Patent Publication Number: US-2021185882-A1

Title: Use Of Aerial Imagery For Vehicle Path Guidance And Associated Devices, Systems, And Methods

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application claims the benefit under 35 U.S.C. § 119(e) to U.S. Provisional Application 62/952,807, filed Dec. 23, 2019, and entitled “Use of Aerial Imagery for Vehicle Path Guidance and Associated Devices, Systems, and Methods,” which is hereby incorporated herein by reference in its entirety for all purposes. 
    
    
     TECHNICAL FIELD 
     The disclosure relates generally to devices, systems, and methods for use of aerial imagery for vehicle guidance for use with agricultural equipment navigation. More particularly this disclosure relates to devices, systems, and methods for use of aerial imagery to establish agricultural vehicle guidance paths. This disclosure has implications across many agricultural and other applications. 
     BACKGROUND 
     As is appreciated, during agricultural operations planters and/or other implements do not always follow the planned vehicle guidance paths. For example, a planting implement may not accurately follow a planned guidance path such that crop rows are planted at a variable offset from the planned guidance path. In these situations, the planned guidance path generated for planting cannot be reused during subsequent operations, such as spraying and harvest. 
     Various vehicle guidance systems are known in the art and include vehicle-mounted visual row following systems. These known mounted vision systems are known to be affected by wind, sections of missing crops, uncertainty about counting rows, and downed plants, among other things. Further these known mounted vision systems often have difficulty identifying crop rows once the plant foliage has grown to the point where bare ground is nearly or wholly obscured. 
     Alternative known vehicle guidance systems use mechanical feelers. These known mechanical feeler systems are affected by downed corn, mechanical wear, and speed of field operations. Further these known mechanical feeler systems require specialized equipment to be mounted on the tractor or other agricultural vehicle for operation. 
     There is a need in the art for devices, systems, and methods for establishing vehicle guidance paths for agricultural operations. 
     BRIEF SUMMARY 
     Disclosed herein are various devices, systems, and methods for use of aerial imagery for establishing, transmitting and/or storing agricultural vehicle guidance paths. 
     In Example 1, an aerial guidance system, comprising an imaging device constructed and arranged to generate aerial images of a field, and a processor in operative communication with the imaging device, wherein the processor is configured to process the aerial images and generate guidance paths for traversal by agricultural implements. 
     Example 2 relates to the aerial guidance system of Example 1, further comprising a central storage device in operative communication with the processor. 
     Example 3 relates to the aerial guidance system of Example 1, wherein the imaging device is a satellite. 
     Example 4 relates to the aerial guidance system of Example 1, wherein the imaging device is a drone. 
     Example 5 relates to the aerial guidance system of Example 1, further comprising a monitor in operative communication with the processor and configured to display the aerial images to a user. 
     In Example 6, a method of generating guidance paths for agricultural processes, comprising acquiring overhead images via an imaging device, identifying crop rows in the acquired aerial images, and generating one or more guidance paths for traversal by an agricultural implement. 
     Example 7 relates to the method of Example 6, further comprising displaying the guidance paths on a monitor. 
     Example 8 relates to the method of Example 6, further comprising adjusting manually the guidance paths by a user. 
     Example 9 relates to the method of Example 6, further comprising determining an actual location of one or more geo-referenced ground control points and adjusting the one or more guidance paths based on the actual location of one or more geo-referenced ground control points in the aerial images. 
     Example 10 relates to the method of Example 6, wherein the imaging device is a terrestrial vehicle, manned aerial vehicle, satellite, or unmanned aerial vehicle. 
     Example 11 relates to the method of Example 10, wherein the imaging device is an unmanned aerial vehicle. 
     Example 12 relates to the method of Example 6, further comprising displaying the one or more guidance paths on a display or monitor. 
     Example 13 relates to the method of Example 6, further comprising providing a software platform for viewing the one or more guidance paths. 
     In Example 14, a method for providing navigation guidance paths for agricultural operations comprising obtaining aerial images of an area of interest, processing the aerial images to determine actual locations of one or more crop rows, and generating guidance paths based on actual locations of the one or more crop rows. 
     Example 15 relates to the method of Example 14, further comprising performing distortion correction on the aerial images. 
     Example 16 relates to the method of Example 14, further comprising identifying actual locations of one or more geo-referenced ground control points found in the aerial images. 
     Example 17 relates to the method of Example 16, wherein the one or more geo-referenced ground control points comprise at least one of a terrain feature, a road intersection, or a building. 
     Example 18 relates to the method of Example 14, wherein the aerial images are obtained in an early stage of a growing season. 
     Example 19 relates to the method of Example 14, further comprising inputting terrain slope data to determine actual crop row locations and spacing. 
     Example 20 relates to the method of Example 14, further comprising performing resolution optimization on the aerial images. 
     While multiple embodiments are disclosed, still other embodiments of the disclosure will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments of the invention. As will be realized, the disclosure is capable of modifications in various obvious aspects, all without departing from the spirit and scope of the disclosure. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an exemplary depiction of a field with a guidance path, according to one implementation. 
         FIG. 2A  is a process diagram of an overview of the system, according to one implementation. 
         FIG. 2B  is a schematic overview of certain components of the system, according to one implementation. 
         FIG. 2C  is a schematic overview of certain components of the system, according to one implementation. 
         FIG. 3  is a schematic depiction of the system, according to one implementation. 
         FIG. 4  is an exemplary aerial image, according to one implementation. 
         FIG. 5  is an exemplary low resolution aerial image, according to one implementation. 
         FIG. 6  is a schematic diagram of the system including a cross sectional view of a field, according to one implementation. 
         FIG. 7A  shows an exemplary guidance path for a six-row implement, according to one implementation. 
         FIG. 7B  shows exemplary guidance paths for a two-row implement, according to one implementation. 
         FIG. 8  shows an exemplary guidance path navigating about an obstacle, according to one implementation. 
         FIG. 9  shows a display for use with the system, according to one implementation. 
     
    
    
     DETAILED DESCRIPTION 
     The various implementations disclosed or contemplated herein relate to devices, systems, and methods for the use of aerial or overhead imagery to establish vehicle guidance paths for use by a variety of agricultural vehicles. In certain implementations, these vehicle guidance paths may be used in agricultural applications, such as planting, harvesting, spraying, tilling, and other operations as would be appreciated. The disclosed ariel system represents a technological improvement in that it establishes optimal guidance paths for agricultural vehicles for traversing a field and/or performing desired operations when previous guidance paths, such as planting guidance paths cannot be used. In certain implementations the aerial system establishes guidance paths via a software-integrated display platform such as SteerCommand® or other platform that would be known and appreciated by those of skill in the art. 
     Certain of the disclosed implementations of the imagery and guidance systems, devices, and methods can be used in conjunction with any of the devices, systems, or methods taught or otherwise disclosed in U.S. application Ser. No. 16/121,065, filed Sep. 1, 2018, and entitled “Planter Down Pressure and Uplift Devices, Systems, and Associated Methods,” U.S. Pat. No. 10,743,460, filed Oct. 3, 2018, and entitled “Controlled Air Pulse Metering Apparatus for an Agricultural Planter and Related Systems and Methods,” U.S. application Ser. No. 16/272,590, filed Feb. 11, 2019, and entitled “Seed Spacing Device for an Agricultural Planter and Related Systems and Methods,” U.S. application Ser. No. 16/142,522, filed Sep. 26, 2018, and entitled “Planter Downforce and Uplift Monitoring and Control Feedback Devices, Systems and Associated Methods,” U.S. application Ser. No. 16/280,572, filed Feb. 20, 2019 and entitled “Apparatus, Systems and Methods for Applying Fluid,” U.S. application Ser. No. 16/371,815, filed Apr. 1, 2019, and entitled “Devices, Systems, and Methods for Seed Trench Protection,” U.S. application Ser. No. 16/523,343, filed Jul. 26, 2019, and entitled “Closing Wheel Downforce Adjustment Devices, Systems, and Methods,” U.S. application Ser. No. 16/670,692, filed Oct. 31, 2019, and entitled “Soil Sensing Control Devices, Systems, and Associated Methods,” U.S. application Ser. No. 16/684,877, filed Nov. 15, 2019, and entitled “On-The-Go Organic Matter Sensor and Associated Systems and Methods,” U.S. application Ser. No. 16/752,989, filed Jan. 27, 2020, and entitled “Dual Seed Meter and Related Systems and Methods,” U.S. application Ser. No. 16/891,812, filed Jun. 3, 2020, and entitled “Apparatus, Systems, and Methods for Row Cleaner Depth Adjustment On-The-Go,” U.S. application Ser. No. 16/921,828, filed Jul. 6, 2020, and entitled “Apparatus, Systems and Methods for Automatic Steering Guidance and Visualization of Guidance Paths,” U.S. application Ser. No. 16/939,785, filed Jul. 27, 2020, and entitled “Apparatus, Systems and Methods for Automated Navigation of Agricultural Equipment,” U.S. application Ser. No. 16/997,361, filed Aug. 19, 2020, and entitled “Apparatus, Systems, and Methods for Steerable Toolbars,” U.S. application Ser. No. 16/997,040, filed Aug. 19, 2020, and entitled “Adjustable Seed Meter and Related Systems and Methods,” U.S. application Ser. No. 17/011,737, filed Aug. 3, 2020, and entitled “Planter Row Unit and Associated Systems and Methods,” U.S. application Ser. No. 17/060,844, filed Oct. 1, 2020, and entitled “Agricultural Vacuum and Electrical Generator Devices, Systems, and Methods,” U.S. application Ser. No. 17/105,437, filed Nov. 25, 2020, and entitled “Devices, Systems And Methods For Seed Trench Monitoring And Closing,” and U.S. application Ser. No. 17/127,812, filed Dec. 18, 2020, and entitled “Seed Meter Controller and Associated Devices, Systems, and Methods,” each of which is incorporated herein. 
     Returning to the present disclosure, the various systems, devices and methods described herein relate to technologies for the generation of guidance paths for use in various agricultural applications and may be referred to herein as a guidance system  100 , though the various methods and devices and other technical improvements disclosed herein are also of course contemplated. 
     The disclosed guidance system  100  can generally be utilized to generate paths  10  for use by agricultural vehicles as the vehicle traverses a field or fields. For illustration,  FIG. 1  shows an exemplary guidance path  10  between crop rows  2 . It is understood that as discussed herein, a guidance path  10  can relate to the route to be taken by the center of an agricultural implement so as to plot a path  10  through a field or elsewhere to conduct an agricultural operation, as would be readily appreciated by those familiar with the art. 
     In these implementations, the vehicle guidance paths  10  may include heading and position information, such as GPS coordinates indicating the location(s) where the tractor and/or other vehicle should be driven for proper placement within a field, such as between the crop rows  2 , as has been previously described in the incorporated references. It would be appreciated that various agricultural vehicles include a GPS unit (shown for example at  22  in  FIG. 3 ) for determining the position of the vehicle within a field at any given time. This GPS unit may work in conjunction with the system  100 , and optionally an automatic steering system, to negotiate the tractor or other vehicle along the guidance paths  10 , as would be appreciated. 
     As would be understood, the guidance paths  10  are used for agricultural operations including planting, spraying, and harvesting, among others. In various known planting or other agricultural systems, as discussed in many of the references incorporated herein, vehicle guidance paths  10  are plotted in advance of operations to set forth the most efficient, cost effective, and/or yield maximizing route for the tractor or other vehicle to take through the field. Additionally, or alternatively, the generated guidance paths  10  may be used for on-the-go determinations of vehicle paths and navigation. 
     The various guidance system  100  implementations disclosed and contemplated herein may not be affected by wind, sections of missing crops, uncertainty about counting rows, and/or downed crops, as experienced by prior known systems. In certain implementations, the aerial imagery is gathered prior to full canopy growth such that the visual obstruction of the ground at later stages of plant growth will not affect the establishment of vehicle guidance paths. In alternative implementations, the aerial imagery may be gathered at any time during a growing cycle. 
     In certain implementations, the system  100  includes geo-reference ground control points. Geo-referenced ground control points may include various static objects with known positions (known GPS coordinates, for example). In another example geo-referenced ground control points may include temporary, semi-permanent, or permanent reference targets placed in and/or around an area of interest. The positions of these geo-referenced ground control points are known and may then be integrated into the aerial imagery to create geo-referenced imagery with high accuracy, as will be discussed further below. 
     It is appreciated that in many instances a guidance system for a planter generates planned guidance paths for use during planting operations, as is discussed in various of the incorporated references. In one example, as noted above, during planting operations the planter and/or associated implement(s) often do not accurately follow the planned guidance paths during planting, thereby planting crop rows  2  at a variable offset from the prior planned planting guidance paths. Deviation from the planned guidance paths may be caused by of variety of factors including GPS drift, uneven terrain, unforeseen obstacles, or other factors as would be appreciated by those of skill in the art. The various implementations disclosed herein allow for setting subsequent vehicle guidance paths  2  that correspond to the actual crop rows  2  rather than estimates of crop row  2  locations derived from the prior planned planting guidance paths that may no longer give an accurate depiction of the location of crop rows  2  within a field. 
       FIGS. 2A-C  depict exemplary implementations of the guidance system  100 . The system  100  according to these implementations includes one or more optional steps and / or sub-steps that can be performed in any order or not at all. In one optional step, the system  100  obtains imagery (box  110 ), such as from a satellite, unmanned aerial vehicle, and/or other high altitude imaging device or devices. In a further optional step, the system  100  processes the imagery (box  120 ), such as by performing stitching, distortion correction, resolution optimization, image recognition and/or pattern recognition, each of which will be detailed further below. In another optional step, the system  100  generates guidance paths (box  140 ) using the imagery data and various other inputs and operating parameters as would be appreciated. In a still further optional step, the system  100  allows for various adjustments to the imagery, data, and/or generated guidance paths to be made (box  150 ). Each of these optional steps and the sub-steps and components thereof will be discussed further below. 
     Imagery Acquisition 
     In various implementations, the system  100  obtains or receives aerial or other overhead imagery (box  110 ) of the area of interest. As shown in  FIG. 3 , the aerial imagery may be obtained via one or more imagers  30 . The imager  30  may be one or more of a satellite, an unmanned aerial vehicle (also referred to herein as a “drone” or “UAV”), a manned aerial vehicle (such as a plane), one or more cameras mounted to an terrestrial or ground based vehicle, or any other device or system capable of capturing and recording aerial or overhead imagery as would be appreciated by those of skill in the art. 
     Turning back to  FIG. 2B , in some implementations, the aerial imagery is captured (box  110 ) before the crop canopy obstructs the view of the ground, thereby obscuring visual identification of the crop rows (shown for example at  2  in  FIG. 1 ) via the contrast between the plant matter and the soil. In alternative implementations, the aerial imagery is captured (box  110 ) at any other time in the growing cycle and various alternative image processing techniques may be implemented to identify the location of crop rows  2 , some of which will be further described below. As would be appreciated with high resolution imagery, a processing system may identify individual crop rows  2  even from a fully grown canopy. 
     For use in navigational path planning, the images used to identify crop rows  2  and plot guidance paths  10  may have a high degree of absolute or global positional accuracy. In practice, the latitude and longitude or other positional coordinates of each pixel, or subset of pixels, in the image may be known or otherwise approximated with a high degree of accuracy. 
     As shown in  FIG. 2B , when capturing aerial imagery (box  110 ) the system  100  may additionally capture and record various data including but not limited to camera orientation data (box  112 ), global positioning system (GPS)/global navigation satellite system (GNSS) data (box  114 ), images (box  116 ), and geo-referenced point data (box  118 ). In various implementations, the imager, shown in  FIG. 3 , may include a variety of sensors such as a GPS sensor  32 , an inertial measurement unit  34 , altimeter  36 , or other sensor(s) as would be appreciated by those of skill in the art, for the collection and recording of various data. 
     As shown in  FIGS. 2B and 3 , in various implementations, the GPS sensor  32  may record the positional information of the imager  30 , such as a drone, during image capture (box  110 ). The positional information, such as GPS data (box  114 ), may then be extrapolated and used to generate positional information for the images (box  116 ). In certain implementations, the GPS sensor  32  is a Real-Time-Kinematic (RTK) corrected GPS configured to provide the required level of absolute accuracy. As would be understood the GPS sensor  32  is at a known position relative to the imager  30 /point of capture of the imager  30  configured to capture the aerial imagery (box  110 ). In these implementations, the known position of the GPS  32  is utilized by the system  100  to geo-reference the images (box  116 ). 
     In further implementations, the imager  30  includes an inertial measurement unit  34  to capture data regarding the orientation of the imager  30  during image capture (box  110 ). In certain implementations, the inertial measurement unit  34  may capture and record data regarding the roll, pitch, and yaw of the imager  30  at specific points that correspond to locations within the images (box  116 ). This inertial measurement data may be integrated into the captured imagery such as to improve the accuracy of the positional information within the images (box  116 ). That is, the inertial data may allow the system  100  to more accurately place the subject item in three-dimensional space and therefore more accurately plot guidance, as discussed herein. 
     Continuing with  FIGS. 2B and 3 , in further implementations, the imager  30  may include an altimeter  36  or other sensor to determine the height of the imager  30  relative to the ground. As with the inertial measurement unit  34  discussed above, data relating to the height/altitude at which the images are acquired by improve the geo-referencing accuracy and as a result the overall accuracy of the system  100  can be improved. 
     In one specific example, the system  100  may use a senseFly eBee RTK drone as the imager  30  to collect the orientation (box  112 ), position (box  114 ), and image (box  116 ) data followed by data processing using DroneDeploy software, as will be discussed further below. In these and other implementations, images may be captured (box  110 ) with 1.2 cm image location accuracy. 
     In certain implementations, the aerial imagery optionally includes and/or is super imposed with geo-referenced ground control points (box  118  in  FIG. 2B ), examples of which are shown in  FIG. 4  at A-E. Various exemplary geo-referenced ground control points may include, a road intersection A, a stream intersection B, a rock outcrop C, a bridge D, a corner of a field E, a structure, a feature on a structure, among others as would be appreciated by those of skill in the art. In further implementations, the guidance system  100  may include geo-referenced ground control points specifically placed in or on the ground and/or field, such as a labeled marker F. 
     In certain implementations, the system  100  records the location of one or more geo-referenced ground control points. In certain implementations, the location is recorded as a set of GPS coordinates. In various implementations, the system  100  utilizes the one or more geo-referenced ground control points to assist in proper alignment of aerial imagery and guidance paths with to a navigation system, as will be discussed further below. As would be understood, certain geo-referenced ground control points will remain the same year over year or season over season such that the data regarding these stable geo-referenced ground control points may be retained by the system  100  to be reused during multiple seasons. 
     Continuing with  FIG. 2B , in certain implementations, uncorrected GPS data (box  114 ) may be used in conjunction with the geo-referenced ground control points (box  118 ) to correct image location data and remove much of the absolute error inherent to image capture. In certain implementations, commercially available software, such as DroneDeploy or Pix4D, can be used with one or more geo-referenced ground control points (shown for example in  FIG. 4  at A-F) with known GPS coordinates or other absolute position information (box  114  in  FIG. 2B ) to assign GPS coordinates and/or absolute position information to the corresponding pixels in the imagery. The software may then extrapolate these coordinates out to the other pixels in the image, effectively geo-referencing the entire image to the proper navigational reference frame. 
     In some implementations, the system  100  may acquire additional data, via the imaging devices or otherwise, such as lidar, radar, ultrasonic, or other data regarding field characteristics. In various of these implementations, the aerial imagery (box  110  of  FIG. 2B ) and/or other data can be used to create 2D or 3D maps of the fields or other areas of interest. 
     In still further implementations, the system  100  may record information relating to crop height. For example, crop height can be recorded as part of 3D records. In various implementations, crop height data can be used for plant identification and/or enhancing geo-referencing processes described above. 
     Storage 
     Continuing with  FIGS. 2B and 3 , in another optional step, the obtained imagery (box  110 ), data regarding geo-referenced ground control points (box  118 ), and/or other data is sent from the imager  30  to a storage device  40  such as a cloud-based storage system  40  or other server  40  as would be appreciated. In some implementations, cloud-based system  40  or other server  40  includes a data storage component  42  such as a memory  42 , a central processing unit (CPU), a graphical user interface (GUI)  46 , and an operating system (O/S)  48 . In some implementations, the imagery (box  110 ) and other data (such as that of boxes  112 - 118 ) is stored in the data storage component  42  such as a memory  42  which may include a database or other organizational structure as would be appreciated. 
     In various implementations, the cloud  40  or server system  40  includes a central processing unit (CPU)  44  for processing (box  120 ) the imagery (box  110 ) from storage  42  or otherwise received from the imager  30 , various optional processing steps will be further described below. Further, in certain implementations, a GUI  46  and/or O/S  48  are provided such that a user may interact with the various data at this location. 
     As shown in  FIG. 3 , in various implementations, a tractor  20  or display  24  associated with a tractor  20  or other vehicle is in electronic communication with the server  40  or cloud  40 . In some implementations, the server  40  or data therefrom may be physically transported to the display  24  via hardware-based storage as would be appreciated. In alternative implementations, the server  40 /cloud  40  or data therefrom is transported to the display  24  via any appreciated wireless connection, such as via the internet, Bluetooth, cellular signal, or other methods as would be appreciated. In certain implementations, the display  24  is located in or on the tractor  20  and may be optionally removable from the tractor  20  to be transportable between agricultural vehicles  20 . 
     In some implementations, the gathered imagery may be stored on a central server  40  such as a cloud server  40  or other centralized system  40 . In some of these implementations, individual users, in some instances across an enterprise, may access the cloud  40  or central server  40  to acquire imagery for a particular field or locations of interest. In some implementations, the image processing, discussed below, occurs on or in connection with the central storage device  40 . 
     Image Processing 
     Turning back to  FIG. 2B  and  FIG. 3 , in another optional step, the obtained aerial imagery (box  110 ) is processed via an image processing sub-system (box  120 ), the image processing sub-system (box  120 ) includes one or more optional steps that can be performed in any order or not at all, shown in one implementation in  FIG. 2B . The image processing sub-system (box  120 ) is configured to use various inputs, including aerial imagery (box  110 ), to identify the crop rows  2  (shown for example in  FIG. 1 ). In various implementations, the image processing sub-system (box  120 ) is executed on a processor  44  within the central server  40 , and/or on a display  24  and processing components associated therewith, various alternative computing devices may be implemented as would be appreciated by those of skill in the art. 
     As shown in  FIG. 2B , in some implementations, the image processing sub-system (box  120 ) includes one or more optional sub-steps including image stitching (box  121 ), distortion correction (box  122 ), resolution optimization (box  124 ), image recognition (box  126 ), and/or pattern recognition (box  128 ). These and other optional sub-steps can be performed in any order or not at all. Further, in some implementations, the one or more of the optional sub-steps can be performed more than once or iteratively. 
     As also shown in  FIG. 2B , various image process steps (box  120 ) may be conducted via known processing software such as Pix4D, DroneDeploy, Adobe Lightroom, Adobe After Effect, PTLens, and other software system known in the art. 
     Turning to the implementation of  FIG. 2B  more specifically, in one optional processing (box  120 ) sub-step, the captured images (shown at  FIG. 2A  at box  110 ) may be stitched together (box  121 ), that is, combining the images having overlapping fields of view and/or various captured details and locations to produce a combined image featuring a combination of the images to comprehensively and accurately image the subject field, as would be understood. 
     In use according to these implementations, the imager  30 , shown in  FIG. 3 , may acquire multiple images of the same location through multiple passes and/or certain images may contain overlapping areas. As shown in  FIG. 2B , in these situations, the images may be stitched together (box  121 ) to create a cohesive, accurate high-resolution image of the area of interest, without duplication. As would be appreciated, by stitching together images, a higher resolution image may be obtained. 
     In a further optional sub-step shown in  FIG. 2B , various camera and perspective distortions may be corrected (box  122 ). Distortion correction (box  122 ) may be implemented to maintain or improve the positional accuracy of the imagery (box  110 ). In some implementations, fidelity of the positional data (boxes  114 ,  118 ) associated with the imagery (box  110 ) may be improved via various known geo-referencing techniques as would be understood and appreciated by those of skill in the art. 
     In certain implementations, the distortion correction (box  122 ) shown in  FIG. 2B  corrects for various distortions in the images (box  116 ) such as those caused by various lens types used to obtain the images (box  116 ) such as fisheye lenses. Various other types of distortions that may be corrected for include optical distortion, barrel distortion, pin cushion distortion, moustache distortion, perspective distortion, distortion caused by the type and shape of lens used, and other types of distortion known to those of skill in the art. These various types of distortion may be corrected via known image processing techniques, as would be appreciated. 
     In further implementations, and as also shown in  FIG. 2B , the imagery may be optionally processed (box  120 ) and the accuracy of one or more geo-referenced ground control points (shown for example in  FIG. 4  at A-F) may be improved by applying additional data inputs such as, but not limited to, data recorded and/or configured during planting. Examples of this data may include the amount of space between planted rows, the recorded position of the tractor during planting, the position of the planting implement itself during planting, the number of rows on the planting implement, and the position in the field where planting was started and/or halted. 
     Continuing with  FIG. 2B , in some implementations, the crop rows  2  (shown for example in  FIG. 1 ) are identified using the aerial imagery (box  110 ). Using the known actual spacing and number of row units on the planting implement, the system  100  can better find the best fit between the crop rows  2  identified in the imagery. 
     In some implementations, the system  100  and image processing sub-system (box  120 ) execute the optional step of resolution optimization (box  124 ), as shown in  FIG. 2B . In certain implementations, the captured aerial imagery (box  110 ) may have insufficient resolution or otherwise lack sufficient clarity to identify crop rows  2 .  FIG. 5  shows an exemplary image with low resolution and/or low clarity. In implementations where the imagery has inadequate resolution or low clarity, the spacing detected between each row by the system  100  may vary by a few inches or greater, shown in  FIG. 5  at X and Y, although the planter row units are at a fixed distance from each other such that there is substantially no actual variation in row spacing. 
     Turning back to  FIG. 2B , in various implementations, the image processing system (box  120 ) can optimize the imagery via resolution optimization (box  124 ). Resolution optimization (box  124 ) may include several optional steps and sub-steps that can be performed in any order or not at all. To optimize the imagery the system  100  may use known data inputs such as the planter row width and number of row units on the planting implement. Use of these known data inputs may allow the system  100  to increase row identification accuracy. Of course, the imagery may be optimized (box  124 ) via any optimization routine or practice known to those skilled in the art. 
     Continuing with  FIG. 2B , in further implementations, the image processing system (box  120 ) may perform an optional step of image recognition (box  126 ) and/or a step of pattern recognition (box  128 ). As would be appreciated, any wavelength of light that can distinguish between the plants and the ground can be used during image recognition (box  126 ) to differentiate between those pixels belonging to a plant and those of the ground, respectively. 
     In certain implementations, additional data such as data from lidar, radar, ultrasound and/or 2D and 3D records can be used instead of or in addition to the imagery (box  110 ) to recognize and identify the actual locations of crop rows  2 . Of course, any other image recognition technique could be used as would be recognized by those of skill in the art, such as those understood and appreciated in the field. 
     In some implementations, the system  100  uses an optional pattern recognition (box  128 ) sub-step during image processing (box  120 ), as shown in  FIG. 2B . In various of these implementations, the imagery is used to identify each crop row  2 . Various image recognition (box  126 ) and pattern recognition (box  128 ) techniques can be implemented including, but not limited to, image segmentation, object bounding, image filtering, image classification, and object tracking. In further implementations, the system  100  may implement machine learning such as via the use of a convolutional neural network, a deterministic model, and/or other methods familiar to those skilled in the art. 
       FIG. 6  shows an example where crops  2  are planted on a slope at a fixed width. In such a situation, the crop rows  2  are planted at a fixed width, such as 30 inches, but when images of these rows  2  are captured by an imager  30 , the width between the crop rows  2  will appear to be smaller due to the slope. In the example of  FIG. 6 , the crop rows  2  will appear closer together, 26 inches apart, from overhead rather than the actual distance of 30 inches. In various implementations, the system  100  may use the information regarding crop row  2  spacing to estimate the degree of terrain slope. For example, the imager  30  may collect images of the rows  30  and transmit those images to the cloud  40  or other server  40  where a CPU  44  or other processer processes the images to determine the slope of the ground at a particular location by enforcing the known spacing between rows  2 . In further implementations, the crop row  2  spacing and the degree of terrain slope can be combined with other data, such as preexisting survey information, to further enhance accuracy of the geo-referenced imagery (box  110  of  FIG. 2B ). 
     Guidance Generation 
     In another optional step, the identified crop rows  2  acquired via image acquisition (box  110 ) and processing (box  120 ) may be used to plan or generate guidance paths  10  (box  140 ) for navigation within and around a field, shown in  FIG. 2C . As noted above, guidance paths  10  (shown for example in  FIG. 1 ) may be collection of navigational coordinates, such as global positioning system (GPS) coordinates, suitable for use by a vehicle steering guidance system. Vehicle steering guidance systems may rely on inertial navigation equipment, satellite navigation, terrestrial navigation, and/or other navigation equipment, as would be appreciated, and as discussed in various of the references cited herein. 
     In various implementations, like that shown in  FIG. 2C , the system  100  uses a variety of data points in addition to the processed imagery (box  130 ) to generate guidance paths (box  140 ). In certain implementations, the system  100  uses terrain data (box  142 ) such as data regarding slope (box  144 ) and/or soil data (box  146 ). In further implementations, the system  100  uses obstacle data (box  152 ) such that the vehicle  20  may navigate around obstacles as necessary. 
     Continuing with  FIG. 2C , in certain implementations, static obstacles (box  152 ) are recorded by the system  100 . These static obstacles (box  152 ), such as structures, fences, and/or roads, do not change or are unlikely to change year over year. In these implementations, the location of static obstacles (box  152 ) may be stored by the system  100  to be used in numerous seasons. In certain implementations, light detection and ranging systems (LIDAR) and/or collision avoidance systems are used to detect such static obstacles (box  152 ). In further implementations, artificial intelligence and/or machine learning techniques may be utilized to detect and record such static obstacles (box  152 ). In still further implementations, a user my identify and classify an obstacle as a static obstacle (box  152 ). In various implementations, the system  100  may recognize changes in the location of a static obstacles (box  152 ) and/or that a static obstacle (box  152 ) is missing from the imagery and alert a user. As would be appreciated, various static objects and the positional information thereof may be used as geo-referenced ground control points (shown for example in  FIG. 4  at A-F). 
     In some implementations shown in  FIG. 2C , transient obstacles (box  154 ) are detected in imagery (box  130 ) and recorded by the system  100 . Certain transient obstacles (box  154 ) such as humans, animals, or vehicles located in the imagery (box  130 ) may be ignored by the system  100  when generating guidance (box  140 ) as such transient obstacles (box  154 ) are unlikely to remain in the same location for a significant period of time. Various alternative transient obstacles (box  154 ) may be recorded by the system  100  and used when generating guidance paths (box  140 ). For example, a flooded zone, a pond, and/or rocks may be located within a field but are more likely to change boundaries or locations over time such that their positional information may remain static for one season but are unlikely to remain in exactly the same position year over year. As noted above, in certain implementations, these transient obstacles (box  154 ) may be identified by artificial intelligence (AI) or machine learning techniques. Alternatively a user may flag or input various transient obstacles (box  154 ) via a GUI  26 ,  46 , as shown in  FIG. 3  and as will be discussed further below in relation to  FIG. 9 . 
     Continuing with the implementation of  FIG. 2C , after the crop rows  2  are identified, with or without geo-referenced points, in certain implementations guidance paths  10  may be generated (box  140 ). As would be appreciated, guidance paths  10  are typically, but not always, placed halfway between adjacent crop rows  2 . In certain implementations, as would be appreciated, guidance paths  10  are typically generated such that a display  24  or other steering system on the vehicle  20  may work with the on-board GPS  22  located on the tractor  20  or other vehicle to follow the guidance paths  10 . In various implementations, the on-board GPS  22  may be centrally located on the vehicle  20  such that a guidance path  10  central to two crop rows  2  is appropriate. In alternative implementations, the on-board GPS  22  may be offset from the center of the vehicle  20  such that the guidance path  10  may vary similarly from the center point between two crop rows  2 . The location of the on-board GPS  22  may vary for different vehicles  20  but would be a known value to be accounted for by the display  24  when generating guidance paths  10 . 
     Further, as shown in  FIG. 2C , various implement data (box  160 ) may be used, such as the number of rows covered (box  162 ), the location of the on-board GPS (box  164 ), and/or the implement function (box  166 ). It is appreciated that various vehicles, machinery, and implements may cover a different number of crop rows  2  with each pass. For example, a planter may cover eighteen (18) rows while a harvester may only cover six (6) rows. Due to the variability in characteristics between agricultural equipment, different types of equipment may require different guidance paths  2 . 
     In some implementations, the system  100  may generate guidance (box  140 ) for one or more different vehicles or implements, as shown in  FIGS. 7A and 7B . In various implementations, the system  100  may optimize guidance paths  10  to provide the efficient operations including considering refilling chemicals, refueling, unloading grain, and other peripheral operations as would be appreciated. 
     As shown in  FIG. 8 , in some implementations, the system  100  may use field boundaries and/or obstacle  4  locations when generating guidance (box  140 ). In these implementations, the guidance paths  10  may be generated (box  140 ) such as to be between each row as well as avoiding collisions with obstacles  4  and/or negotiating around obstacles  4 . 
     Turning back to  FIG. 2C , in further implementations, the system  100  may detect and/or locate terrain features and data (box  142 ), such as ditches and waterways, that require the vehicle to slow down to prevent vehicle damage and/or user discomfort. The system  100  may identify terrain features via an elevation map, lack of crops shown in the imagery, existing drainage maps, and/or any combination thereof. In various implementations, the generated guidance (box  140 ) may include instructions regarding vehicle speed, gears, and/or other parameters that may be automatically adjusted to appropriate levels as indicated. Further, in some implementations, the generated guidance (box  140 ) may include instructions to either apply or turn off the application of herbicides, fertilizer, and/or other chemical and treatments as indicated by the imagery and/or other collected data. 
     Geo-Referencing and Adjustments 
     Returning to  FIG. 2A , in some implementations, adjustments (box  150 ) may be necessary to maintain a high degree of fidelity between the generated guidance (box  140 ) and the actual vehicle location. In some implementations, the guidance path  10  pattern may be shifted with respect to the current vehicle navigational frame of reference. Adjustments (box  150 ) may be automatic and/or manual. In some implementations, adjustments (box  150 ) may eliminate lateral and/or longitudinal bias, such as that created by GPS drift or other phenomena as would be appreciated. 
     In some implementations, the guidance (box  140 ) may be adjusted using one or more reference locations (box  148 ), such as geo-referenced ground control points A-F discussed above in relation to  FIG. 4 . In these implementations, the vehicle may be driven to a specific reference location and the bias between the actual vehicle location and the recorded location compared, measured, and corrected. 
     In an alternative implementation, the guidance paths  10  (box  140 ) may be adjusted by driving the vehicle in the field, gathering data, and using the data to eliminate positional bias. In various implementations, the data gathered may include the navigational track of the vehicle, vehicle speed, and/or data from vehicle mounted sensors such as to detect the presence and/or absence of the planted crops  2 . In various implementations, then when the system  100  collects sufficient data to determine the location of the vehicle with a high confidence with respect to the map then automatic guidance and navigation may be engaged. 
     Turning to  FIG. 9 , in these and other implementations, the display  24  may show an orthomosaic image  50  of the field derived from the imagery, guidance paths  10  within the field  50 , a classification function  54  and/or other information or functions as would be appreciated. In certain implementations, the display  24  may be a monitor or other viewing device as would be appreciated by those of skill in the art. In various implementations, the display  24  may be a touch screen display  24  or other interactive display  24 . 
     In various implementations, an operator may shift the map and/or guidance paths  2  until the guidance paths  10  are properly aligned with crops  2 /imagery  50 , as shown and discussed in relation to  FIG. 2A  at box  150 . As shown in  FIG. 9 , a display  24  may be configured with a graphical user interface  26  including one or more buttons  52  to adjust the alignment of the field imagery  50  and the guidance paths  10 . For example, a user may manually adjust the guidance paths  10  in relation to the navigational system of the tractor  20  or other agricultural implement by pressing the left, right, or other appropriate buttons  52 , as would be understood. In various implementations, this manual adjustment may eliminate lateral bias. Of course alternative implementations and configurations are possible. 
     It is understood that various implementations make use of an optional software platform or operating system  28  that receives raw or processed acquired images, or one or more guidance paths  10  for use on the display  24 . That is, in various implementations, the various processors and components in the user vehicle  20  may receive image and/or guidance data at various stages of processing from, for example, a centralized storage (such as the cloud  40  of  FIG. 3 ), for further processing or implementation in the vehicle  20 , such as via a software platform or operating system  28 . 
     Turning back to  FIG. 9 , in some implementations, longitudinal bias of the guidance paths  10  may be adjusted via monitoring when grain is harvested, such as via a yield monitor or stalk counter, as would be understood. In certain implementations, yield monitoring and/or stalk counting are integrated functions in the display  24 . In an alternative implementation, longitudinal bias of the guidance paths  10  may be adjusted via monitoring when herbicide or fertilizer is being applied thereby determining where the crop  2  starts and/or ends. 
     In further implementations, the display  24  may include a classification function  54  for use with the obstacle data (box  150  in  FIG. 2C ). Continuing with  FIG. 9 , in various implementations the classification function  54  may present a user with a thumbnail  56 , reproduction  56 , or other indicator of a potential obstacle  58  identified in the field imagery  50 . In certain implementations, a user may then indicate if the obstacle  58  shown in the thumbnail  56  is a transient or static obstacle by pressing the corresponding buttons  60 . In certain other implementations, the system  100  may pre-classify and object based on prior classification, object recognition, artificial intelligence, and/or machine learning and the user may modify or confirm the classification via the classification function  54 . 
     Although the disclosure has been described with references to various embodiments, persons skilled in the art will recognized that changes may be made in form and detail without departing from the spirit and scope of this disclosure.