Patent Publication Number: US-9832928-B2

Title: Crop sensing

Description:
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS 
     The present application is a continuation application claiming priority under 35 USC Section 120 from the pending U.S. patent application Ser. No. 13/771,682 filed on Feb. 20, 2013 and entitled CROP SENSING, the full disclosure of which is hereby incorporated by reference. The present application is related to co-pending U.S. patent application Ser. No. 13/771,727 filed on Feb. 20, 2013 and entitled PER PLANT CROP SENSING RESOLUTION, the full disclosure of which is hereby incorporated by reference. The present application is related to co-pending U.S. patent application Ser. No. 13/771,760 filed on Feb. 20, 2013 and entitled CROP SENSING DISPLAY, the full disclosure of which is hereby incorporated by reference. The present application is related to co-pending U.S. patent application Ser. No. 13/771,795 filed on Feb. 20, 2013 and entitled SOIL COMPACTION REDUCTION SYSTEM AND METHOD, the full disclosure of which is hereby incorporated by reference. 
    
    
     BACKGROUND 
     Crop harvesting machines, such as combines, sometimes include crop throughput sensors. Such sensors detect the ongoing crop yield of the swath of the harvesting machine. The information produced from such sensors may be inadequate for the ever-increasing sophistication of crop management. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic illustration of an example crop sensing system. 
         FIG. 1A  is a schematic illustration of a portion of the crop sensing system of  FIG. 1 . 
         FIG. 2  is a flow diagram of an example method that may be carried out by the crop sensing system of  FIG. 1 . 
         FIG. 3  is a flow diagram of another example method that may be carried out by the crop sensing system of  FIG. 1 . 
         FIG. 4  is a diagram of an example field map that may be generated by the crop sensing system of  FIG. 1  carrying out the method of  FIG. 3 . 
         FIG. 5  is a diagram of another example field map that may be generated by the crop sensing system of  FIG. 1  carrying out the method of  FIG. 3 . 
         FIG. 6  is a diagram of an example configuration display screen that may be presented by the crop sensing system of  FIG. 1 . 
         FIG. 7  is a diagram of an example output display screen that may be presented by the crop sensing system of  FIG. 1 . 
         FIG. 8  is a schematic illustration of an example of the crop sensing system of  FIG. 1 . 
         FIG. 9  is a side elevational view of an example crop sensing system comprising the crop sensing system of  FIG. 8 . 
         FIG. 10  is a schematic illustration illustrating the sensing of one or more crop attributes by the system of  FIG. 9 . 
         FIG. 11  is a flow diagram illustrating an example method that may be carried out by the crop sensing system of  FIG. 9 . 
         FIG. 12  is a flow diagram illustrating another example method that may be carried out by the crop sensing system of  FIG. 9 . 
         FIG. 13  is a front elevational view of an example harvesting platform for the crop sensing system of  FIG. 9 . 
         FIG. 14  is a top perspective view of an example row unit of the harvesting platform of  FIG. 13 . 
         FIG. 15  is a bottom perspective view of the row unit of  FIG. 14 . 
         FIG. 16  is a top perspective view of an example frame of the row unit of  FIGS. 14 and 15 . 
     
    
    
     DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS 
       FIG. 1  schematically illustrates an example crop sensing system  20 . Crop sensing system  20  outputs crop data and field maps with an enhanced resolution. In an example embodiment, the term “resolution” refers to the level of detail with regard to crop data and/or field maps. Resolution for crop data or field maps is determined by the smallest unit for which an attribute is sensed or for which an attribute is derived. Generally, the smaller the unit, the greater the resolution. Crop sensing system  20  outputs crop data and maps a field using sensed or derived attributes and/or identified conditions for individual units or portions of the field having a width less than a utilized crop harvesting width of a harvester. For example, even though a harvester may have a harvesting swath of 12 rows, crop sensing system  20  may output crop data or field maps providing crop attributes such as, yield, for less than 12 rows, such as on a row-by-row basis or even a plant-by-plant basis. Crop sensing system  20  may be similarly implemented with respect to non-row crops and non-row harvesters. The greater crop data resolution provided by crop sensing system  20  facilitates more advanced and sophisticated crop management. 
     Crop sensing system  20  comprises an agricultural machine, an example of which is the illustrated harvester  22 . Crop sensing system  20  further comprises display  24 , input  26 , processor  30  and memory  28 . Harvester  22  comprises a mobile machine configured to travel across a field or plot of land while harvesting a crop. Harvester  22  comprises head  34  and sensors  36 A- 36 H (collectively referred to as sensors  36 ). In other implementations, crop sensing system  20  may comprise other types of agricultural machines. Other examples of an agricultural machine are planters, cultivators, herbicide, insecticide or fertilizer applicators, cutters, mowers, pruners and/or the like. 
     Head  34  comprises a mechanism configured to gather and harvest a crop along a swath. The swath of head  34  has a utilized width, Wu, when harvesting crops. In an example embodiment, the utilized width Wu constitutes that portion of the length or swath width that is being utilized to harvest crops at a particular time. Although in most instances, the utilized width Wu is equal to the physical length of the swath of head  34 , in some circumstances, the utilized width Wu may constitute only a portion of the swath of head  34 , such as along an end row, waterway and/or the like. Head  34  includes various mechanisms for harvesting, such as mechanisms to sever or separate the crop from a remainder of a plant. Such mechanisms may include knives or blades, stripper plates, rollers, snapping roles, augurs, gathering chains or belts and/or the like. In one implementation, head  34  may comprise a corn head for a combine, wherein the corn head separates ears of corn from the remaining stalk. In another implementation, head  34  may comprise a grain head for a combine, wherein the grain along with the stalk is severed and subsequently threshed by the combine. In other implementations, head  34  may have other configurations. For example, although head  34  is illustrated as being located at a forward end of harvester  22  and as being interchangeable with other heads (facilitating the change of corn and grain heads), in other implementations, head  34  may be supported at other locations by harvester  22  and/or may be a permanent, non-interchangeable component of harvester  22 . 
     Sensors  36  comprise mechanisms to sense or detect one or more crop attribute values for a crop of forage plants. In one example embodiment, a forage plant comprises a poacea family or grass plant, a fabaceae family or legume plant and/or a forb plant, but excludes trees such as coniferous and deciduous trees. Examples of poaceae plants or grass plants comprise corn, rice, wheat, barley, millet, oats, sugarcane, sorghum, rye and bamboo. Examples of fabacea plants or legume plants comprise beans such as soybeans. An example of a forb comprises a sunflower plant. Sensors  36  detect one or more crop attribute values for the forage plants along the entire swath of head  34  or a portion of swath or harvesting width of head  34 . In one example embodiment, sensors  36  are located and carried by head  34 . In one example embodiment, sensors  36  are provided in each row harvesting portion of head  34 . In other implementations, sensor  36  may be provided at other locations. 
     Each of sensors  36  senses one more crop attribute values for crops harvested by a corresponding distinct portion of the utilized width Wu. Sensors  36  collectively detect multiple non-zero crop attribute values for a plurality of distinct portions of the utilized width Wu. Said another way, each of sensors  36  senses only a portion of the total crop being harvested at any moment in time by head  34 , wherein each of sensors  36  provide crop attribute values for just that portion. For example, in one embodiment, each of sensors  36  may cents a crop attribute for plants along an individual row, providing “per row” crop attributes. 
     For example, as shown by  FIG. 1 , in one circumstance, the entirety of head  34  may be receiving and harvesting crops such that the utilized width Wu of head  34  is substantially equal to the physical width or swath of head  34 . Sensors  36  each detect a less than whole portion or a fraction of the crop being harvested by the utilized width Wu. In one implementation, as indicated by partitioning  40 , the utilized width Wu may be partitioned or divided into two equal portions P 1  and P 2 , wherein sensors  36 A- 36 D provide a first crop attribute value for crops received by portion P 1  while sensors  36 E- 36 H provide a second crop attribute value for crops received by portion P 2 . In another implementation, as indicated by partitioning  42 , the utilized width Wu may be partitioned or divided into four equal portions P 1 , P 2 , P 3  and P 4 , wherein sensors  36 A- 36  B, sensors  36 C- 36 D, sensors  36 E- 36 F and sensors  36 G- 36 H provide independent and distinct crop attribute values for crops received by portions P 1 -P 4 , respectively. In yet another implementation, as indicated by partitioning  44 , the utilized width Wu may be partitioned or divided into 8 equal portions P 1 -P 8 , wherein sensors  36 A- 36 H each provide a distinct crop attribute value for crops received from portions P 1 -P 8 , respectively. 
     Although the individual portions of partitionings  40  and  42  are each illustrated as being associated with multiple sensors, in other implementations, each of the portions of partitionings  40  and  42  may alternatively be associated with a single sensor or with other numbers of sensors. Although head  34  is illustrated as including eight sensors, in other implementations, head  34  may include a greater or fewer number of such sensors along the physical width or swath of head  34 . For example, a crop row harvester may have greater than or less than eight rows, wherein the head of the harvester may similarly divide with greater than or less than eight row sensing sensors. Although head  34  is illustrated as being partitioned into equal portions, in other example embodiments, head  34  is partitioned into unequal portions, wherein sensors sense crop attributes for the unequal portions. For example, in another implementation, one of sensors  36  senses or detects crop attributes for an individual row while another center  36  senses crop attributes for a plurality of rows. 
     As shown by  FIG. 1 , in some implementations, each of sensors  36  may offer an even higher degree of crop sensing resolution by being configured to detect crop attribute values for the individual plants  46  themselves. In some implementations, the sensed crop attribute values for individual plants  46  may be aggregated into sets or collections  48  of plants based upon time, distance, a number of plants, and/or the like to reduce the amount of data that is processed or stored. Aggregating individual plant data may also improve useability of the data by eliminating noise in the data. The sensed crop attribute values for the individual plants  46  comprise values which are independent of, or do not merely comprise the presence or location of the plant. Such crop attribute values for the individual plants  46  do not merely comprise data regarding the population of plants or the spacing of plants. Instead, each of sensors  36  may be configured to specifically sense other attributes of the individual plant such that crop attribute values pertaining to estimated mass of the grain or product of the individual plant, the estimated mass other than grain (MOG) of the plant and/or the like may be derived. 
     For example, in one implementation, each of sensors  36  senses an interaction or impact force of grain upon a portion of the head  34 , such as a stripper plate of head  34 , wherein a mass of the grain may be derived based upon the sensed impact force and other sensed or known values. In another implementation, sensors  36  detect a stalk thickness/diameter of an individual plant. The stalk thickness/diameter of the individual plant may be detected either through physical contact with individual plant or through laser or optical and camera-based sensors. The mass of the grain or the MOG may be derived from the sensed stalk thickness/diameter. Other examples of sensors  36  include, but are not limited to for example, light detection and ranging (LIDAR or LADAR), structured light or stereo camera vision, strain gauges, and/or accelerometers (where crop impact is sensed), and/or the like. 
     Display  24  comprises a device by which information may be visually presented to an operator of harvester  22  or to a remotely located monitor/manager/operator of harvester  22 . Display  24  may comprise a monitor or screen which is stationary in nature or which is mobile in nature. In one implementation, display  24  is carried by harvester  22  along with the operator. In another implementation, display  24  comprises a stationary monitor remote from harvester  22 . In yet other implementations, display  24  may be mobile in nature, being provided as part of a computer tablet, smart phone, personal data assistant (PDA) and/or the like. 
     Input  26  comprises one or more devices by which controls and input may be provided to processor  28 . Examples of input  26  include, but are not limited to, a keyboard, a touchpad, a touch screen, a steering wheel or steering control, a joystick, a microphone with associated speech recognition software and/or the like. Input  26  facilitates the input of selections, commands or controls. In implementations where harvester  22  is remotely controlled or remotely steered, input  26  may facilitate such remote steering. 
     Memory  28  comprises a non-transient computer-readable medium or persistent storage device for storing data for use by processor  30  or generated by processor  30 . In one implementation, memory  28  may additionally store instructions in the form of code or software for processor  30 . The instructions may be loaded in a random access memory (RAM) for execution by processor  30  from a read only memory (ROM), a mass storage device, or some other persistent storage. In other embodiments, hard wired circuitry may be used in place of or in combination with software instructions to implement the functions described. For example, at least regions of memory  28  and processor  30  may be embodied as part of one or more application-specific integrated circuits (ASICs). In one implementation, memory  28  is carried by harvester  22 . In other implementations, memory  28  may be provided remote from harvester  22 . 
     In the example illustrated, memory  28  comprises data storage portion  52 , correlation module  54 , condition detection module  56 , display module  58  and operations adjustment module  60 . Data storage portion  52  contains historical data, such as lookup tables, facilitating analysis of data and information sensed by sensors  36 . Data storage portion  52  is further configured to store the crop attribute values directly sensed by sensors  36 , crop attribute values derived from the directly sensed crop attribute values using correlation module  54 , crop or field conditions identified based upon the directly sensed crop attribute values and/or the derived crop attribute values. Such stored information may be in various formats such as tables, field maps and/or the like. Data storage portion  52  may additionally store various settings and operator preferences. 
     Correlation module  54 , condition detection module  56 , display module  58  and operations adjustment module  60  comprise programming, software or code for directing the operation of processor  30 . Correlation module  54  instructs processor  30  in the correlation of one or more directly sensed crop attribute values detected by sensors  36  to derived crop attribute values. In other words, correlation module  54  instructs processor  30  and the derivation of crop attribute values, such as yield and/or the like, from directly sensed crop attribute values. In one implementation, correlation module  54  directs processor  30  to consult a lookup table in data storage portion  52  to correlate a stalk thickness/diameter as detected by sensors  36  to a grain mass or grain yield value, the derived crop attribute value. In another implementation, correlation module  54  directs processor  30  to carry out one or more algorithms/mathematical equations using a sensed impact of a plant or grain, and possibly using other additional factors, to derive a grain mass or yield of the plant. In other implementations, correlation module  54  directs processor  30  to derived crop attribute values from directly sensed crop attribute values in other fashions. 
     Condition detection module  56  directs processor  30  in the identification of field and/or crop conditions based upon the directly sensed crop attribute values or the derived crop attribute values. Examples of such field/crop conditions include, but are not limited to, the absence of plants, a field washout condition, an area of the field having yields suffering from wheel compaction beyond a predetermined threshold, the existence of a weed patch, the existence of yield loss due to inappropriate chemical application, and/or the like. In one implementation, condition detection module  56  directs processor  30  to consult a lookup table in data storage portion  52  to correlate a stalk thickness/diameter as detected by sensors  36  and/or a derived grain mass or grain yield value, the derived crop attribute value, to one of various predefined conditions, examples of which are set forth above. In another implementation, condition detection module  56  directs processor  30  to carry out one or more algorithms and/or mathematical equations using a directly sensed crop attribute value and/or a derived crop attribute value and to further compare the resulting calculation to one or more predefined thresholds to identify a field and/or crop condition. In other implementations, condition detection module  56  may direct processor  30  to identify or detect crop and/or field conditions in other fashions. 
     Display module  58  instructs processor  30  to generate control signals causing display  24  to present various information and/or prompts to an operator. For example, display module  58  may cause processor  30  to prompt an operator to select what partitioning  40 ,  42 ,  44  or individual plants is to be utilized, whether or not and how individual plant data is to be aggregated, how data is to be displayed (graph, chart, field map), what conditions are to be identified, how the operator is notified or alerted to such conditions, where such data is to be stored and/or the like. Display module  58  further instructs processor  30  in the display of data per operator preferences. 
     Operations adjustment module  60  comprises code or programming which directs processor  30  to automatically generate control signals adjusting operational parameters of harvester  22  based upon directly sensed or derived crop attribute values. In one implementation, operations adjustment module  60  generates control signals independently adjusting operational parameters of distinct portions of head  34  along its utilized width Wu. For example, operations adjustment module  60  may adjust the operational parameters of one row unit of head  34  independent of or differently with respect to another row unit of head  34  based upon directly sensed or derived crop attribute values for the crops being presently harvested by the different row units. For example, operations adjustment module  60  may, automatically in response to sensed or derived crop attribute values for crops harvested by a particular row unit, generate control signals for an actuator coupled to stripper plates of the row unit to adjust the spacing of stripper plates. This adjustment of stripper plates for the particular row unit may be independent of and different from the spacing adjustment of other stripper plates for other row units. As a result, the enhanced crop sensing resolution provides enhanced more refined control over the operation of harvester  22  to better harvest crops. 
     Processor  30  comprises one or more processing units configured to carry out instructions either hardwired as part of an application-specific integrated circuit or provided as code or software stored in memory  28 . In the example illustrated, display  24 , input  26 , memory  28  and processor  30  are each illustrated as being part of and carried by harvester  22 . In other implementations, one or more of such components may alternatively be located remote from harvester  22  and in communication with harvester  22  in a wireless fashion. In some implementations, some of the aforementioned functions of processor  30  in memory  28  may be shared amongst multiple processors or processing units and multiple memories/databases, wherein at least some of the processors and memories/databases may be located remote with respect to harvester  22 . 
       FIG. 2  is a flow diagram illustrating an example method  100  that may be carried out by system  20  to sense crop harvesting. As indicated by block  110 , processor  30  receives sensed crop attribute values for each of multiple portions of the utilized width Wu of head  34  of harvester  22 . For example, in an implementation where partitioning  44  is employed, sensor  36 A provides processor  30  with a first sensed crop attribute value for portion P 1 . Sensor  36 B provides processor  30  with a second sensed crop attribute value for portion P 2 . Sensors  36 C- 36 H similarly provide processor  30  with distinct crop attribute values for their associated portions P 3 -P 8 , respectively. In some implementations, the sensed crop attribute values may comprise a thickness or diameter of a plant stalk. In some implementations, the sensed crop attribute values may comprise an impact of a grain, such as an ear of corn, upon a portion of the head, such as a stripper plate. 
     As indicated by block  112 , processor  30 , following instructions provided by correlation module  54 , utilizes the received crop attribute values (CAV  1 ) for each of the portions to derive a secondary crop attribute value (CAV  2 ) for each of the portions. In one implementation, the secondary crop attribute value CAV  2  may comprise an estimated yield. In such an implementation, processor  30  derives an estimated yield for portions that are harvesting a crop. For example, in an implementation where partitioning  44  is employed, processor  30  derives a first yield value for portion P 1 , the second yield value for portion P 2 , a third yield value for portion P 3  and so on. In other implementations, other secondary crop attribute values (CAV  2 ), such as MOG, may be derived from the directly sensed crop attribute values CAV  1 . 
     As indicated by block  114 , processor  30  generates control signals, following the instructions contained in display module  58 , to store or display the derived crop attribute values. In one implementation, processor  30  stores the derived crop attribute values in data storage portion  52  of memory  28 . In one implementation, processor  30  transmits the derived secondary crop attribute values to a remote database or memory location via a wide area network, such as a wired or wireless connection. In some implementations, the root or base data, CAV  1 , is also stored and/or transmitted. In some implementations, the derived secondary crop attribute values are further displayed on display  24 . In some implementations, a visible or audible alert or notice may be output by display  24  in response to the derived secondary crop attribute value for a particular portion satisfying a predefined threshold. For example, if a derived crop yield for a particular portion P, such as a particular row unit of head  34 , falls below a predefined threshold, the operator may be provided with an alert or notice possibly indicating problems with the operation of the particular row unit. 
     As noted above, because system  20  determines crop attributes for individual portions of the harvesting width, such as individual rows or individual plants (or aggregations of plants along a row), system  20  provides an operator with more detailed information having a higher resolution, allowing the operator (or the harvesting machine automatically) to make adjustments to the setting of the harvester on a row-by-row basis to adapt to different conditions that may exist on a row-by-row basis. The operator may further utilize such information to correlate the yield results for individual rows during harvest to individual row settings of other operations such as planting, tillage, fertilizer, insecticide, or herbicide application and/or the like. As a result, row-by-row settings for such other equipment operations such as planter, tillage, fertilizer, insecticide or herbicide application may be subsequently adjusted based upon the row-by-row harvesting information. For example, strip till, planters, fertilizer, insecticide, herbicide applicators and/or the like may have given rise to uneven emergence or crop development rates, wherein row level sensing information allows an operator to determine that a problem exists, to identify causes and to identify solutions prior to the next harvesting season. 
     Such information may also be utilized to better calibrate other crop harvesting yield estimating devices. For example, per-row yield estimates may be used with yield data captured elsewhere on the machine, such as a grain yield sensor mounted on the clean grain auger, or off the machine, such as a weigh scale at a grain storage facility. The combination of this data may be used for purposes such as sensor calibration and post-harvest data processing. 
       FIG. 3  is a flow diagram illustrating an example method  200  that may be carried out by system  20 . As indicated by  FIG. 3 , method  200  comprises blocks  110  and  112  described above with respect to method  100 . As indicated by block  213 , processor  30 , following instructions contained in condition detection module  56 , utilizes the derived secondary crop attribute values and/or the directly sensed primary crop attribute values CAV  1  to identify crop and/or field conditions. For example, using the directly sensed primary crop attribute values and/or the derived secondary crop attribute values, processor  30  may identify a field condition such as yield reducing soil compaction, a wet spot, a weed patch, a washout, a yield reducing chemical application and/or the like. 
     Other factors may also be employed by processor  30  in identifying a crop or field condition. For example, historical planting data may be retrieved by processor  30  in making such a condition determination. In some implementations, processor  30  may additionally generate control signals causing display  24  to prompt an operator for input based upon visual inspection of the crop or field during harvest or during planting, wherein such input information may be factored into the identification of the condition by processor  30 . 
     As indicated by block  214 , processor  30  generates control signals, following the instructions contained in display module  58 , to store or display the identified field/crop condition. In one implementation, processor  30  stores the identified conditions for different regions of a field or plot of land in data storage portion  52  of memory  28 . In one implementation, processor  30  transmits the identified conditions to a remote database or memory location via a wide area network, such as a wired or wireless connection. In some implementations, the root or base data, CAV  1  and the derived secondary crop attribute values are also stored and/or transmitted. In some implementations, the identified conditions are further displayed on display  24 . In some implementations, a visible or audible alert or notice may be output by display  24  in response to the identification of a particular condition. In some implementations, processor  30  may identify and retrieve solutions from memory  28  and may generate control signals causing display  24  to display recommended remedial action for the identified condition. 
     Although system  20  and methods  100 ,  200  have been described with respect to harvester  22 , such individual row-by-row sensing may alternatively be incorporated on other vehicles or mobile machines. For example, such row-by-row sensing may be utilized on corn pickers, utilized in seed corn production, for achieving high-throughput phenotyping, allowing characterization of differential growth patterns/yields for different varieties, and/or the like. In one implementation, individual row sensors may be mounted on any vehicle providing information with regard to differential developmental rates (stalk size at different times a season). In yet other implementations, individual plant or row-row characterization may alternatively be implemented in other vehicles such as sprayers, scouting vehicles, autonomous vehicles, push carts and/or the like. 
       FIG. 4  is a diagram illustrating an example field map  300  that system  20  may store in storage portion  52  and/or present using display  24 . Field map  300  represents an implementation of methods  100  and  200  carried out by system  20  in which both the derived secondary crop attribute values and identified conditions are mapped across a field. Field map  300  has an enhanced resolution. In the example illustrated, field map  300  has a row-by-row resolution. Field map  300  is the product of partitioning the utilized width Wu of head  34  on a row-by-row basis, wherein a metric or crop attribute of the crop being harvested is detected for each and every row unit of head  34 . The different sensed metric values for the different rows of crop being harvested by the different row units of head  34  are utilized to derive the secondary crop attribute values, such as yield, for each row on a row-by-row basis. In the example shown in  FIG. 4 , field map  300  graphically depicts the derived secondary crop attribute values for 14 rows. As the harvester traverses the field, the sensed primary crop attribute values and the derived secondary crop attribute values (CAV  2 ) vary along the row. Based upon the derived secondary crop attribute values, processor  30  further detector identifies field conditions pursuant to method  200 . The identified conditions are further graphically presented as part of field map  300 . 
       FIG. 5  illustrates field map  400  that system  20  may store in storage portion  52  and/or present using display  24 . Field map  400  represents an implementation of methods  100  and  200  carried out by system  20  in which both the derived secondary crop attribute values and identified conditions are mapped across a field. Field map  400  has an enhanced resolution. In the example illustrated, field map  400  has a plant-by-plant resolution. Field map  400  is the product of partitioning the utilized width Wu of head  34  on a row-by-row basis and distinguishing each individual plant from adjacent individual plants, wherein a metric of the crop being harvested is detected for each and every plant. In other implementations, the field map  400  may be the product of the distinguishing aggregated sets of individual plants based upon time, distance or plant count. For example, rather than processing and storing a sensed crop attribute value on a plant-by-plant basis, crop attributes may be processed and/or stored for all those plants harvested by a particular row unit during a particular period of time, for all those plants harvested as a harvester traverses a predetermined distance or for a predetermined number of plants. The different sensed metric or crop attribute values for the individual plants or aggregation of individual plants harvested by the different row units of head  34  are utilized to derive the secondary crop attribute values, such as yield, for each plant or aggregation of plants. In the example shown in  FIG. 5 , field map  400  graphically depicts the derived secondary crop attribute values for 15 plants. As the harvester traverses the field, the sensed primary crop attribute values and the derived secondary crop attribute values (CAV  2 ) vary from plant to plant. Based upon the derived secondary crop attribute values, processor  30  further detects or identifies field conditions pursuant to method  200 . The identified conditions are further graphically presented as part of field map  400 . 
       FIGS. 6 and 7  illustrate example screen displays by display  24  under the operation of system  20 .  FIG. 6  illustrates an example configuration screen display  500  which may be presented on display  24  by processor  30 , following instructions in display module  58 . Screen display  500  presents various prompts or selections for options or modes for the configuration and operation of system  20  from which an operator may choose. As indicated by prompts  502 , display  24  allows the operator to input and select the interval for which the sensed crop attributes for individual plants  46  are to be aggregated into a single data value for processing and/or storage. In the example illustrated, the operator may select from a particular time, a particular distance or a particular number of individual plants. 
     As indicated by prompts  504 , the operator may indicate how crop attribute values for the particular interval or aggregation of individual plants are to be derived: determining an average derived crop attribute value for the aggregation of plants, determining a median value for the derived crop attribute value or a range of the derived crop attribute values. 
     As indicated by prompts  506 , the operator is allowed to select how the derived secondary crop attribute values are displayed: whether on a continuous basis or only in response to a predefined condition being met. As indicated by prompts  508  the operator is allowed to indicate how identified conditions are presented on display  24 : whether continuously displayed or only when certain conditions are identified. 
     As indicated by prompts  510 , the operator is allowed to select which conditions are identified and which conditions are then presented on display  24  when discovered. Examples of such conditions include: no plants, wash out, wheel compaction, chemical and weed patch. In other implementations, other options or selections may be provided to the operator for the aggregation interval, the processing, the display and the conditions. 
       FIG. 7  illustrates an example output display screen  550  which may be presented on display  24  by processor  30 , following instructions in display module  58 . Screen display  550  presents the output of system  20  pursuant to the configuration selections made with respect to the screen shown in  FIG. 6 . As shown by data rows  552  and  554 , processor  30  outputs on display  24  the derived momentary secondary crop attribute of yield for each of the eight combine rows. In other words, data rows  552  and  554  identify the momentary yield (bushels per acre) for a crop that is being harvested for each of the eight row units of head  34 . 
     As indicated by data row  556 , processor  30  further retrieves data from data storage portion  52  and correlates the particular combine rows to previously stored planter rows (the row units of the planter that planted the particular rows that are now being harvested by the harvester/combine). In some implementations, additional planting information for each of the indicated planting rows may further be retrieved from data storage portion  52  by processor  30  and presented on screen display  550 . For example, different planted rows may have different values for the type or amount of applied herbicide, insecticide, or seed used in the particular row. In another example, bins containing seed and agrichemicals may have different weights in different portions of the field. As a result, the operator may be presented with information that may assist in subsequent planting by correlating different planting conditions to different yield results on a row-by-row basis. In the current example, data from planting is correlated with the per-row yield. Without limitation, data could also be drawn from past row-by-row data collection such as during tillage, spraying, scouting, land-based scouting, and aerial scouting. The data may be collected or aggregated at resolutions such as greater than field, field level, sub-field, row, and plant levels. In some embodiments, the data is geo-referenced and time-stamped to facilitate use in later analysis. 
     In some implementations, in addition to correlating machine-relative positions during different operations (row 3 on an 8 row combine to row 11 on a 16 row planter), system  20  may further indicate on display  550  the direction of travel of the particular mobile machine for the particular rows. For example, the direction of travel may be very beneficial when comparing processing data to tillage data where the direction of travel may be at 45° from planting and harvesting directions of travel. 
     As indicated by prompts  558 , in addition to presenting such information in the form of a chart, system  20  further allows the operator to select other formats for presenting such information. In the example illustrated, the operator may request that such information be additionally presented as a bar graph. In other implementations, other derived crop attribute values, such as MOG, may also be displayed in the same format or other formats. 
     As indicated by data line  560 , using the results of condition detection module  56  and following the instructions of display module  58 , processor  30  presents the detected condition existing for an individual row or group of rows. In the example illustrated, processor  30  has determined, with a 73% degree of confidence, that the commodity tank weight during planting was an issue that may have resulted in soil compaction which may have resulted in lower yields for the particular rows. As indicated by portion  562 , processor  30  additionally consults data storage portion  52  (or additional local or remote databases) to analyze any possible causes for the identified conditions and present such possible causes as part of screen display  550 . In the example illustrated, processor  30  presents, on display  24 , the various conditions that occurred for the particular set of rows, for example, the weight of the material in the commodity tank was high during planting of the particular rows, the landscape of the rows is that of a depression and that there were large amounts of rain prior to planting. 
       FIG. 8  schematically illustrates crop sensing system  620 , an example implementation of crop sensing system  20 . Crop sensing system  620  comprises crop characterizer  623 , on-board operator output  624 , on-board operator input  626 , localization input  627 , memory  628 , on-board processor  630 , static database  700 , learned database  702 , online database  704 , communications  706 , enterprise back office  708 , third-party service providers  710 , other on-site machines  712  and remote operators/observers  714 . 
     Crop characterizer  623  comprises a device configured to sense or detect multiple non-zero crop attribute values for a plurality of distinct portions of the utilized width of a harvesting machine. In the example described, crop characterizer  623  detects crop attributes or crop characteristics on at least a row-by-row basis. Individual row of crops  720  are independently sensed and different attribute values may be identified and stored for the individual rows. In the example described, crop characterizer  623  detects crop attributes on a plant-by-plant basis. Individual plants  722  are independently sensed and different attribute values may be identified and stored for the individual plants or for a predefined aggregation of individual plants along a row  720  (for example, an aggregation based upon time, distance or plant count as described above). As a result, crop characterizer  623  facilitates data gathering and field maps having an enhanced resolution for more sophisticated analysis and crop management. In one example, crop attributes are defined by crop characterizing  623  on both a plant-by-plant basis and a row-by-row basis. In another example, crop attributes are defined for a selected one of the plant-by-plant basis or the row-by-row basis. 
     Crop characterizer  623  comprises sensors  636  and one or more cameras  637 . Sensors  636  are similar to sensors  36  described above. Sensors  636  comprise mechanisms to concurrently sense or detect one or more crop attribute values for multiple portions of a utilized crop harvesting width of the harvester. Said another way, each of sensors  636  senses only a portion of the total crop being harvested at any moment in time by the harvester  622 , wherein each of sensors  636  provide crop attribute values for just that portion. As noted above, in one implementation, sensors  636  provide crop attribute values on a row-by-row basis. In another implementation, sensors  636  provide crop attribute values on a plant-by-plant basis. Such crop attribute values for the individual plants  722  do not merely comprise of data regarding the population of plants or the spacing of plants. Each of sensors  636  may be configured to specifically sense other attributes of the individual plant such that crop attribute values pertaining to estimated mass of the grain or product of the individual plant, the estimated mass other than grain (MOG) of the plant and/or the like may be derived. 
     For example, in one implementation, each of sensors  636  senses an interaction or impact force of grain upon a portion of the harvester, such as a stripper plate. A mass of the grain may be derived based upon the sensed impact force. In another implementation, sensors  636  detect a stalk thickness/diameter of an individual plant either through physical contact with individual plant or through non-physical contact mechanisms such as laser or optical and camera-based sensors). The mass of the grain or the MOG may be derived from the sensed stalk thickness/diameter. Examples of sensors  636  include, but are not limited to, light detection and ranging (LIDAR or LADAR), structured light or stereo camera vision, strain gauges and/or accelerometers (where crop impact is sensed). 
     In one implementation, camera  637  comprises an optical capture device carried by the harvester  622  to capture one or more rows  720  just prior to the harvesting of such rows  720 . In one implementation, camera  637  captures images that are used to detect or determine one or more crop attributes or crop characteristics on a row-by-row basis or a plant-by-plant basis. In one implementation, camera  637  employee stereo vision or LIDAR for such detection. In one implementation, camera  637  captures images of the crop prior to harvesting, wherein the individual images or portions of video are linked to the crop attribute values detected by sensors  636 . These values may be stored. The captured images or video are linked and indexed in a time-based manner or location-based manner to particular regions, individual rows or individual plants for which data is detected by sensors  636 . As a result, when reviewing directly sensed crop attribute values (as detected by sensors  636 ) or derived crop attribute values for a particular region of a field, a particular set of rows of the field or a particular grouping of plants in the field, the operator may also retrieve and view images or videos of the actual region of the field, the particular rows of the field or the particular plants of the field corresponding to the data being viewed in a chart or map. Thus, system  620  allows an operator/monitor to visibly review the actual crops to either identify one or more conditions that may have affected the crop attribute such as yield or allows the operator/monitor to visibly confirm the crop/field condition identified by processor  630  as a reason for a particular crop yield or other attribute. For example, based upon data from sensors  636 , processor  630  may output a conclusion that a drop in yield was caused by a wet spot in the field. Camera  637  permits the operator to pull up (from memory) actual stored video images of the particular portion of the field to confirm whether indeed the particular rows were in a wet spot. 
     In the example illustrated, system  620  offers several modes of operations for characterizer  623 . In one mode, sensors  636  may be employed for crop characterization. In another mode, camera  637  may be employed for crop characterization. In yet another mode, both sensors  636  and camera  637  may be utilized for crop characterization. In some implementations, system  620  may omit one of sensors  636  or camera  637 . 
     In some implementations, crop characterizer  623  may additionally comprise a local processor  639 . Processor  639  receives signals from sensors  636  and conditions such signals prior to their transmission to on-board processor  630  via datalink  730 . For example, in some implementations, processor  639  derives other crop attribute values from the signals prior to their transmission to processor  630 . Processor  639  may filter such signals to reduce noise prior to transmission by link  730 . In some implementations, processor  639  may trim data or compress data prior to transmitting such data across link  730  to processor  630  to reduce transmission and/or processing loads. In another implementation, processor  639  may be omitted. 
     On-board operator output  624  comprises one or more devices carried by harvester  622  by which information and data may be presented to an onboard operator of harvester  622 . Output  624  may comprise a display comprising a monitor or screen with or without a speaker. On-board operator input  626  comprises one or more devices carried by harvester  622  by which selections and/or data may be input, entered and provided by a local operator  32  riding or operating harvester  622 . Examples of input  626  include, but are not limited to, a keyboard, a touchpad, a touch screen, a steering wheel or steering control, a joystick, a microphone with associated speech recognition software and/or the like. In one implementation, input  626  may be provided as part of output  624  in the form of a touchscreen. 
     Localization input  627  comprises an input to processor  630  which provides geo-data to processor  630 . In other words, input  627  provides location or positional information to processor  630 . For example, in one implementation, localization input  627  may comprise a global positioning system (GPS) receiver. In other examples, other geo-data sources may be utilized. 
     Memory  628  comprises a non-transient computer-readable medium or persistent storage device for storing data for use by processor  630  or generated by processor  630 . In one implementation, memory  628  may additionally store instructions in the form of code or software for processor  630 . The instructions may be loaded in a random access memory (RAM) for execution by processor  630  from a read only memory (ROM), a mass storage device, or some other persistent storage. In other embodiments, hard wired circuitry may be used in place of or in combination with software instructions to implement the functions described. For example, at least regions of memory  628  and processor  630  may be embodied as part of one or more application-specific integrated circuits (ASICs). In the example illustrated, memory  628  is carried by harvester  622 . In other implementations, memory  628  may be provided remote from harvester  622 . 
     In the example illustrated, memory  628  comprises configuration module  650 , correlation module  654  and condition detection module  656 . Configuration module  650  comprises software code and associated stored data regarding the configuration of system  620 . In the example illustrated, configuration module  650  includes sub-modules which direct processor  630  to prompt for selections from an operator, to store such selections and to operate according to such various selections. The stored selections control how processor  630  handles and analyzes data from characterizer  623  and how data or information is presented on output  624 . In the example illustrated, configuration module  650  comprises interval sub-module  670 , processing sub-module  672  and notification sub-module  674  which cooperate to present display screen  500  shown and described above with respect to  FIG. 6 . Interval sub-module  670  prompts for and stores operator input regarding how individual plants are to be aggregated such as the various aggregation technique prompts  502  in  FIG. 6 . Processing sub-module  672  prompts for and stores operator input regarding how such data is to be processed, for example, using statistical values such as average, median or range. Notification sub-module  674  prompts for and stores display settings such as with prompts  506  and  508  as well as prompts  510 . 
     Correlation module  654  and condition detection module  656  comprise programming, software or code for directing the operation of processor  630 . Correlation module  654  instructs processor  630  in the correlation of one or more directly sensed crop attribute values detected by sensors  36  to derived crop attribute values. In other words, correlation module  654  instructs processor  630  and the derivation of crop attribute values, such as yield and/or the like, from directly sensed crop attribute values or possibly along with other factors or inputs. In one implementation, correlation module  654  directs processor  630  to consult a lookup table in a database to correlate a stalk thickness/diameter as detected by sensors  636  to a grain mass or grain yield value, the derived crop attribute value. In another implementation, correlation module  654  directs processor  630  to carry out one or more algorithms/mathematical equations based upon a sensed impact of a plant or grain to derive a grain mass or yield of the plant. In other implementations, correlation module  654  may direct processor  630  to derived crop attribute values from directly sensed crop attribute values in other fashions. 
     Condition detection module  656  directs processor  630  in the identification of field and/or crop conditions based upon the directly sensed crop attribute values or the derived crop attribute values. Examples of such field and such are crop conditions include, but are not limited to, the absence of plants, a field washout condition, an area of the field having yields suffering from wheel compaction beyond a predetermined threshold, the existence of a weed patch, and the existence of yield loss due to inappropriate chemical application. In one implementation, condition detection module  656  directs processor  630  to consult a lookup table in the database to correlate a stalk thickness/diameter as detected by sensors  636  and/or a derived grain mass or grain yield value (the derived crop attribute value) to one of various predefined conditions, examples of which are set forth above. In another implementation, condition detection module  656  directs processor  630  to carry out one or more algorithms/mathematical equations using a directly sensed crop attribute value and/or a derived crop attribute value and to further compare the resulting calculation to one or more predefined thresholds to identify a field and/or crop condition. In other implementations, condition detection module  656  may direct processor  630  to identify or detect crop and/or field conditions in other fashions. 
     Static database  700  comprises a data storage containing data regarding historical or predefined data such as historical planting data, historical yield information, historical field or soil data (e.g., topography, soil type). Static database  700  may additionally contain tables and other information for correlating sensed crop attribute values to derived crop attribute values. Learned database  702  comprises a data storage containing data that varies as harvester  622  travels across the field. Database  702  stores the raw directly sensed crop attribute values from sensors  636  and/or camera  637 , camera captured video or images, derived crop attribute values, and varying or adjustable harvester operational parameters, for example, harvester velocity, head height, and other harvester settings. In one example, database  702  further stores GPS data. 
     In the example illustrated, static database  700  and learned database  702  comprise databases that are part of memory  628  on board harvester  622 . In other implementations, such databases  700 ,  702  may be remote from harvester  622  and may be accessed through communication  706 . Online database  704  comprises a database that is accessed through a wide area network or a local area network using communication  706 . Online database  704  may contain additional information for use by processor  630  and harvester  622 . Communication  706  comprises a communication network facilitating communication between harvester  622  and remote entities such as online database  704 , office  708 , service provider  710 , other on-site machines  712  and remote operator/observer  714 . 
     Enterprise back office  708  comprises a location remote from harvester  622  such as the home farm. Enterprise back office  708  may include computing devices and a database, wherein processor  630  transmits data stored in learned database  702  to office  708  through communication  706  for backup and/or remote analysis. Third-party service provider  710  comprises a server in communication with harvester  622  through communications  706  and associated with a third-party such as an agronomist, a seed dealer, a seed company, a chemical, insecticide or fertilize supplier or third-party data storage host. 
     As indicated by  FIG. 8 , other harvesters or other machines on a particular worksite or field may also be in communication with harvester  622  through communications  706 . As a result, sensed crop data may be shared amongst such multiple machines on a particular field or worksite. In some implementations, harvester  622  may communicate with the remote operator/observer  714  through communications  706 . As a result, harvester  622  may be remotely controlled (the steering of harvester  622  and/or the adjustment of settings for the operation of crop sensing by harvester  622 ). 
       FIGS. 9 and 10  illustrate crop sensing system  820 , an example of crop sensing system  20  or an example of crop sensing system  620 . In the example illustrated, crop sensing system  820  comprises a harvester  822  (in the form of a combine). Crop sensing system  820  comprises each of the components illustrated and described with respect to  FIG. 8 , some of which are shown and similarly numbered in  FIG. 9 , except that crop sensing system  820  specifically includes sensors  836 , particular examples of sensors  636 . 
     Harvester  822  comprises a chassis  912  which is supported and propelled by ground engaging wheels  914 . Although harvester  822  is illustrated as being supported and propelled on ground engaging wheels  914  it can also be supported and propelled by full tracks or half-tracks. A harvesting assembly  916  (shown as a corn head) is used to take up crop and to conduct it to a feeder house  918 . The crop is conducted by the feeder house  918  to a beater  920 . The beater  920  guides the crop upwardly through an intake transition region  922  to a rotary threshing and separating assembly  924 . Although harvester  822  is described as a rotary combine, in other implementations, harvester  822  may comprise other types of combines (for example combines having a transverse threshing cylinder and straw walkers or combines having a transverse threshing cylinder and rotary separator rotors) or other agricultural harvesting machines including, without limitation, self-propelled forage harvesters, sugar cane harvesters, and windrowers 
     The rotary threshing and separating assembly  924  comprises a rotor housing  926  and a rotor  928  arranged in the rotor housing  926 . The harvested crop enters the rotor housing  926  through the intake transition region  922 . The rotary threshing and separating assembly  924  threshes and separates the harvested crop. Grain and chaff fall through grates at the bottom of the rotor housing onto a cleaning assembly  934 . The cleaning assembly  934  removes the chaff and conducts the clean grain to a grain elevator  936  which conducts upwardly to a distributing screw conveyor  938 . The distributing screw conveyor  938  deposits the clean grain in a grain tank  940 . The clean grain in the grain tank  940  can be unloaded through an unloading auger  942  into a trailer or truck. Threshed straw separated from the grain is conducted out of the rotary threshing and separating assembly  924  through an outlet to a discharge beater  946 . The discharge beater  946  ejects the straw out the rear of harvester  822 . 
     The operation of harvester  822  is controlled from an operator&#39;s cab  948 . In the illustrated embodiment, localization input  627  (a geographic position sensor in the form of a receiver) for the reception of GPS signals (global positioning system) is attached above the operator&#39;s cab  948 . A speed sensor measuring the speed of the wheels  914  may be provided. Mounted on one side of the clean grain elevator  936  is a capacitor moisture sensor  952  for measuring the moisture content of the clean grain. Such a sensor is disclosed in DE 199 34 881 A., the full disclosure of which is hereby incorporated by reference. A mass flow sensor  954  is located at the outlet of the clean grain elevator  936 . The mass flow sensor  954  comprises an impeller plate mounted for rotation about a horizontal axis. Its deflection is dependent upon the mass flow rate of the clean grain. The deflection of the impeller plate is measured and thus data on the mass flow rate of the harvested grain is provided. Such a sensor is described in EP 0 853 234 A (the full disclosure of which is hereby incorporate by reference) and the documents recited therein. 
     Sensors  836  are similar to sensors  636  in that sensors  836  comprise mechanisms to concurrently sense or detect one or more crop attribute values for multiple portions of a utilized crop harvesting width of the harvester. Said another way, each of sensors  836  senses only a portion of the total crop being harvested at any moment in time by the harvester  822 , wherein each of sensors  836  provide crop attribute values for just that portion. In one implementation, sensors  836  provide crop attribute values on a row-by-row basis. In another implementation, sensors  836  provide crop attribute values on a plant-by-plant basis or based upon an aggregation of individual plants. Such crop attribute values for the individual plants do not merely comprise of data regarding the population of plants or the spacing of plants. Instead, each of sensors  836  are configured to specifically sense other attributes of the individual plant such that crop attribute values pertaining to estimated mass of the grain or product of the individual plant, the estimated mass other than grain (MOG) of the plant and/or the like may be derived. 
     As further shown by  FIG. 9 , crop sensing control unit  956  is located in the operator&#39;s cab  948  or somewhere else on the harvester  822 . Crop sensing control unit  956  comprises each of memory  628 , processor  630  and databases  700 ,  702  described above with respect to  FIG. 8 . Crop sensing control unit  956  is in communication with localization input  627 , the moisture sensor  952 , the mass flow sensor  954 , the speed sensor, when present, and sensors  836 . Crop sensing control unit  956  is provided with an internal clock or receives external time signals, for example from the input  627 . Crop sensing control unit  956  records the amount of harvested grain, measured by means of the mass flow sensor  954 , and its moisture content, measured by means of the moisture sensor  952 , dependent on the geographical position of the harvester  822  (measured by means of the localization input  627 , e.g., a GPS receiver. Crop sensing control unit  956  additionally receives signals and/or data from sensors  836  and derives one or more crop attribute values for each of multiple distinct portions of harvesting platform  916 . In one implementation, crop sensing control unit  956  derives one or more crop attributes for individual rows or road units of harvesting platform  916 , wherein data is processed and stored on a row-by-row basis. In another implementation, crop sensing control unit  956  derives and stores one or more crop attributes for individual plants or aggregations of individual plants. Crop sensing control unit  956  logs the data in learned database  702  and produces a field summary which may also be stored in learned database  702  and presented on output  624 . In one implementation, crop sensing control unit  956  creates a yield map, similar to either of maps  300  or  400  shown in  FIGS. 4 and 5 , respectively. 
       FIG. 10  schematically illustrates an example operation of sensors  836  and crop sensing control unit  956 . As shown by  FIG. 10 , in one implementation, sensors  836  are mounted to or within harvesting platform  916  (shown as a corn head). In one implementation, sensors  836  comprise accelerometers, strain gauge sensors and/or the like mounted to or coupled to at least one stripper plate  980  along multiple row units of harvesting platform  916 . In one implementation, sensors  836  are mounted to or couple to at least one stripper plate  980  along each row unit of harvesting platform  916 . Sensors  836  are in communication with processor  630  of crop sensing control unit  956  (shown in  FIG. 9 ). In one implementation, one sensor is associated with one row unit. In other implementations, more than one sensor may be associated with one row unit. In such a case, the sensors may be of the same type sensing the same or different attributes, or of different types sensing the same or different attributes. In yet other implementations, one sensor may be associated with multiple row units. 
       FIG. 11  is a flow diagram of an example method  1000  by which crop sensing control unit  956  may determine or derive one or more crop attribute values using signals from sensors  836 . As indicated by block  1002 , processor  630  receives signals from sensors  836  sensing the interaction, such as contact or movement, of the crop with or with respect to the head or harvesting platform  916 . In the example illustrated in which harvesting platform  916  comprises a corn head, processor  630  receive signals from sensors  836  that are coupled to at least one stripper plate  980  of each row unit, wherein sensors  836  sense an impact of an ear  984  of corn upon the one or more stripper plates  980  along a row unit. As indicated by block  1004 , based at least upon this sensed interaction, i.e., the impact of the ear  984  of corn upon the one or more stripper plates  980 , processor  630  derives a secondary crop attribute value, such as yield. As indicated by block  1006 , processor  630  stores and/or displays the secondary crop attribute value. Although processor  630  is described as receiving signals from sensors  836  which are illustrated as being coupled to stripper plates so as to sense interaction of ear  984  with stripper plates  980 , in other implementations, processor  630  may receive crop interaction signals from sensors  836  mounted at other locations to sense other interactions of the plant or its grain product with harvesting platform  916 . 
       FIG. 12  is a flow diagram of method  1050 , a specific implementation of method  1050  by which crop sensing control unit  956  may determine or derive one or more crop attribute values using signals from sensors  836 . As indicated by block  1052 , processor  630  receives signals from sensors  836  sensing a pulse of ear impact upon stripper plate  980 . As indicated by block  1054 , processor  630  further determines the velocity component of the ear  984 . Such a velocity may be determined based at least in part upon the velocity of harvester  822  as it moves in the direction indicated by arrow  988 . This velocity may be obtained from the aforementioned speed sensor or from localization input  627 . As indicated by block  1056 , processor  630  divides the sensed pulse by the determined velocity to estimate a mass of the individual ear  984 . 
     As indicated by block  1058 , processor  630  may then derive the crop attribute, such as yield, for ear  984  based upon the determined mass of ear  984 . In one implementation, processor  630  may consult a lookup table, such as contained in database  700 , to derive a grain yield for ear  984 . Using such information, processor  630  may also determine a yield for the individual plant. Based upon the spacing in time between consecutive pulses provided by sensor  836 , processor  630  may determine whether consecutive pulses are the product of two ears on a single plant or two ears on separate plants. As a result, processor  630  may determine the yield for the individual plant. Results for individual plants may be aggregated (as described above) or may not be distinguished from one another along a row to output yield on a row-by-row basis. As indicated by block  1068 , the derived crop attributes, such as yield, may be stored in learned database  702  and/or may be presented on output  624 . 
       FIGS. 13-16  illustrate harvesting platform  1116  (shown as a corn head) and sensors  1136 , shown in  FIG. 14 , examples of harvesting platform  916  and sensors  836  described above. As shown by  FIG. 13 , harvesting platform  1116  comprises a frame  1212 , row units  1214 , auger  1215 , outer dividers  1216 ,  1218  and central dividers  1220 . Frame  12  extends across the physical width of harvesting platform  1116  and supports row units  1214 . Row units  1214  harvest corn from individual rows of crop and convey the harvested corn to auger for further conveyance into harvester  1212 . Row units  1214  are spaced in a side-by-side relationship with each other a distance commensurate with the spacing between adjacent rows of corn to be harvested. In some implementations, the row units  1214  may be adjustable to accommodate other corn row spacings. Outer dividers  1216 ,  1218  and central dividers  1220  separate co-mingled stalks of adjacent rows from one another. Central dividers  1220  extend between consecutive row units  1214 . Dividers  1216 ,  1218  and  1220  cooperate to define longitudinal passages  1222  which are centered relative to the rows to be harvested and a fore-and-aft extending relatively narrow throat  1224  defined by each row unit  1214 . 
       FIGS. 14-16  illustrate one example of a row unit  1214  in more detail. As shown by  FIGS. 14-16 , in addition to sensor  1136 , each row unit  1214  comprises frame  1226 , right and left stripper plates, also known as deck plates,  1228 ,  1230 , right and left gathering units  1232 ,  1234  and snapping rolls  1236 ,  1238  (shown in  FIG. 15 ). As shown by  FIG. 16 , frame  1226  comprise a U-shaped member having right and left, fore and aft extending legs  1240 ,  1242  interconnected by a transversely extending bracket or bight  1244 . Legs  1240 ,  1242  support stripper plates  1228 ,  1230  as well as right and left gathering units  1232 ,  1234  and snapping rolls  1236 ,  1238 . 
     Stripper plates  1228 ,  1230  comprise plates having inner edges spaced apart so as to define narrow throat  1224 . Throat  1224  receives cornstalks of an aligned row as row unit  1214  moves along a row of crops. As row unit  1214  is moved along the row, the stalks are drawn down through throat  1224  with the assistance of snapping rolls  1236 ,  1238  (shown in  FIG. 15 ) such that ears of corn carried by the stalk impact the stripper plates and are separated from the stalk. Such stripper plates  1228 ,  1230  may include elongated openings for receiving fasteners such that stripper plates  1228 ,  1230  may be laterally adjusted to adjust the width or size of throat  24 . As noted above, in some implementations, an actuator may be coupled to stripper plates to automatically adjust the spacing a stripper plates  1228 ,  1230  in response to control signals from processor  630  based upon sensor derived crop attribute values for the particular row unit  1214 . 
     In the example illustrated, at least one sensor  1136  (schematically shown), such as a accelerometer or strain gauge is mounted to an underside of at least one of stripper plates  1228 ,  1230  to sense the impact of the ear of corn upon stripper plates  1228 ,  1230 . As discussed above with respect to sensors  836  and a method  1050 , signals produced by sensor  836  are used by processor  630  to ultimately derive a mass of the particular ear corn that has impacted stripper plates  1228 ,  1230  as well as to derive the yield from the particular ear of corn. 
     Right and left gathering units  1232 ,  1234  gather the ears of corn and transport such ears rearwardly towards auger  1215  (shown in  FIG. 13 ). In the example illustrated, each of gathering units  1232 ,  1234  comprises driveshaft  1240 , drive sprocket  1242 , idler shaft  1244 , idler sprocket  1246 , gathering chain  1248 , and chain tensioning assembly  1250 . Each of drive shafts  1240  extends from and is driven by a gearbox  1252  to rotationally drive sprocket  1242 . Each of drive shafts  1240  extends through a corresponding opening  1254  in bight  1244  of frame  1226  (shown in  FIG. 16 ). Drive sprockets  1242  cooperate with idler sprockets  1246  to support and drive gathering chain  1248 . 
     Idler shafts  1244  are rotationally supported by chain tensioning assemblies  1250 . Idler shafts  1244  rotationally support idler sprockets  1246 . Chain tensioning assemblies  1250  adjustably support idler sprockets  1246  for movement between different fore and aft positions to adjust the tension of gathering chains  1248 . Snapping rolls  1236 ,  1238  are mounted to a pair of drive shafts  1260  with project forwardly from gearbox  1252 . As noted above, snapping rolls  1236 ,  1238  draw cornstalks down through throat  1224 , between stripper plates  1228 ,  1230 . Because ears of corn are too large to pass down through throat  1224 , such ears impact stripper plates  1228 ,  1230  and are detached or severed from the stalks for being gathered by gathering chains  1248 . 
     Although the present disclosure has been described with reference to example embodiments, workers skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope of the claimed subject matter. For example, not everything feature shown in drawings is required and one or more features may be omitted. Although different example embodiments may have been described as including one or more features providing one or more benefits, it is contemplated that the described features may be interchanged with one another or alternatively be combined with one another in the described example embodiments or in other alternative embodiments. Because the technology of the present disclosure is relatively complex, not all changes in the technology are foreseeable. The present disclosure described with reference to the example embodiments and set forth in the following claims is manifestly intended to be as broad as possible. For example, unless specifically otherwise noted, the claims reciting a single particular element also encompass a plurality of such particular elements.