Patent Publication Number: US-11651478-B2

Title: Methods for agronomic and agricultural monitoring using unmanned aerial systems

Description:
BENEFIT CLAIM 
     This application claims the benefit under 35 U.S.C. § 120 as a continuation of application Ser. No. 16/438,128, filed Jun. 11, 2019, which is a continuation of application Ser. No. 15/853,356, filed Dec. 22, 2017, issued U.S. Pat. No. 10,346,958 on Jul. 9, 2019, which is a continuation of application Ser. No. 14/831,165, filed Aug. 20, 2015, issued U.S. Pat. No. 9,922,405 on Mar. 20, 2018, which claims the benefit under 35 U.S.C. 119(e) of provisional application 62/040,859, filed Aug. 22, 2014, and provisional application 62/046,438, filed Sep. 5, 2014, the entire contents of which are hereby incorporated by reference as if fully set forth herein. 
    
    
     COPYRIGHT NOTICE 
     A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright or rights whatsoever. © 2015-2021 The Climate Corporation. 
     FIELD 
     This disclosure generally relates to agronomic and agricultural monitoring, and more specifically, to methods for agronomic and agricultural monitoring using unmanned aerial systems or drones. 
     BACKGROUND 
     Unmanned aerial vehicles (UAVs), sometimes referred to as drones, are remotely piloted or self-piloted aircraft that may carry sensors, communications equipment, cameras or other payloads. UAVs have been used for military reconnaissance and intelligence-gathering, as well as for capturing terrestrial images for civilian applications. While UAVs have also been used for agricultural monitoring, such systems are not entirely satisfactory. An improved UAV for agricultural use is needed. 
     This Background section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art. 
     BRIEF SUMMARY 
     One aspect is a method for agronomic and agricultural monitoring. The method includes designating an area for imaging, determining a flight path above the designated area, operating an unmanned aerial vehicle (UAV) along the flight path, acquiring images of the area using a camera system attached to the UAV, and processing the acquired images. 
     Another aspect is a system for agronomic and agricultural monitoring. The system includes a computing device configured to designate an area for imaging, and determine a flight path above the designated area. The system further includes an unmanned aerial vehicle communicatively coupled to the computing device and having a camera system, the unmanned aerial vehicle configured to travel along the flight path, acquire images of the area using the camera system, and process the acquired images. 
     Various refinements exist of the features noted in relation to the above-mentioned aspects. Further features may also be incorporated in the above-mentioned aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to any of the illustrated embodiments may be incorporated into any of the above-described aspects, alone or in any combination. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of a system for use in agronomic and agricultural monitoring. 
         FIG.  2    is a flowchart of a method for operating an unmanned aerial vehicle for agronomic and agricultural monitoring that may be used with the system shown in  FIG.  1   . 
         FIG.  3    is a flowchart of a mission planning stage of the method shown in  FIG.  2   . 
         FIG.  4    is a flowchart of a flight execution stage of the method shown in  FIG.  2   . 
         FIG.  5    is a flowchart of a post flight data transfer/processing stage of the method shown in  FIG.  2   . 
         FIG.  6   ,  FIG.  7   ,  FIG.  8   ,  FIG.  9   ,  FIG.  10   ,  FIG.  11    are examples of maps that may be created by the system shown in  FIG.  1   . 
     
    
    
     Corresponding reference characters indicate corresponding parts throughout the several views of the drawings. 
     DETAILED DESCRIPTION 
     Referring initially to  FIG.  1   , an example of an unmanned aerial system of the present disclosure is indicated generally at  100 . Unmanned aerial system  100  includes a plurality of components including an unmanned aerial vehicle (UAV)  110 , a cloud  120 , a graphical user interface (GUI)  130  (e.g., implemented using a tablet computing device), a base station  140 , a personal computer  150 , and a user input device (UID)  160 . The components of system  100  will be described in more detail below. 
     In this embodiment, the components of system  100  are communicatively coupled with one another via one more communications media (e.g., direct cable connection, cloud computing networks, the Internet, local area networks (LAN), wireless local area networks (WLAN) (e.g., 802.11ac standard), or wide area networks (WAN)). Accordingly, components of system  100  may include a wireless transmitter and receiver (e.g.,  118 ,  135 ,  143 ,  151 ,  165 ) and/or a cellular transfer module (e.g.,  113 ,  131 ,  142 ,  162 ) to facilitate wireless communication between components. Additionally, one or more of the components ( 110 ,  120 ,  120 ,  140 ,  150 , and  160 ) may include a global positioning system (GPS) therein (e.g.,  111 ,  133 ,  145 ,  153 , and  161 ) for determining a position of the associated component, normalizing GPS data between components, and enabling triangulation calculations for position determinations. 
     In this embodiment, unmanned aerial vehicle  110  is a remote piloted or self-piloted aircraft which may be hover-capable (e.g., a helicopter or rotorcraft) or may be fixed wing. An example of a hover-type “quadricopter” UAV is described in U.S. Patent Application Publication No. 2013/0176423, which is hereby incorporated by reference in its entirety. In the systems and methods described herein, UAV  110  assists agricultural and farming operations by mapping and monitoring agricultural status and evolution. 
     In this embodiment, unmanned aerial vehicle (UAV)  110  includes a suitable global positioning system (GPS)  111  that provides the location of UAV  110  using, e.g., GPS satellites orbiting Earth. Location and time data may be provided to a user (e.g., human operator) or to a computer that automatically controls the vehicle. An elevation sensor (e.g., sonar) may be part of GPS system  111  for determining elevation of UAV  110  during flight. UAV  110  also includes one or more mounted inertial measurement units (IMUs)  112  that measure and report the velocity, orientation, and gravitational forces of UAV  110  using a combination of mounted accelerometers, gyroscopes, and/or magnetometers. In cooperation with GPS  111  and IMUs  112 , an autopilot capability  115  on UAV  110  controls take-off, in-flight navigation, and landing operations. For communication during return flight operations, UAV  110  has a drone-base communication system  116  that includes a radio transmitter and receiver (e.g., 900 MHz or 1.2 GHz) to communicate with a point of origin, such as base station  140 , while in flight. 
     In the example embodiment, UAV  110  also includes a camera system  117  mounted to its underside for acquiring images during flight. Camera system  117  may hang from UAV  110  by gravity using a set of gimbals that allow rotation about a plurality of axes. The gimbals may include dampers that slow down reactions to changes in orientation of UAV  110  during flight. Alternatively, camera system  117  may be mounted directly to UAV  110  and be controlled by the movement of actuators. Camera system  117  may include a still photo camera, a video camera, a thermal imaging camera, and/or a near infrared (NIR) camera for capturing normalized difference vegetation index (NDVI) images. Alternatively, camera system  117  may include any image acquisition device that enables system  100  to function as described herein. 
     Camera system  117  and positioning of camera system  117  is controlled by an on-board central processing unit (CPU) and memory storage unit  114 . The central processing unit (CPU) may include a microprocessor. CPU and memory storage unit  114  facilitates arithmetical, logical, and input/output operations of the on-board CPU. CPU and memory storage unit  114  may also assist and/or control other aspects of UAV  110 , as discussed herein. For example, in some embodiments, CPU and memory storage unit  114  receives information from IMUs  112  during in-flight operations to assist with orientation of the camera system  117  and/or to detect whether or not conditions (e.g., light, speed, angle, etc.) are adequate to capture useful, visible images. UAV  110  may also include one or more sensors (e.g., an incident light sensor) coupled to CPU and memory storage unit  114  for monitoring ambient conditions. 
     In the example embodiment, base station  140  includes a drone-base communication system  141  comprising a radio transmitter and receiver (e.g., 900 MHz or 1.2 GHz) to facilitate communicating with UAV  110  while in flight. Base station  140  also includes a GPS system  145  and a CPU and memory storage unit  144  similar to those discussed above in relation to UAV  110 . 
     In this embodiment, personal computer (PC)  150  is a computing device such as a laptop or desktop. PC  150  includes a CPU and memory storage unit  153 , and also includes spatial agricultural data processing and mapping software (e.g., Farm Works Software® or SST Summit Professional®) installed thereon. In one embodiment, PC  150  may serve as a user interface for system  100 . 
     System  100  also includes a graphical user interface (GUI)  130  that serves as a portable user interface. GUI  130  may be implemented using a tablet or other portable computing device that allows the user, or operator, to control system  100 . In particular, GUI  130  may allow the user to designate flight paths of UAV  110  and/or identify aerial obstacles which may otherwise obstruct the flight path of UAV  110 . In this embodiment, GUI  130  includes an application (“app”) or viewing software  136  which allows the user to remotely access spatial maps including data regarding harvest, yield, and/or nitrogen content created from images taken by UAV  110 . For example, GUI  130  may include software similar to that described in International Patent Application Publication No. WO 2014/026183, which is hereby incorporated by reference in its entirety. Accordingly, GUI  130  includes a CPU and memory storage unit  132 , and is in communication with other components of system  100 . 
     System  100  also includes the user interface device (UID)  160  (e.g., a joystick or keypad) that allows the user, or operator, to control system  100 . In particular, UID  160  may allow the user to designate flight paths of UAV  110  and/or identify aerial obstacles which may otherwise obstruct the flight path of UAV  110 . In this embodiment, UID  160  includes a display  164  which allows the user to remotely view images from camera system  117 . Accordingly, IUD  160  includes a CPU and memory storage unit  163 , and is in communication with other components of system  100 . In one embodiment, the UID  160  may allow the user or operator to control the UAV  110  while viewing images from camera system  117  on touch screen display  134  on GUI  130 . 
     In this embodiment, cloud  120  is a data storage, image processing, and computing hub for the unmanned aerial system  100 . More specifically, cloud  120  is a set of interconnected computers and servers connected through a communication network to allow distributed computing. For example, cloud  120  could be a remote data storage center. Cell module  113  mounted to UAV  110  allows photographs to be uploaded to cloud  120  while UAV  110  is in flight. Cloud  120  may receive and store current and forecasted weather information including air temperature and precipitation amounts. Cloud  120  may also communicate with one or more analysis and recommendation services that provide analysis and/or recommendations based on image data acquired using UAV  110 . 
     In one embodiment, UAV  110  transmits images taken with camera system  117  during flight to other components (e.g.,  130 ,  140 ,  150 ,  160 ) for storage and/or processing. Images and metadata uploaded from the UAV  110  to cloud  120  may be orthorectified and stitched together to create a single contiguous image. Examples of orthorectifying oblique imagery to a singular view are described, for example, in U.S. Pat. No. 8,512,266, which is hereby incorporated by reference in its entirety. 
     Referring to  FIG.  2   , an example of a method of operating an unmanned aerial system, such as system  100 , is indicated generally at  200 . In this embodiment, method  200  includes three stages: a mission planning stage  201 , a flight execution stage  202 , and a post flight data transfer/processing stage  203 . The three stages of method  200  will be described in more detail below. 
     Referring to  FIG.  3   , an example of the mission planning stage of method  200  is indicated generally at  300 . Mission planning stage  300  of method  200  includes a sequence of actions performed by the user and the system  100 . In  FIG.  3   , actions performed by the user are provided in a circle and actions performed by system  100  are provided in a square. 
     In the example method  200 , following activation of the system  100 , the user first indicates the flight area  301  for mapping. In one embodiment, the user outlines the flight area to be covered by UAV  110  on GUI  130  or UID  160  using map data from Google Maps® or other GPS software. 
     In one embodiment, system  100  analyzes the user&#39;s flight area  301  input, calculates possible flight path(s) to generate a contiguous image of the flight area, and provides the user with possible UAV flight paths  302 . System  100  may also identify potential obstacles (e.g., telephone poles and/or electrical lines) in the flight path based on previous flights and/or user input, and may adjust the flight path accordingly. In another embodiment, system  100  provides the user with multiple possible UAV flight paths at different elevations and velocities depending upon the desired image resolution and flight duration. For example, and for purposes of illustration, system  100  could provide the user with two optional UVA flight paths on GUI  130  or UID  160  as provided in Table 1 below: 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                   
                 Path 
                 Elevation 
                   
                 Duration 
               
               
                   
                 # 
                 (ft) 
                 Resolution 
                 (min) 
               
               
                   
               
             
            
               
                   
                 1 
                  50 
                 High 
                 30 
               
               
                   
                 2 
                 100 
                 Low 
                 15 
               
               
                   
               
            
           
         
       
     
     In the example method  200 , once provided with possible flight paths, the user selects a desired flight path  303 . In one embodiment, the user may request system  100  provide additional flight path options by entering specific parameters for the flight path (i.e., elevation, picture resolution, duration, etc.). 
     In the example method  200 , upon the user selecting a desired flight path, system  100  provides the user with a selection of possible image types to be taken by camera system  117 . In one embodiment, user has the option of selecting  305  from still photos, thermal images, near infrared (NIR) images, and videos that visible light, thermal, and/or NIR imaging. For example, GUI  130  or UID  160  may provide the user with a list that allows the user to select the desired image type  304  (e.g., by displaying a checkbox or other selection mechanism). Based on the image types selected by the user, in some embodiments, GUI  130  or UID  160  determines an optimized elevation and/or estimates a flight time. 
     In the example method  200 , system  100  provides the user with flight details and facts  306  on GUI  130  or UID  160 . In one embodiment, the system  100  may provide the user with the route, elevation and/or duration of the UAV flight, as well as the anticipated resolution of images to be taken in the selected image type. In another embodiment, prior to generating flight paths, system  100  determines whether flight obstacles (e.g., telephone poles or electrical lines) have been previously identified in the applicable flight area. In yet another embodiment, the user identifies flight obstacles  307  on GUI  130  or UID  160  using satellite imagery from Google Earth® or another imagery provider. Specifically, in one embodiment, GUI  130  or UID  160  enables the user to draw a border around any flight obstacles and to enter the approximate height of the obstacle to prevent the UAV from entering the obstructed area. Using the input from the user, system  100  recalculates the flight path to avoid the obstacles. 
     Referring to  FIG.  4   , an example of the flight execution stage of method  200  is indicated generally at  400 . Flight execution stage  400  of method  200  includes a sequence of actions performed by the user and system  100 . In  FIG.  4   , actions performed by the user are provided in a circle and actions performed by system  100  are provided in a square. 
     In the example method  200 , flight execution stage  400  occurs after mission planning stage  201 . In one embodiment, the user directs system  100  to start the flight execution stage  400  using GUI  130  or UID  160 . In another embodiment, flight execution stage  400  automatically commences following the identification of obstacles, if any, in the UAV flight path. 
     In the example method  200 , flight execution stage  400  begins with system  100  comparing the flight duration and elevation to a battery life  401  of UAV  110 . When a level of battery charge is insufficient, system  100  provides an indication to the user (e.g., on GUI  130  or UID  160 ) that charging is required. In addition to a power check, system  100  also performs an operational test of the system components, particularly UAV  110 . In one embodiment, the system  100  conducts an operation test  402  to confirm the necessary cameras on camera system  117  are installed and operational, that weather conditions are safe for UAV flight, that the area surrounding UAV  110  is clear and safe for take-off, and that GPS coordinates of UAV  110  are correct. 
     In the example method  200 , following confirmation by system  100  that UAV  110  capable and ready for operation, the user is prompted by system  100 , via GUI  130  or UID  160 , to start flight  403 . In one embodiment, the user pushes a “start flight” or “go” button on GUI  130  or UID  160 . Upon initiation of flight, system  100  commences the UAV flight and continually monitors UAV systems  404 . In one embodiment, UAV  110  performs one or more test maneuvers. For example, UAV  110  may take off vertically from base station  140  and perform simple maneuvers (e.g., moving back and forth, side to side, up and down, etc.) to check operation and maneuvering capabilities. In the event of a UAV or system malfunction at any time during flight, system  100  and user have the ability to end the flight prematurely  405 . In such an instance, the selected flight path is terminated, and UAV  110  returns to base station  140  and/or attempts to return to the ground without damaging UAV  110 . 
     In the example method  200 , during flight of UAV  110 , camera system  117  takes pictures or video of the selected flight area and stores the images on on-board CPU and memory storage unit  114 . In one embodiment, on-board CPU and memory storage unit  114  orthorectifies the imagery to a singular view and identifies areas with low quality imagery. 
     In some embodiments, UAV  110  acquires an initial set of images, and then returns to one or more target areas to acquire additional images at a higher resolution after reviewing the initial image maps  406 . For example, camera system  117  may acquire NDVI images of the selected flight area, identify areas with low nitrogen levels (or other problems) via the NDVI map, and display these areas to the user via GUI  130  to enable the user to instruct UAV  110  to acquire additional, low-elevation (e.g., between 10 and 50 feet above the ground), high resolution (“scouting”) pictures. In one embodiment, images of a planted population (e.g., corn, soybean, etc.) are captured by camera system  117  from an aerial view before the planted population reaches a mature length (i.e., at a time when individual plants are indistinguishable from neighboring plants). 
     In another embodiment, UAV  110  automatically flies a “Go Back and Scout” route  407  following a first pass over the selected flight area to take additional high resolution pictures of target areas (e.g., areas with low nitrogen levels) shown in the NDVI imagery. In yet another embodiment, additional high resolution pictures of target areas are taken to eliminate crop shadows. In such embodiments, to reduce processing time, image processing and analysis may be performed on-board UAV  110 . 
     In the example method  200 , following the UAV&#39;s completion of the flight path, the UAV lands (e.g., at base station  140 ) to end the flight  408 . 
     In the example method  200 , upon completion of the flight execution stage  400 , post flight data transfer/processing stage  203  commences. Alternatively, data transfer/processing may occur while UAV  110  is still airborne such that data transfer/processing stage  203  overlaps flight execution stage  400 . Hence, transfer and processing of the imagery obtained by UAV  110  may occur in real-time as the data is captured, or shortly thereafter (e.g., within 10 minutes of data capture). In one embodiment, low-quality images are constantly transmitted to GUI  130  or UID  160  during flight to keep the user apprised of the status of the flight. 
     Transferring the data and images captured by UAV  110  may be done via wireless and/or cellular communication between the components of the system  100 . The transfer is typically directed to the component where processing will be executed. 
     Processing the data and images may include orthorectification and stitching of the aerial images into a single contiguous area map. Notably, processing of the data and images may be performed using any component of system  100 . For example, processing may be performed on-board UAV  110 , and the processed images may then be transferred to base station  140 , GUI  130 , and/or UID  160 . 
     In one embodiment, the acquired images are superimposed (e.g., with a 50% transparency) over Google Map® tiles or aerial geographic images and displayed to the user. Alternatively, aerial images may be processed and displayed with Google Map® tiles or aerial geographic image such that they are displayed in a locked side-by-side orientation, such that moving and/or zooming one image moves and/or zooms the other image by the same amount. The center points of the images in the side-by-side orientation may be indicated with an icon (e.g., cross-hairs), similar to the techniques described in International Patent Application Publication No. WO 2014/026183. In another embodiment, a sequence of aerial images taken at different times during a growing season (e.g., daily, weekly, monthly, etc.) are processed into an animation that steps through the images in sequence. In some embodiments, the animation is played automatically by displaying the images for set time periods; in other embodiments, the next sequential image is displayed in response to a user input on a graphical interface (e.g., selection using an arrow icon or dragging a slider icon across a scale). The animation may be superimposed over Google Map® tiles or aerial geographic images. The images may be, for example, NDVI images, aerial maps, and/or emergence maps. 
     Processing images may also include filtering the images using software to filter out dirt and shadows that may affect image quality. The filtering creates a color contrast between the plant canopy and dirt, which may be difficult to distinguish from one another in the unfiltered image. For example, in one embodiment, image processing removes anything in the aerial photograph below a threshold reflectance or color value. 
     In one example, an expected greenness density is identified based on a planted population and/or a development stage of plants in the imaged area. The planted population may be determined from an as-planted map, and the development stage may be determined, for example, using a hybrid-specific chart that relates the number of growing degree days to an expected development stage. Once the expected greenness density is identified, everything in an image that is above the expected greenness density may be depicted in shades of green, and everything in the image that is below the expected greenness density may be depicted in shades of red. 
     System  100  may also use imaging data to generate emergence maps in which a number of plants per area is calculated, and areas devoid of plants or desired greenery in the images are marked as “blanks.” Blanks are areas where plants or greenery either failed to grow or were not initially planted. In one embodiment, system  100  correlates the blank data with initial planting data (e.g., the as-planted map) to remove any blanks that occurred due to no initial planting, leaving only true blanks that are indicative of areas where seeds were planted, but did not emerge. This processing can be applied to NDVI image data or other image data acquired by camera system  117 . 
     In some embodiments, spatial application decisions may be made automatically based on images acquired by UAV  110 . For example, levels of an NDVI map may be associated with a post-planting application (e.g., side-dressing or crop dusting) to generate an application map based on the NDVI map. The generated application map may be displayed to the user to allow the user to reject, modify, or accept the application map. In some embodiments, the generated application map is transmitted to a service provider (e.g., an employee or third-party contractor) with instructions to apply the application map. 
     The data acquired by UAV  110  may also be used to make general agronomic recommendations. For example, if an NDVI map generated using system  100  has a nitrogen level below a threshold, system  100  may recommend that nitrogen be applied by a sidedress to increase nitrogen levels. The threshold may be determined based on a development stage of the crop, for example. In another example, if a plant health map indicates an area of healthy plants is below a threshold, system  100  may recommend that nitrogen be applied by a sidedress. In yet another example, if an emergence map has an emergence area below a threshold prior to a critical time in development, system  100  may recommend the field be replanted. 
     Referring to  FIG.  5   , an example of data transfer/processing stage  203  of method  200  is indicated generally at  500 . Data transfer/processing stage  500  of method  200  includes a sequence of actions performed by the user and system  100 . In  FIG.  5   , actions performed by the user are shown in a circle and actions performed by system  100  are shown in a square. 
     Data transfer/processing stage  500  generally includes the following eight stages: obtaining NDVI image(s) from flight execution stage  501 ; converting NDVI image(s) into a map stage  502 ; filtering out non-crop matter stage  503 ; identifying crop rows stage  504 ; partitioning individual plants stage  505 ; identifying individual plant features stage  506 ; estimating crop yield potential stage  507 ; and generating report stage  508 . 
     In the example method  200 , post flight data transfer/processing stage  500  begins with system  100  obtaining NDVI image(s) from flight execution  501 . Again, data transfer/processing may occur while UAV  110  is still airborne such that data transfer/processing stage  500  overlaps flight execution stage  400 . Data transfer/processing stage  500  may occur in real-time as the data is captured by UAV  110 , or shortly thereafter (e.g., within 10 minutes of data capture). 
     In this example, images obtained from flight execution are converted by system  100  into a map  502  (e.g., a bitmap, an emergence map, etc.). In one embodiment, an expected greenness density is established based on a planted population and/or a development stage of plants in the imaged area. Once the expected greenness density is identified, in the generated map, pixels in each image that are above the expected greenness density are depicted in white, and pixels in the image that are below the expected greenness density are depicted in black. Accordingly, a map is created with unitary white spaces  601  correlating approximately to the location and area of each plant in the images. An example map  600  is provided in  FIG.  6   . In  FIG.  6   , individual plants in the planted population (identified using the expected greenness density) are depicted as white spaces  601 . Surrounding features  602  (e.g., surrounding soil, weeds, etc.) are lightly shaded. Until further processing, the map may include single white spaces  601  that include multiple plants (e.g., shown on the right side of map  600 ) and/or white spaces  601  that are weeds or other non-crop plant matter (e.g., shown on the lower left side of map  600 ). 
     Filtering out non-crop matter stage  503  in the example method includes identifying “anomalies” in the generated map. “Anomalies”, as used herein, refer to areas in the generated map that are initially identified by system  100  as a white space  601  (e.g., based on greenness density), but do not actually represent a desired plant from the planted population. For example, a weed may be an anomaly in the generated map. Stage  503  also includes filtering these anomalies from map  600 . In this example, system  100  identifies anomalies by calculating a size (e.g., area, diameter, etc.) for each white space  601 , and then anomalies are identified as white spaces with a size substantially different than (e.g., 2 standard deviations from) the typical (e.g., mean, median, average, etc.) size of white spaces  601  in map  600 . An example anomaly is shown generally in  FIG.  7    as anomaly white space  701 . System  100  filters out anomalies by shading anomalies the same color as surrounding features  602  or removing anomalies from further consideration in method  500 .  FIG.  8    shows an example map  600  with anomaly white space  701  removed. In another embodiment, system  100  compares the anomalies with initial planting data (e.g., an as-planted map) to remove any anomalies that occur in areas where there was no initial planting. Accordingly, system  100  filters anomalies by shading them appropriately or removing them from further consideration. This processing can be applied to NDVI image data or other image data acquired by camera system  117 . 
     Identifying crop rows stage  504  in the example method includes marking a centroid  801  for each remaining white space  601 .  FIG.  8    shows an example map  600  with a row  802  of white spaces  601  marked with centroids  801 .  FIG.  9    shows another example map  600  with two rows  901  and  902  of white spaces  601  marked with centroids  801 . System  100  identifies rows (e.g.,  802 ,  901 ,  902 ) by calculating, approximating, and assigning best fit lines to the rows based on positions of centroids  801 . Specifically, system  100  uses a row spacing distance  805 , which may be either a standard value (e.g. 30 inches) or a user entered value, to identify approximate locations of parallel rows through white spaces  601  and/or centroids  801 . In other embodiments, stage  504  may overlap or occur at the same time as stage  503  to assist system  100  with identifying anomalies. 
     Partitioning individual plants stage  505  in the example method includes identifying two or more overlapping white spaces  601  (i.e., two or more overlapping plants).  FIG.  10    shows an example of an overlapping pair of white spaces  601  within circle  1000 . In the example method, to identify an overlapping pair of white spaces  601 , system  100  first compares (i) in-row spacing (e.g.,  1001  and  1003 ) between adjacent centroids; and (ii) in-row spacing value (e.g.,  1002  and  1004 ) determined by (a) a nominal value from the user, (b) an as-planted spacing value from an as-planted map, or (c) the median or average spacing between in-row plants. In the instance of an overlapping pair of white spaces  601 , such as those shown in circle  1000  in  FIG.  10   , a difference  1005  between spacing  1003  and  1004  is markedly larger than a difference  1006  between spacing  1001  and  1002 . As a separate step or as part of the same step in identifying an overlapping pair of white spaces  601 , system  100  may also calculate and compare the median area of white spaces  601 . Accordingly, system  100  is able to identify the overlapping pair of white spaces  601  (e.g., within circle  1000 ) using the above-described analysis. Upon identification of an overlapping pair of white spaces  601 , as shown for example in  FIG.  11   , system  100  partitions individuals plants by re-assigning two centroids  801  to mark the location of individual white spaces  601  equidistant from the location  1100  of deleted centroid  801 . 
     In this example, system  100  also assigns a “confidence value” (e.g., 90%) to each white space  601  indicating the statistical probability or certainty that each white space  601  correlates to the location and/or area of a distinct plant in the images. In one example, the confidence value for an individual white space  601  is higher when (i) the location of its centroid  801  is approximately equal to an in-row spacing value (e.g.,  1002  and  1004 ); and (ii) its area is approximately equal to the median and/or average area of white spaces  601  on map  600 . Accordingly, system  100  may store the confidence value for each white space  601  on each map  600  to reference for various purposes, as described below. 
     Identifying individual plant features stage  506  in the example method includes both correcting images captured by camera system  117  and analyzing individual plants (e.g., those identified within white spaces  601 ). In this example, system  100  corrects aerial images captured by camera system  117  by considering an image data point (e.g., the location, elevation, and speed of UAV  110  when each image was taken, the resolution of camera system  117 , the angle and zoom used by camera system  117 , etc.) and in-row and parallel-row spacing measurements identified in stages  504  and  505  described above. More specifically, system  100  assigns a scale to each pixel in each image by comparing the known in-row or parallel-row spacing measurements (e.g., in inches) to the known in-row or parallel-row image spacing measurements (e.g., in pixels). 
     In another example, correcting images captured by camera system  117  may include a “Go Back and Scout” route  407  by UAV  110  to take additional high resolution pictures of target areas. 
     In this example, system  100  also analyzes individual plants (e.g., those identified within white spaces  601 ) by examining one or more images of each plant captured by camera system  117  from differing positions and elevations. Similar to stage  504  where each white space  601  is marked with a centroid  801 , system  100  locates structures (e.g., leaves, stalks, ears, etc.) of each plant and marks each structure with a centroid. In one example, system  100  locates plant structures using a length:width ratio for structures consistent with the planted population. Further, leaf spines may be located by calculating midpoints between leaf edges. In this example, system  100  also locates an updated, more precise centroid of the plant using centroids from the individual plant structures. In another example, system  100  may use an intersection point of lines fitted along the length or width and through the centroid of a plurality of plant structures (e.g., leaf spines) to find the updated plant centroid. Still in other embodiments, system  100  may return to previous stages to improve white space  601  identification and/or centroid  801  placements, for example. 
     In this example, system  100  uses the images and plant structure location to determine data regarding the characteristics of plants in the planted population. Plant characteristics of particular interest, for example, suitably include without limitation leaf length (e.g., average spine length), width, and area (e.g., of the entire plant) and number of leaves (which may be, for example, the number of spines identified). Again, system  100  may use image data points to adjust for unclear or skewed views of plant characteristics. Accordingly, system  100  may store the information regarding plant characteristics for each plant to reference for various purposes described below. 
     Estimating crop yield potential stage  507  in the example method includes using information gathered and calculated by system  100  to estimate a yield potential. Information gathered includes, for example, the number of plants in the planted population, the confidence value for each white space  601 , and/or information regarding plant characteristics. In this example, system  100  may not consider plant characteristics when a confidence value for a particular plant is below a first threshold (e.g., 95%). Also in this example, system  100  may not include that particular plant for the planted population stand count when the confidence value is below a second, lower threshold (e.g., 80%). 
     In one example, system  100  may use the following Equation 1 to estimate a plant or planted population yield potential:
 
yield potential= Ax+By+Cz   Equation 1:
 
where,
 
x=number of leaves
 
y=leaf area
 
z=maximum leaf length or average of two longest leaves
 
A=0, if x&lt;threshold value; A&gt;0, if x&gt;threshold value
 
B=0, if x&lt;threshold value; B&gt;0, if x&gt;threshold value
 
C=0, if x&lt;threshold value; C&gt;0, if x&gt;threshold value
 
     In one example, system  100  may calculate an ear potential using a Boolean approach. For example, if any two variables (e.g., number of leaves, leaf area, maximum leaf length) are above a predetermined threshold associated with each variable, then the ear potential is set at 1. Otherwise, the ear potential is set at 0. It should be appreciated that the threshold values used to determine yield or ear potential may be selected to require a high confidence (e.g., 99%) that the plant has the classified potential, or to require only a relatively low confidence (e.g., 80%). 
     In another example, the plant characteristics (number of leaves, leaf area, leaf length, etc.) used to calculate yield/ear potential are relative to other plants in the field. The may be, for example, relative to neighboring or nearby plants, or relative to a mean/average number for the image and/or field. For example, system  100  may use the following Equation 2 to estimate a plant or planted population yield potential, based on relative plant characteristics:
 
yield potential= A ( x−l )+ B ( y−m )+ C ( z−n )  Equation 2:
 
where,
 
x=number of leaves on one plant
 
y=leaf area on one plant
 
z=maximum leaf length or average of two longest leaves on one plant
 
l=average number of leaves on plants in the same image or planted population
 
m=average leaf area on plants in the same image or planted population
 
n=average maximum leaf length or average of two longest leaves on plants in the same image or planted population
 
A=0, if x&lt;threshold value; A=1, if x&gt;threshold value
 
B=0, if x&lt;threshold value; B=1, if x&gt;threshold value
 
C=0, if x&lt;threshold value; C=1, if x&gt;threshold value
 
In both Equations 1 and 2, the threshold value for determining A, B, and C may be (i) a nominal value from the user; (ii) an expected value based on previous planted populations; (iii) an extrapolated value from individual plants; or (iv) an interpolated value from larger planted populations.
 
     Generating report stage  508  in the example method includes creating a map or report of data regarding the planted population. In this example, the map generated compares the as-planted map with another map later in the development of the planted population. The map may show, for example, regularity of plant spacing, skipped plantings, double planted plants, etc. Also in this example, the report generated may include a potential or generated yield (e.g., number of ears, seeds, stalks, etc.) from the planted population. 
     In some embodiments one or more measurements and spatial maps may be generated and displayed to the user based on information gathered from aerial imagery. 
     In one embodiment, a weed pressure value is determined for each location or region in the field based upon the relative amount of weeds in the standing crop. The weed pressure value is preferably related to the amount of green plant matter identified between the rows of a row crop. For example, the weed pressure value may be determined for a region A in the field by dividing the area of “anomalies” identified as described above within the region A by the total area of the region A. In some such methods, weeds are distinguished from other anomalies or from crop material based on a shape or size criterion of the weed; for example, anomalies having a total area or width less than a threshold may be ignored for purposes of calculating a weed pressure value. The weed pressure value determined for locations throughout the field may then be displayed as a field or region value or presented as a spatial weed pressure map. 
     In another embodiment, the leaf width of crop plants identified in the field (determined as described above) is reported as a field average or presented to the user as a spatial map of average leaf width in the field. 
     In another embodiment, an estimated emergence date of identified crop plants is determined for each plant or region of the field. The estimated emergence date may be estimated based on the size of each identified crop plant; additionally, where no crop plants are observed in a portion of the field at a given date the emergence date for that portion of the field may be assumed to be after that date. The spatial variation in estimated emergence date may be presented to the user as a map or may be used to improve estimations of plant moisture or plant maturity later in the season, e.g., when determining a recommended harvest date. It should be appreciated that for field-wide operational decisions, the latest emergence date should be used; for example, a delay of one day in the latest emergence date determined for the field may result in a one day delay in the recommended harvest date. 
     In another embodiment, an estimated plant vigor of crop plants identified in the field is reported as a field average or presented to the user as a spatial map of plant vigor in the field. The plant vigor value for each plant or group of plants is preferably determined by calculating a weighted sum or product of plant characteristics (e.g., leaf width and number of leaves). For example, a plant vigor value for a crop plant may be calculated by multiplying the average leaf width by the number of leaves or by adding the average leaf width to a value 10 times the number of leaves. A statistical variation (e.g., standard deviation) of the plant vigor value with respect to the mean plant vigor value for the field (or for a region including multiple fields) may also be measured and used to generate a spatial map of plant vigor deviation. 
     In another embodiment, a plant disease identification is determined by comparing the reflectivity (e.g., visual spectrum, infrared or NDVI value) of portions of a single identified crop plant to a threshold value or to the average reflectivity value of the crop plant. If one or more portions of a crop plant has a reflectivity greater than the selected reflectivity threshold (and preferably has an area greater than an area threshold), the user is preferably alerted to potential disease and may be presented with a photographic image of the crop plant. 
     In another embodiment, a pest identification is determined by comparing the reflectivity (e.g., visual spectrum, infrared or NDVI value) of portions of a single identified crop plant to a threshold value or to the average reflectivity value of the crop plant. If one or more portions of a crop plant has a reflectivity greater than the selected reflectivity threshold (and preferably has an area greater than an area threshold), the user is preferably alerted to potential pest presence and may be presented with a photographic image of the crop plant. 
     Because the pest and disease identification methods discussed above may be improved by higher-resolution imagery, in some embodiments the UAV  110  returns to areas having poor NDVI values (either those selected by the user or those having sub-threshold NDVI values) and captures a high-resolution image, e.g., by flying at lower altitudes (e.g., 20 feet or lower) over the identified area or hovering (i.e., pausing at a stationary position) over the identified area and taking an image at a higher resolution and/or greater zoom level than during the initial NDVIA image capture flight. When obtaining low-altitude photos (e.g. 20 feet or lower), the UAV  110  preferably determines its distance to the ground to avoid collisions due to unknown changes in elevation. In some embodiments, the distance-to-ground may be determined using a sonar device on the UAV. In other embodiments, the distance-to-ground may be determined by processing an image and determining the number of pixels between crop rows and calculating the distance-to-ground based on the known distance between rows and known image-gathering settings such as the camera field of view and zoom level. 
     In some embodiments, the yield potential and/or ear potential of plants (e.g., seedling-stage plants) as discussed above may be alternatively determined by taking images of the crop at a significant angle (e.g., between 30 and 60 degrees) relative to vertical in order to observe and compare the height of individual plants. Plants shorter than neighboring plants by a threshold percentage are preferably identified as late-emerging plants having a lower yield potential. 
     In some embodiments, the orientation of identified crop plants may be determined by determining the plant orientation (e.g., relative to north) of a line best fitting through the spines of one or more leaves (e.g., a line running through two opposing leaves separated by 180 degrees about the stalk). A correlation of plant orientation to a yield performance may be determined based on later-developed yield map for the same field. A yield or ear potential prediction may be generated based in part on the plant orientation of each plant; for example, the yield potential may be reduced by 1 bushel per acre for each 5 degree-decrease in average plant orientation relative to north (i.e., in the angular offset of the leaves relative to north) per acre. In addition, a stalk diameter measurement taken from an aerial image (preferably at a significant angle from vertical, e.g., 45 degrees) or by a land-based camera to the side of the stalk may be improved by determining the orientation of the stalk based on the plant orientation. For example, the aerial or land-based image taken for stalk diameter measurement may be taken at a desired stalk measurement angle, e.g., normal to the plant orientation. In other embodiments, the stalk diameter measurement may be reduced by a factor related to the difference between the angle of the image relative to the stalk and the desired stalk measurement angle. The stalk diameter measurement may be used to modify the predicted yield or ear potential; for example, the predicted yield may be increased by 1 bushel per acre for every 0.5 cm increase in measured stalk diameter. 
     In some embodiments of the methods described herein, a measurement based on an image of a first portion of the field may be generalized to a larger portion of the field for purposes of generating a map of the measurement across the field. In some such embodiments, the larger portion of the field may comprise an area surrounding and/or adjacent to the first portion of the field. In other embodiments, the larger portion of the field may comprise a management zone (e.g., an adjacent or surrounding region of the field having a common soil type, yield range, planted hybrid type, or other characteristic or applied farming practice). 
     When introducing elements of the present invention or the embodiments thereof, the articles “a”, “an”, “the” and “said” are intended to mean that there are one or more of the elements. The terms “comprising”, “including” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. The use of terms indicating a particular orientation (e.g., “top”, “bottom”, “side”, etc.) is for convenience of description and does not require any particular orientation of the item described. 
     As various changes could be made in the above constructions and methods without departing from the scope of the invention, it is intended that all matter contained in the above description and shown in the accompanying drawing figures shall be interpreted as illustrative and not in a limiting sense.