can be a multi-label label that labels the pixel as non-residue or as one of a plurality of different types of residue (e.g., “wheat residue” vs. “corn residue” vs. “soybean residue” vs. “soil”, etc.). Other labeling schemes can be used in addition or alternatively to these example schemes. 
     Additionally, at ( 210 ), the method  200  may include determining a crop residue parameter value for the image data based at least in part on the respective labels received for the plurality of pixels. The crop residue parameter value can provide a value for one or more different crop residue parameters. As one example, the crop residue parameter value can be a percent crop residue cover for the portion of the field depicted by the image data. 
     In one example, as indicated above, the image analysis module  126  of the controller  102  may, in accordance with aspects of the present subject matter, be configured to perform various processing techniques on the labels provided for the pixels to determine the crop residue parameter value for the image data. For example, various averaging or combinatorial techniques can be used to determine the crop residue parameter value from the labels. The crop residue parameter value determined at ( 210 ) can be stored in memory and/or logically associated with the image data and/or the portion of the field. 
     More particularly, as one example,  FIG. 6  provides a flow diagram of one embodiment of a method  300  for determining a crop residue parameter value based on labels in accordance with aspects of the present subject matter. In general, the method  300  will be described herein with reference to the work vehicle  10  and the implement  12  shown in  FIGS. 1 and 2 , as well as the various system components shown in  FIGS. 3 and/or 4 . However, it should be appreciated that the disclosed method  300  may be implemented with work vehicles and/or implements having any other suitable configurations and/or within systems having any other suitable system configuration. In addition, although  FIG. 6  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 methods 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. 6 , at ( 302 ), the method  300  may include obtaining respective labels associated with the plurality of pixels. For example, the image analysis module  126  of the controller  102  may be configured to receive the labels as an output from the machine-learned semantic segmentation model  128 . 
     At ( 304 ), the method  300  may include determining an approximate ground area for each of the plurality of pixels. For example, the image analysis module  126  of the controller  102  may be configured to determine an approximate ground area for each pixel. 
     In some embodiments, the image analysis module  126  can determine the approximate ground area associated with each pixel based at least in part on one or both of: imaging device calibration information (e.g., primarily intrinsic calibration) or topography data associated with the field. In particular, in some embodiments, the approximate ground area for each pixel can be inferred using geometry and trigonometry of a rectangular pyramid and/or using other techniques including knowledge of the 3D-ness (topology) of the ground below the sensor. In some embodiments, the topology of the ground can be measured using ground-sensing sensors such as RADAR sensors, ultrasonic sensors, LIDAR sensors, infrared sensors, and/or other sensors that provide “elevation” height of the ground. The elevation can be expressed as a true elevation or can be expressed as a distance to the ground from the sensor. 
     Thus, in some embodiments, the image analysis module  126  can determine the approximate ground area for each image pixel i. The approximate ground area can be denoted as G i . 
     At ( 306 ), the method  300  may include determining a respective weight w i  for each pixel based on its approximate ground area G i . For example, the image analysis module  126  of the controller  102  may be configured to determine a respective weight for each pixel based on its approximate ground area. 
     In one example, the weight w i  for each pixel may simply be equal to the ground area G i . In other embodiments, various relationships (e.g., linear or non-linear relationships) between ground area and weight can be used to determine the weight for each pixel based on its ground area. 
     At ( 308 ), the method  300  may include determining a weighted average of the labels weighted according to their respective weights. For example, the image analysis module  126  of the controller  102  may be configured to determine the weighted average of the labels weighted according to their respective weights. 
     Thus, in some embodiments, the weighted average of the labels can be determined according to the following expression: 
               L   average     =         ∑     i   =   1     R     ⁢           ⁢       w   i     ⁢     l   i             ∑     i   =   1     R     ⁢           ⁢     w   i               
where R is the total number of pixels.
 
     In some embodiments, the average label (e.g., computed according to the expression above) can be returned as the crop residue parameter value for the image data. In other embodiments, some further relationship (e.g., linear or non-linear relationship) between the average label and the crop residue parameter value can be used to determine the crop residue parameter value from the average label. For example, the average label can be multiplied by 100 and then returned as a percent crop residue cover for the portion of the field associated with the image data. 
     Referring again to method  200  of  FIG. 5 , after determining the crop residue parameter value for the image data at  210 , then at ( 212 ), the method  200  may include controlling the operation of at least one of a work vehicle or an implement as the implement is being towed by the work vehicle across a field based at least in part on the crop residue parameter value. For example, as indicated above, the control module  129  of the controller  102  of the disclosed system  100  may be configured to control the operation of the work vehicle  10  and/or the implement  12 , such as by controlling one or more components of the work vehicle  10  and/or the implement  12  to allow an operation to be performed within the field (e.g., a tillage operation). 
     As one example, in some embodiments, when the crop residue parameter value determined at ( 210 ) differs from a target value set for such parameter, the controller  102  may be configured to actively adjust the operation of the work vehicle  10  and/or the implement  12  in a manner that increases or decreases the amount of crop residue remaining within the field following the operation being performed (e.g., a tillage operation), such as by adjusting the ground speed at which the implement  12  is being towed and/or by adjusting one or more operating parameters associated with the ground-engaging elements of the implement  12 , including, for example, down force, angle or position relative to the ground (e.g., height), and/or other operational parameters associated with the ground-engaging elements. 
     Furthermore, in some embodiments, the method  200  may further include determining or updating a total crop residue parameter value for the entire field based on the crop residue parameter value determined at ( 210 ). For example, the total crop residue parameter value can be an overall average, a running average, auto-regressive filtering techniques (e.g., auto-regressive moving average filters), and/or other averaging or filtering techniques. The total crop residue parameter value can also be expressed in the form of a field map for the field that describes, for each analyzed portion of the field, one or more corresponding crop residue parameter values. Such a map can be consulted to identify discrepancies in or other characteristics of the crop residue at or among various granular locations within the field. 
     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.