Correcting bias in agricultural parameter monitoring

A sensing system bias is reduced across a first agricultural machine and a second agricultural machine. A collection of agronomic data is accessed, that is indicative of an estimated crop yield. The collection that is accessed, for example, includes at least a first set of data sensed by the first agricultural machine and a second set of data sensed by the second agricultural machine. In addition, the first and second sets of data can be scaled based on a yield correction factor. A bias between the scaled first set of data and the scaled second set of data is determined, and a smoothing operation is performed on the scaled first and second sets of data. For example, performing the smoothing operation can include generating a calibration correction factor based on the determined bias, removing the bias between the scaled first set of data and the scaled second set of data to obtain a corrected set of crop yield data, and using the calibration correction factor in sensing the first set of data on the first agricultural machine and the second set of data on the second agricultural machine.

FIELD OF THE DESCRIPTION

The present disclosure generally relates to techniques for obtaining accurate measurements of spatial parameters. More specifically, but not by limitation, the present disclosure relates to correcting post calibration bias in systems configured to measure agronomic parameters.

BACKGROUND

There are a wide variety of agricultural machines currently in use. Such machines include combine harvesters (combines), planting machines, tillage machines, and nutrient applicators, among others. Many agricultural machines operate not only to perform certain machine functionality, but also to obtain information about the operation being performed.

To obtain this information, machines may use one or more sensors during the operation. These sensors may be calibrated at the time of manufacture or at some time before, during, or after the operation is performed.

Calibration generally refers to a method of accounting for inaccuracies in data measurements. To calibrate a system with one or more sensors, for example, a measurement value is compared to a known value of accuracy to determine a difference between the two. The determined difference is then used to adjust the system so that future data measurements are more accurate.

SUMMARY

A sensing system bias is reduced across a first agricultural machine and a second agricultural machine. A collection of agronomic data is accessed, that is indicative of an estimated crop yield. The collection that is accessed, for example, includes at least a first set of data sensed by the first agricultural machine and a second set of data sensed by the second agricultural machine. In addition, the first and second sets of data can be scaled based on a yield correction factor. A bias between the scaled first set of data and the scaled second set of data is determined, and a smoothing operation is performed on the scaled first and second sets of data. For example, performing the smoothing operation can include generating a calibration correction factor based on the determined bias, removing the bias between the scaled first set of data and the scaled second set of data to obtain a corrected set of crop yield data, and using the calibration correction factor in sensing the first set of data on the first agricultural machine and the second set of data on the second agricultural machine.

DETAILED DESCRIPTION

Agricultural machines may use systems to capture information indicative of an agronomic parameter. Agronomic parameters may include a measurable form of information that relates to the properties of the agricultural operation being performed. For instance, machines may monitor agronomic parameters such as the amount of crop that is harvested, the amount of crop planted or a depth at which planting occurs, the depth of a soil tillage, or the amount or type of nutrients supplied, among others. As such, the term “agronomic parameter” used herein may refer to any measurable form of information such as, but not limited to, crop yield, planting depth, soil moisture level, and crop nutrient properties, among others.

As an example, agricultural combines harvest crops in a field. While harvesting, the combine may use a sensing system that includes a grain yield monitor, which measures a mass flow of crop with an impact-based mass flow sensor or another type of sensor. As such, the sensing system monitors the amount of crop that is being harvested from the field relative to the location at which it was harvested. For purposes of discussion, crop yield generally refers to the yield of crop per unit area of land that is cultivated and is typically measured in tons per hectare (t/ha) or bushels per acre (bu/ac). It is important that crop yield is accurate, as this information can provide valuable insights as to how specific areas of land performed with respect to a certain crop. In addition, crop yield can provide insights into the performance of the agricultural machine and the sensing system.

However, there may be inaccuracies in the measurements of mass flow, and thus the actual amount of crop that is harvested will differ from the sensed amount. It is also noted that similar measurement inaccuracies may occur in other sensing systems that obtain information indicative of any of the agronomic parameters mentioned above, or others.

In an attempt to ensure that sensed measurements are accurate, sensing systems may be calibrated. In typical systems, a calibration is used to adjust a sensed value based on a determined difference between the sensed value and a value that is known to be accurate (or more accurate).

In order to calibrate sensing systems such as a grain yield monitoring system, a series of manual steps is performed by an operator. Operators are often required to start and stop harvesting to manually record accurate measurements for comparison to the sensed measurements. Upon stopping a harvesting operation, an operator may initiate a calibration sequence and briefly continue to harvest a portion of the field in order to obtain sensor information that is indicative of an estimated weight of the harvested crop. Then, the operator moves the harvested crop to an accurate scale to obtain a ground truth weight. The operator then manually enters the ground truth weight, such as a calculated weight, into the calibration system and the system uses the weight to determine a deviation from the sensed crop yield to the actual crop yield. This provides a calibration deviation for a single load. Multiple deviations for multiple loads are often required to obtain an average deviation. The deviation can be used to calibrate the system.

However, this method of calibrating a sensing system may have deficiencies. For one, it can be time consuming. It generally requires the operator to postpone harvesting, and to perform multiple calibration field passes (to obtain multiple loads). In addition, this particular method can result in data inaccuracy. For example, inaccuracies may arise from the operator manually entering crop yield information, using a variety of different machines (e.g. multiple combines harvesting in the same field), variations in an accurate measurement device (e.g. a scale), and a variety of other factors.

Further, typical calibration systems do not allow a calibration adjustment to be applied to previously obtained (e.g. historical) data. This can result in calibration induced offsets in a single machine. That is, these will be an offset in the data values collected pre-calibration (the historical data) and those values collected post-calibration. There may also be calibration induced offsets between multiple machines. For instance, it may be beneficial for an operator to use several machines in a single field as this can decrease the amount of time that it takes to harvest a crop. However, as a calibration is performed on each individual machine, there may be variances between the total estimated crop yield amongst all machines as the sensors on each machine may vary from one machine to the other. Typical calibration operations may not apply the calibration adjustment to other machines.

Thus, there is a need for a system that accurately calibrates sensing systems and automatically corrects any post-calibration deficiencies. It will be noted that some examples of the present disclosure include a system that reduces the amount of manual steps involved in performing a calibration, decreases the operator time that is required to perform a calibration sequence, reduces calibration-induced offsets within a single machine, reduces machine-to-machine calibration bias, and provides more accurate sensed data, which leads to more accurate agronomic maps, improved agronomic decision making, and improved operational performance.

FIG. 1is a block diagram of one example of an agricultural machine100. Agricultural machine100illustratively includes one or more processors102, memory104, communication system105, user interface108, control system110, controllable subsystems112, data store114, and other components116.FIG. 1also shows that, in one example, machine100can include an operational parameter monitoring system118, a calibration system132, a calibration correction system138, and a data visualization system148.

In one example, user interface108includes operator input mechanisms and output mechanisms. The output mechanisms can be mechanisms that convey information to operator166, such as visual display devices, audio devices, haptic feedback devices, etc. In one example, user interface108interacts with data visualization system148to produce a variety of output mechanisms that are indicative of monitored operations, which will be discussed in further detail below. The operator input mechanisms can include a wide variety of different mechanisms that can be actuated by operator166to control and manipulate various systems and subsystems (e.g. controllable subsystems112) of agricultural machine100. The operator input mechanisms, for instance, can include levers, steering wheels, pedals, joysticks, buttons, keypads, touch sensitive display devices, and user input mechanisms on user interface displays, among a wide variety of other input mechanisms.

Control system110may receive sensor signals from sensors122and generate control signals to control the various controllable subsystems112. It is shown inFIG. 1that sensors122are included in operational parameter monitoring system118. It is also noted that sensors122may be included in the general architecture of agricultural machine100, and are therefore not limited to sensing signals indicative of operational parameters. Controllable subsystems112can include a wide variety of mechanical and computer implemented systems of agricultural machine100that relate to the movement of the machine, the agricultural operation that is performed, and other controllable features. Some examples are described below.

Operational parameter monitoring system118illustratively identifies an operational parameter associated with each of the sensor signals that is received from sensors122and provides that information to control system110, so that control system110can accommodate various levels of signal variability obtained by sensors122. Sensors122can include sensors that are configured to determine operational parameters such as grain mass flow, soil moisture, planting depth, tillage depth, among a variety of others. Sensors122may also include a variety of other sensors such as a machine state sensor. It is illustratively shown that operational parameter monitoring system118also includes geospatial system120. Geospatial system120includes at least one of a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. In one example, geospatial system120is configured to associate signals obtained by sensors122with a geographical location, such as a location within a field. As such, a variety of different spatial parameter data may be obtained by sensors122, geospatial system120, and identified by system118. Embodiments described herein may also be configured to perform loss sensing, such as grain loss sensing.

Operational parameter monitoring system118further illustratively includes yield monitoring logic124. Yield monitoring logic124is configured to use sensors122, such as grain mass flow sensors, and geospatial system120, to estimate a crop yield at various locations in an operation environment (e.g. a field with a planted crop that is harvested). As such, yield monitoring logic124may use information obtained by a sensing system to estimate a yield density for a harvesting session. Operational parameter monitoring system118may also include moisture monitoring logic126, and planting depth monitoring logic128, among other monitoring logic130. Moisture monitoring logic126may use soil moisture sensors (e.g. sensors122) to estimate a moisture content of soil at various locations in the operation environment. Similarly, planting depth logic128may use a planting implement depth sensor (e.g. sensors122) to estimate an average planting depth at various locations in the operation environment. Other monitoring logic130may be configured to monitor any other agronomic parameters discussed herein and use the sensed parameter information in association with the geospatial information.

It is also noted that the information obtained by operational parameter monitoring system118, along with the other components of machine100, may be stored in a variety of locations including, but not limited to, memory104and/or data store114. Further, agricultural machine100may be in communication with one or more agricultural machines162and164, and/or remote systems160over network158. Network158may be any of a wide area network (WAN), local area network (LAN), or a wireless local area network (WLAN), among others.

Agricultural machines162and164may perform the same or a similar operation as that being performed by agricultural machine100. For instance, agricultural machines100,162, and164may include combines that are performing a harvesting operation in different spatial regions of the same field. As such, it may be beneficial to utilize information from each machine in determining accurate measurements and correcting a single machine bias or multi-machine bias. Remote systems160may include any other systems relevant to an agricultural operation that is being performed or an agronomic parameter that is being monitored. For instance, remote system160includes a remote agricultural management system, other agricultural machines, and an imaging system such as an aerial imaging drone, among others.

In the example shown inFIG. 1, agricultural machine100also illustratively includes calibration system132. Calibration system132includes a manual calibration system134and a sensor interface136. As briefly discussed above, at some point during the operation of agricultural machine100, operator166may initiate a calibration sequence. For example, operator166can actuate a calibration input mechanism that is generated by user interface108. In response to receiving actuation of the calibration input mechanism, manual calibration component134initiates a calibration sequence. In one example, performing a calibration sequence with manual calibration component134includes obtaining a sample indicative of an operational parameter and comparing that sample to a calculated value of the operational parameter. For instance, when harvesting a crop in a field, operator166may initiate a manual calibration sequence with manual calibration component134to compare an estimated crop yield to an actual crop yield, which may be obtained by weighing the sample with an accurate scale. Calibration component134then determines a calibration adjustment, based on the comparison, and provides that adjustment to sensor interface136. Sensor interface136may instruct a variety of sensors122to adjust a signal output that is provided by the sensors. It is also noted that calibration system132may be configured to perform a calibration adjustment automatically, such as in response to a predetermined calibration time, a specific indication provided by operational parameter monitoring system118, or another indication. By automatically it is meant, in one example, that it is performed without further operator input, except perhaps to authorize or initiate the calibration adjustment.

FIG. 1Aalso illustratively shows that agricultural machine100includes data visualization system148. Data visualization system148includes a field map generator150, a corrected metric output152, a calibration correction summary154, and other data views156. It may be beneficial for operator166to be provided with a visualization of the performance of operational parameter monitoring system118. For example, field map generator150may be configured to generate one or more field maps that include a plot of the measured parameters during operation. In one example, field map generator150is configured to generate a crop yield plot that is displayed with user interface108. Prior to discussing the other various features of data visualization system148, calibration correction system138will now be discussed in further detail with respect toFIG. 1B

FIG. 1Bis a block diagram showing one example of a calibration correction system138in an agricultural machine architecture. Calibration correction system138illustratively includes a data aggregator140, pre-processing logic142, bias correction logic144, and other calibration logic146. It is shown inFIG. 1Bthat data aggregator140may receive a variety of information relating to the operation of agricultural machine100from operational parameter monitoring system118and/or data store114. It is also noted that data aggregator140may be configured to crawl data store114and parse one or more data sets for use with calibration correction system138. For example, in an embodiment where operation parameter monitoring system118monitors a crop yield during harvesting operation (e.g., by using yield monitoring logic124), data aggregator140may receive or obtain data that is indicative of the crop yield at various locations within a harvesting operation, where those locations are provided by geospatial system120. Of course, it is also noted that data aggregator140may obtain any of the data related to moisture monitoring logic126, planting depth logic128, other monitoring logic130, and any other information that is obtained by geospatial system120and sensors122. Data aggregator140may then provide the obtained sensor signal information to pre-processing logic142.

Pre-processing logic142is configured to prepare the obtained data for use with bias correction logic144. At some point, it may be beneficial for the data that is aggregated to be filtered based on one or more filter criteria. The filter criteria may be defined by one or more pieces of filter logic. As such, pre-processing logic142includes a calibration data filter168. Calibration data filter168illustratively includes path redundancy logic170. Path redundancy logic170may filter the parameter data obtained by data aggregator140by removing any sensor signals from redundant geospatial locations. For example, during harvesting, a combine or agricultural machine100may perform a single pass across a field to harvest a crop in a row. In some instances, an operator may be required to perform an additional pass over that same row to harvest any crops that were missed on the first pass. Similarly, an operator may be required to pass over an already harvested row to reach a desired location within the field (e.g., the operator is not harvesting when re-passing over the already harvested row). The information from the subsequent pass can be filtered out.

In addition, or alternatively, pre-processing logic may perform a fusion operation that combines obtained information. For instance, calibration data filter168may determine similar sets of information and combine those similar sets to generate a fused set of obtained information.

Parameter data may also be filtered based on a machine state that is determined by machine state logic172. Machine states can be indicative of the particular condition of a machine during an operation. For example, but not by limitation, machine states can include any of the following; an idle state, an idle to unload state, a field transport state, a road transport state, a harvesting state, a harvesting while unloading state, and headland turn state, among others. Some of the parameter data that is obtained by sensors122may not be relevant if the data is obtained during a certain machine state. For example, when an operator performs a headland turn, which is a turn at the end of a row pass to position the machine for a next pass on the adjacent row, a crop yield sensor (such as a grain mass flow sensor) may continue to obtain data during the turn. However, the data obtained during the turn may not include an indication of a crop being harvested as machine100may temporarily leave the harvestable area of the field. In addition, or alternatively, parameter data may be filtered based on an identified delay correction, such as a delay correction that is applied due to a change in machine speed that is inconsistent with the rate at which sensor information is captured. Any delay correction that is performed may be identified as a delay period that occurred while capturing sensor information. Machine state logic172may, accordingly, identify a set of spatial information that was obtained during a correction period. As such, machine state logic172may determine irrelevant parameter data based in part on an identified machine state, and remove said irrelevant data.

It is also contemplated that a variety of other filter logic174may be applied to filter the relevant parameter data in accordance with calibration data filter168. For instance, other filter logic174may determine data points that are determined to be statistical outliers within a set.

In addition to filtering the obtained parameter data, pre-processing logic142may prepare the obtained parameter data in a variety of other ways. It is illustratively shown inFIG. 1Bthat pre-processing logic142includes parameter scaling logic176. In one example, parameter scaling logic176is configured to scale the obtained parameter data based on a parameter correction factor. To scale the parameter data, logic176determines a statistical offset in a set of obtained parameter data. For instance, logic176determines a statistically significant standard deviation in a set of obtained parameter data. This may be a statistically significant (e.g. not due to sampling error alone, and rather a characteristic of the whole set) standard deviation between sensed crop yields (e.g., mass flow, density) from a variety of locations within a sampling region. In addition, or alternatively, determining a statistical offset may include determining a standard deviation of the mean, an upper limit of the sensed parameter data, etc. Based on the degree of statistical offset, parameter scaling logic176may determine a parameter correction factor and scale the data accordingly.

In the example of sensing crop yield as an agronomic parameter, parameter scaling logic176recalculates a yield from a sensed mass flow, machine speed, and header width (e.g. a component of controllable subsystems112) to remove a yield upper limit. Upon removing a yield upper limit, the yield values may be scaled by parameter scaling logic176based on a determined yield correction factor. Once the yield correction factor is determined and the yield values are scaled, logic176may then calculate a new yield upper limit and truncate the determined yield values above the new yield upper limit. As such, scaling logic176may include a mechanism for determining a more accurate operational parameter by scaling the estimated parameters in relation to one another.

In one example, once the obtained parameter data is prepared by pre-processing logic142, the prepared data is provided to bias correction logic144to reduce a bias determined between the prepared data. It is shown inFIG. 1Bthat bias correction logic144includes smoothing logic178, a multi-machine factor logic180, artifact removal logic182, and data verification logic184.

When multiple machines are harvesting in the same field, there may be a sensing system bias that occurs from machine to machine. In addition, when there is a single machine harvesting in a field, there may be a sensing system bias between the sensors on that machine. Conventional systems may exhibit a sensing system bias, either internally (single machine) or externally (multiple machines) that reflects a 10-50% difference between sets of sensed operational parameters. It is desirable to have a system that accounts for these determined offsets and provides accurate operational parameter outputs from each machine that is operating.

In one example, bias correction logic144provides a post-calibration algorithm that statistically calculates bias from machine to machine, or from sensor-to-sensor on an individual machine, to reduce the bias and output accurate parameter data. Operational parameter monitoring system118may obtain multiple sets of parameter data. In an operation where a single machine is used, each set of data is indicative of, for example, a set of parameter data that is obtained in a determined spatial region of the field. For instance, a first set corresponds to data obtained during a first harvesting pass (e.g. harvesting a first set of rows), while a second set corresponds to data obtained during a second harvesting pass (e.g. harvesting a second set of rows). Where multiple machines are used, each set of data that is obtained may be obtained by a different agricultural machine. For instance, a first set may correspond to data obtained by agricultural machine100, while a second set corresponds to data obtained by agricultural machine162, and a third set corresponds to data obtained by agricultural machine164, etc. Each of the individual sets, regardless of whether one or more machines are used, may include parameter data from a localized region of the field. The sets may be compared to identify a bias between the sensing systems or individual sensors on a single machine, and therefore eliminate bias that occurs during measurement of those local regions.

In one example, bias correction logic144uses the scaled data sets, as scaled by parameter scaling logic176. As such, the data sets have already been pre-processed to remove any identified statistical offsets within the sets. Bias correction logic144is then configured to analyze the sets with respect to one another, and thus determine a bias between the sets.

A variety of algorithms may be applied to the scaled parameter data with bias correction logic144, and more specifically with smoothing logic178. For example, smoothing logic178may apply a generalized additive model to smooth the parameter data in accordance with the determined latitude and longitude from geospatial system120. Alternatively, but not by limitation, smoothing logic178may include a localized regression model that is executed to reduce a sensing system bias. In one example, smoothing logic178adjusts the yield value for each combine so that the average yield equals the original overall weighted average yield. It is also noted that smoothing logic178may be configured to perform a localized smoothing. For example, yield parameter data may be obtained for 1% of a total region of a field. Smoothing logic178will recognize that only a specific region of the field has been harvested and execute a smoothing algorithm on the data indicative of the 1% region. Upon executing the smoothing, a localized smoothing metric may be applied to data obtained from other localized regions within the field. As such, a result of performing smoothing on only a portion of the field can be used to smooth the remainder of the field data.

Multi-machine factor logic180is configured to determine a weighted offset value between various machines that are operating and obtaining operational parameters. For instance, it may be determined by multi-machine factor logic180that between each machine operating in a field, there is a sensing system bias of approximately 3% from machine-to-machine. As such, multi-machine factor logic180will determine a yield span across the machines and a deviation from one machine to another. The yield span and deviation may be used to generate a multi-machine factor that is applied with smoothing logic178.

It is also noted that even when sensors (across a single machine or between multiple machines) have a relatively low bias (e.g., less than +/−2%), this does not guarantee that accurate parameter data will be determined by bias correction logic144. For instance, there may be transient characteristics within the data. It may be beneficial to identify and remove these transient characteristics to provide a more accurate parameter output. For example, but not by limitation, when two combines (e.g. machines100and164) are harvesting in a single field, each combine may change a forward speed depending on a machine state. The combines may need to slow down to perform a headland turn, move over rough terrain, pass over a slope or a particularly dense portion of the crop in the field, etc. When the forward speed of a combine changes while obtaining parameter data, there is generally a delay correction that is performed. For instance, there may be a delay of approximately 10 seconds between when geospatial data is obtained and when parameter data is obtained. As such, as speed is changed, the delay correction may be inconsistent until a consistent speed is reached. Of course, it is contemplated herein that delay correction can be identified and applied to a variety of channels in addition to a sensed change in speed. Regardless of the sensed channel, the need to correct delay may manifest as a need to attribute the sensed data (e.g. mass flow) to a corresponding geolocation. In conventional systems, these and other delay corrections may show up as inaccurate data parameter outputs when viewing a field map. Specifically, in some maps, these inconsistent delays may be visualized as specks or another visual inaccuracy on a field map. Artifact removal logic182is configured to identify and remove such artifacts that are indicative of outliers in the sensed operational parameter data.

Bias correction logic144also illustratively includes data verification component184. In one example, data verification component184is configured to incorporate data that is provided by direct observation to further reduce the calibration bias. One example of a source of data that would be provided with a verification component184includes an indication of a ground truth. Ground truth may generally refer to an actual observed value of any of the parameters obtained by operational parameter monitoring system118.

An example of ground truth for yield parameter data includes an actual measured weight of a sample of a harvested load. For instance, when harvesting a crop, the combine may place the harvested grain into a cart with one or more measurement scales (e.g. sensors122). In another example, the agricultural machine100may be driven over a scale. The scales may obtain an indication of measured weight and verification logic184will use the indication in determining a calibration adjustment with logic178. Two particular scenarios will now briefly be discussed. In one scenario, each combine obtains an accurate weight to determine the amount of grain that was harvested by that harvester. Data verification logic184then calculates a sum of the weights across all machines. Logic184may then compare the sum to the estimated mass flow as provided by yield monitoring logic124from each machine. In a second scenario, a single combine is used, and thus the single machine may obtain a final accurate weight measurement when all the field is harvested, or multiple accurate weight measurements that correspond to specific regions of the field. The measurement(s) will be reconciled by data verification component184and provided to smoothing logic178for reducing the calibration bias. It is noted that a variety of parameters in addition to crop yield may be utilized with data verification component184.

Before describing these scenarios, it will be noted thatFIG. 1Billustratively shows that bias correction logic144may provide determined bias reduction information to calibration system132. For instance, bias correction logic144provides a bias correction factor to sensor interface136, which uses the correction factor to adjust sensor signals generated by sensors122.FIG. 1Balso shows that calibration system132may provide an indication of the bias correction factor to operational parameter monitoring system118. System118may then use the correction factor with any of the monitoring logic discussed herein to reduce data inaccuracies during operational monitoring. Further, it is noted that the determined calibration correction factor may be applied to previously obtained operational parameter data, which may be stored at data store114. This allows agricultural machine100to adjust prior yield, moisture, planting depth, and other parameters data to reflect the newly accurate calibration scale.

FIG. 2illustrates one example of a method200illustrating the operation of the calibration correction system with multiple agricultural machines. More specifically, method200may include a method of reducing a sensing system bias across a first agricultural machine and a second agricultural machine.

As similarly noted above, there may be several agricultural machines performing the same or similar operations in a field at the same or different times. Calibration of the sensors on each machine may initially occur at the time of manufacture. In addition, an operator166may perform manual calibrations with manual calibration component134. Thus, as agricultural machine100performs an operation and operational parameter data is obtained with monitoring system118, data is unique for each machine that is operating, and there is currently no way to reconcile that data across the machines to provide a consistent output that is representative of accurate parameter monitoring.

A calibration correction system, according to embodiments described herein, may operate in substantially real-time. It is noted that the term “real-time” used herein generally refers to operations that occur concurrently or with respect to one another without a great deviation between a time at which those operations are performed. The processing time does not negate the ability of the system to operate in real-time. As such, multiple machines may operate in the same field at the same time so sensor information and calibration correction is performed by the systems in substantially real-time.

At block202, it is illustratively shown that multiple machines collect or have collected operational parameter data. Each machine may collect a variety of data that is included but not limited to machine identification, geospatial data, and parameter data. Multiple machines collecting machine identification information is generally indicated at block216. Each of agricultural machines100may include a machine identifier stored at data store114and provided to remote systems116via communication system108and over network158. In addition, as an operation is being performed, geospatial system120may obtain information such as, but not limited to, latitude and longitude at a variety of time points that are indicative of when the operation occurs. Collecting geospatial data with multiple machines is generally indicated at block218. Depending on the operation that is being performed (such as a harvesting operation, planting operation, tillage operation, or nutrient monitoring operation, among others) each machine will collect parameter data that is indicative of the operation. Collecting parameter data is generally indicated at block220inFIG. 2. It is also noted that collecting parameter data, in accordance with block202, may specifically include accessing a collection of previously-collected agronomic data from data store114or another storage structure. Calibration correction system138may access a collection of data that includes a first set of data that is sensed by a first agricultural machine and a second set of data sensed by the second agricultural machine.

At block204, it is illustratively shown that the method includes aggregating the multi-machine operational parameter data. Data aggregator114may aggregate the obtained data from operational parameter monitoring system118and/or data store114in preparation for performing data pre-processing. Aggregating the multi-machine parameter data may include aggregating individual data points into one or more sets, where the sets are defined by the machine that obtained the data, the location at which the data was obtained, the time at which the data was obtained, or other criteria. For instance, the first set of data is sensed by the first agricultural machine during a harvesting operation that is performed relative to a first portion of a harvesting environment, and the second set of data is sensed by the second agricultural machine relative to a second portion of the harvesting environment.

At block206, the method includes performing data pre-processing. Performing data pre-processing can include executing any of the features discussed with respect to pre-processing logic142inFIG. 1B. For example, pre-processing logic142may determine a correction factor between the aggregated set of data based on, for example, a statistical deviation. Further, performing pre-processing may include filtering the obtained data, as indicated at block224. Filtering the obtained data may include executing path redundancy logic170, machine state logic172, or other filter logic174, as provided by calibration data filter168. Generally, filtering the obtained data provides a mechanism for removing outliers and irrelevant data sources that will not be beneficial in generating an accurate parameter output.

Performing data pre-processing may also include scaling the obtained data, which is generally indicated by block226. In one example, scaling the obtained data includes scaling each of the first and second sets, individually, based on a determined correction factor corresponding to each of the sets. For instance, a first offset is determined for the data in the first set and a second offset is determined for the data in the second set, and the parameter correction factor is partially based on the determined first and second offsets. Further, the first offset may be indicative of a first average deviation in the first set, wherein the first average deviation is greater than a minimum deviation threshold that is used to perform the scaling. Similarly, the second offset may be indicative of a second average deviation in the second set, wherein the second average deviation is greater than a minimum deviation threshold that is used to perform the scaling.

At block208of method200, it is illustratively shown that the method includes determining that there is a bias between the sensing systems of the multiple machines. In one example, determining there is a bias includes analyzing a deviation between the average parameter output between each machine. For example, calibration correction system138determines there is a significant bias between an average deviation of the scaled first set and an average deviation of the scaled second set. If the bias is above a threshold, calibration correction system138may determine that there is a significant bias between the machines and that a calibration correction is to be used.

Block210of method200includes performing a bias correction to smooth the operational parameter data. In one example, bias correction logic144executes one or more smoothing functions, one of which can include generalized additive smoothing, as indicated at block228. Executing a generalized additive smoothing with smoothing logic178may include adjusting the obtained operational parameter values (e.g. in the scaled first and second sets) so that the average parameter value equals the original overall weighted average parameter value. In addition, performing a bias correction can include applying a multi-machine factor with multi-machine factor logic180, which is generally indicated at block232. The multi-machine factor230can include a weighted offset that is determined by multi-machine factor logic180, which generally indicates an offset from the overall average between each agricultural machine. Further, performing a bias correction may include removing artifacts, as indicated at block232. For example, artifact removal logic182removes transient characteristics in the obtained operational parameter data such as, but not limited to, inconsistencies that result from change of speed delay corrections, data outliers, among others. Performing a bias correction also illustratively includes verifying the data correction, as indicated at block234. Verifying the data correction may include utilizing data verification component184to compare the estimated data correction to a known actual value of the operational parameter. For instance, the ground truth can be determined for a crop yield and that ground truth can be utilized to determine an additional correction offset between an actual crop mass that is harvested and the estimated crop mass.

At block212, method200illustratively includes applying a bias correction to all the relevant machines collecting the operational parameter data. For example, applying the bias correction may include providing a calibration offset adjustment with bias correction logic144to calibration system132. As such, the sensors122, themselves, can be calibrated or block212may include calibration system132instructing sensor interface136to adjust the sensor signals provided by sensors122so any future operational parameter data that is obtained is consistent across all machines, relative to one another.

At block214, method200includes outputting the corrected operational parameter data. Corrected parameter data may be output in a variety of ways. In one example, outputting the corrected parameter data includes providing a corrected field map with field map generator130, as indicated at block236. A corrected field map will generally provide a smooth visualization of the output parameter (e.g., crop yield) based on the location at which the operation was performed. This may provide a real-time view for operator166that indicates how the machine is performing during operation. Corrected field maps will generally be discussed in further detail below with respect toFIGS. 5A and 5B. In addition, or alternatively, outputting the corrected parameter data may include outputting an infographic, as indicated by block238. Data visualization system148may also generate other data views156that may include charts, graphs, maps, and a variety of infographic material.

Further, calibration correction system138may be configured to output corrected metrics, as indicated at block240. The corrected metrics are indicative of the obtained parameter data as adjusted by bias correction logic144. For instance, in the example of harvesting with a combine, corrected metrics240include an average yield compared to a percent deviation for each of the machines, and also a corrected yield compared to a deviation across an average of all of the machines. Further, outputting corrected operation parameter data can include outputting a calibration correction summary, as indicated at block242. In one example, the calibration correction summary includes a visual output that compares the original parameter outputs to the corrected parameter outputs and the weighted offset that will be applied to future and/or historical parameter data. Metrics may also include an adjusted mass flow (kilograms per second) and an adjusted productivity (e.g. crop productivity in tons per hour), a combine average yield and a percent deviation from an overall average, along with a span that includes a calculated max percent standard deviation relative to a minimum percent standard deviation, among others. These are examples only.

FIG. 3is a flow diagram illustrating one example of the operation of calibration correction system138with a single agricultural machine. In one example, method300includes a method of reducing a sensing system bias in an agricultural machine.

As similarly discussed above with respect to method200, a calibration correction system and the associated systems described herein may operate in substantially real-time with respect to a single machine. For one, a standalone, single machine may perform the method of reducing sensing system bias as a direct onboard operation. Second, but not by limitation, a single machine may use a calibration correction system where that machine is processing historical spatial information in combination with information from the single machine.

At block302, it is illustratively shown that method300includes collecting or accessing a collection of operational parameter data. Collecting operational parameter data may include accessing geospatial data, as indicated at block318, historical operational data, as indicated at block320, and/or a current parameter data, as indicated at block322.

Upon collecting or accessing the data, method300proceeds to aggregate the data that is associated with that single machine. This is generally indicated at block304. As similarly discussed above with respect toFIG. 2, data aggregator140may aggregate the parameter data from operational parameter monitoring system118and/or data store114. When a single machine is operating, aggregating data may include aggregating the data into one or more sets. For instance, each set may include data obtained during a specific operation timeframe, from a specific region in a field, etc. In one example, a first set includes agronomic data that is sensed during a first pass of the agricultural machine in an operation environment, and a second set includes agronomic data that is sensed during a second pass of the agricultural machine in an operation environment. The first set may refer to data obtained during a current operating session, while the second set may refer to data obtained during an operating session that was performed at some previous time. These are examples only.

Data pre-processing may be performed on the obtained (and aggregated) parameter data, as indicated at block306. It is noted that performing data pre-processing may include any of the pre-processing features discussed with respect toFIG. 2(method200, block206) and preprocessing logic142ofFIG. 1. As shown inFIG. 3, performing pre-processing includes determining a correction factor, as indicated at block324, filtering the obtained data, as indicated at block326, and scaling the obtained data, as indicated at block328. It is also noted that scaling the obtained data in accordance with block328can include using parameter scaling logic178to analyze a localized region of data for a specific field, and applying the localized pre-processing to a remainder of the field data. In one example, data scaling includes adjusting each of the first and second sets, individually, based on a determined correction factor corresponding to each of the sets. The parameter correction factor may be determined by a statistical deviation between the data contained within each of the sets.

At block308, method300includes determining a bias between the first and second sets, such as between current and historical operational parameter data that is obtained with the single machine. Determining a bias may include calibration correction system138determining there is a bias between an average deviation of the scaled first set and an average deviation of the scaled second set. Of course, a bias may be determined in a variety of other ways as well.

At block310, the method includes performing a bias correction to smooth the scaled first and second sets from the machine, such as the current and historical operational sets. The bias correction that is performed with respect toFIG. 2and multiple machines that are performing an operation may be also applied to method300for a single machine. As such, block310illustratively includes executing generalized additive smoothing with smoothing logic178. In addition, or alternatively, smoothing logic178may execute local regression smoothing, as indicated at block332. Utilizing local regression smoothing may generally not include using a combine factor because the regression only requires information from a single machine. One example of a smoothing regression that may be applied, using smoothing logic178, to information obtained with a single machine, includes a Gaussian process regression. In addition, or alternatively, smoothing logic178may include a generalized linear model that may be applied to either single machines or multiple machine instances. Of course, it is noted that a variety of different geospatial smoothing operations for several data sources with source specific correction that may be applied. Performing the smoothing operation may include generating a calibration correction factor, and applying that calibration factor to the adjusted (e.g. scaled) first and second sets of data obtained by the single machine.

Performing the bias correction may also include removing artifacts, as indicated at block334, using artifact removal logic182to identify data outliers and transient characteristics in the parameter data. In addition, verifying a data correction is indicated at block336, which may include using data verification component184to compare a ground truth (such as an actual yield as determined by a measurement with an accurate scale) to the scaled sets of parameter data.

At block314, a bias correction is applied to all the relevant historical parameter data for the machine. Applying the bias correction to historical data may smooth all of the parameter data obtained by the single machine for a specific operation. This generates operational parameters that are accurate for the entire operation period.

At block316, the corrected operational parameter data is output which may include any of: outputting a corrected field map and infographic, as indicated at blocks338and340, respectively; corrected metrics, as indicated at block342; and/or a calibration correction summary, as indicated at block344; or any of the other outputs discussed with respect toFIG. 2(method200, block214) and data visualization system148.

Prior to describing the operational control insights that are provided by calibration correction system138, one example of an agricultural machine100will first be described.FIG. 4is a partial pictorial partial schematic illustration of machine100, in an example where machine100is a combine400. It can be seen inFIG. 4that combine400illustratively includes an operator compartment458, and a set of front end equipment that includes a header402, and a cutter generally indicated at404. Combine400can also include a feeder house406, a feed accelerator408and a thresher generally indicated at410. Thresher410illustratively includes a threshing rotor412and a set of concaves414. Further, combine400can include a separator416that includes a separator rotor. Combine400can include a cleaning subsystem (or cleaning shoe)418that, itself, can include a cleaning fan420, chaffer422, and sieve424. The material handling system in combine400can also include discharge beater426, tailings elevator428, clean grain elevator430as well as unloading auger434and spout436. Combine400can further include a residue subsystem438that can include chopper440and spreader442. Combine400can also have a propulsion subsystem that includes an engine that drives ground engaging wheels444or tracks, etc. It will be noted that combine400may also have more than one of any of the subsystems mentioned above.

In operation, and by way of illustration only, combine400illustratively moves through a field and in a direction generally indicated by arrow446. As it moves, header402engages the crop that is harvested and gathers the crop towards cutter404. Once the crop is cut, the crop is moved by a conveyor in feeder house406toward feed accelerator408which accelerates the crop into thresher410. The crop is threshed by rotor412rotating the crop against concave414. The threshed crop is moved by a separated rotor and separator416where some of the residue is moved by discharge beater426towards the residue subsystems438. It can be chopped by residue chopper440and spread on the field by spreader442. In other implementations the residue simply dropped in a windrow instead of being chopped and spread.

Grain falls to cleaning shoe (or cleaning subsystem)418. Chaffer422separates some of the larger material from the grain and sieve424separates some of the finer material from the clean grain. Clean grain falls to an auger in clean grain elevator430, which moves the clean grain upward and deposits it into clean grain tank432. Residue can be moved from the cleaning shoe418by airflow generated by cleaning fan420. That residue can be moved rearwardly in combine400toward the residue handling subsystem438.

FIG. 4also illustratively shows that, in one example, combine400can include a ground speed sensor446, one or more separator loss sensors448, a clean grain camera450, and one or more cleaning shoe loss sensors452. Ground speed sensor446illustratively senses the travel speed of combine400over the ground as the combine moves in a direction generally indicated by arrow446. Sensing the ground speed can be done by sensing the speed of the rotation of the wheels, drive shaft, the axel, or other components. The travel speed may also by a positioning system, such as geospatial system120, generally represented by geospatial sensor460inFIG. 4.

Cleaning shoe loss sensors452illustratively provide an output signal indicative of the quantity of grain loss by both the right and left sides of the cleaning shoe418. In one example, sensors452are strike sensors which count grain strikes per unit of time (or per unit of distance traveled) to provide an indication of the cleaning shoe grain loss. The strike sensors for the right and left sides of the cleaning shoe can provide individual signals, or a combined or aggregated signal. It will be noted that sensors452can comprise only a single sensor as well, instead of separate sensors for each shoe.

Separator loss sensors448provides a signal indicative of grain loss in the left and right separators. The sensors associated with the left and right separators can provide separate grain loss signals or a combined or aggregate signal. As can be done using a wide variety of different types of sensors as well, it will be noted that separator loss sensors448may also comprise only a single sensor, instead of separate left and right sensors.

It will also be noted that the sensors described with respect to combine400(in addition to the sensors already described with respect to machine100) can include other sensors as well. For instance, it may include a moisture sensor that is configured to sense the moisture level of the material that is passing through combine400, and/or sense the moisture level of the soil that combine400passes over during operation. Combine400may also include a machine state sensor462that is configured to sense whether combine400is configured to drop the residue, drop a windrow, or perform another machine operation. They can also include cleaning shoe fan speed sensors that can be configured proximate fan420to sense the speed of the fan. They can include machine setting sensors that are configured to sense the various configurable settings on combine400. They can also include a machine orientation sensor that can be any of a wide variety of different types of sensors that sense the orientation of combine400. For instance, the sensed orientation may identify the orientation of the combine400, the position of parts of combine400relative to other parts, or the position of the parts relative to the ground, etc. Another example of machine orientation sensors includes a sensor that senses the height of a header402above the ground. Further, crop property sensors can sense a variety of different crop properties such as crop type, crop moisture, and other crop properties. Other crop properties may include different grain constituents such as, but not by limitation, oil, starch, and protein properties. More particularly, the crop property sensors may sense characteristics of the crop as they are being processed by machine400, for example as the crop is being passed through a grain elevator430. One particular example of a crop property sensor includes a mass flow rate sensor464that senses the mass flow rate of a crop through elevator430, or provides other output signals indicative of similar variables.

As such, the size and number of sensors used with the systems described herein may vary. In addition, or alternatively, inferred measurements may be obtained from virtual sensors. In one example, virtual sensors include combinations and series of combinations of communication between related inputs, commands, and actual sensors that, together, provide said inferred measurements.

FIG. 5Ashows a pictorial view of a field plot representing a measured agronomic parameter such as crop yield. The field plot500generally depicts an original set of parameter data obtained with an agricultural machine, where that data is not pre-processed or corrected in accordance with calibration correction system138. For instance, field map plot500represents a crop yield with respect to a location in the field as determined by a mass flow sensor. More specifically, but not by limitation, operational parameter monitoring system118may utilize a mass flow sensor122and geospatial system120in accordance with yield monitoring logic124to obtain a relational set of data that is provided by field map generator150in the form generally shown by field map plot500. It can be seen that field map plot500depicts data with a high degree of variance between the sensed crop yield throughout the field. Specifically, there are individual rows that vary quite significantly when compared to a neighboring row. For instance, each row may correspond to a header width, and sensor information from sensors across the header may vary. However, it is also noted that variances in the original parameter data may manifest in the map for specific plant rows or even a single plant, where information for that plants is sensed either individually or as a set of plants. This map likely reflects data inaccuracies as crop yield is unlikely to vary this much between neighboring rows. In one example, sensed high density areas (sensed high crop yield) are generally colored red, while sensed medium density areas are generally colored yellow, and sensed low density areas are generally colored blue.

To further illustrate, it can be generally seen near portions502and504that a high crop yield was originally sensed with monitoring system118. To the contrary, it is generally shown in portions506and508that a relatively low crop yield was sensed by system118. A sensed medium crop yield was generally sensed at the area represented by reference numeral503. Such a high degree of variability as presented in a map can be confusing when the map is viewed by operator166. Further, a group of transient characteristics is generally indicated by the cluster of specks within the circle referenced as numeral501. Plot500provides little insight as to the performance of the crop as well as the performance of the machine in harvesting the crop.

FIG. 5Bshows a pictorial view of a smoothed field plot510representing an adjusted and corrected agronomic parameter such as crop yield. In one example, calibration correction system138has utilized pre-processing logic142, and biasing collection logic144to prepare crop yield data and perform a smoothing operation on that data. It may be seen that plot510generally indicates a lesser degree of variance between the various portions of the field and the adjusted crop yield values, when compared to the raw data indications of plot500. In one example, bias correction logic144utilizes smoothing logic178to remove a determined calibration bias either between separate instances of a single machine, or individual instances of a plurality of machines operating in the same field. For example, it can be seen in plot510that the transient characteristics have been removed (e.g. represented by501in plot500), and that smoothed high density areas are generally represented by reference numerals512and514. On the other hand, smoothed low density areas are generally represented by reference numerals516and518. One example of a smoothed medium density area is generally represented by reference numeral520. As such, smoothed high density (e.g. high crop yield) is represented by regions that are colored red, while smoothed medium density is represented by regions that are colored yellow, and smoothed low density is represented by regions that are colored blue.

Field plot510provides an accurate field map, as generated by field map generator150, that allows an operator166to glean insights relating to the operation of agricultural machine100and the performance of the particular crop that is harvested. Of course, it is noted that map510may be also representative of any of the other previously discussed operational or agronomic parameters herein.

FIG. 6is a pictorial view of another example in which agricultural machine100is shown as a tillage machine600(or disc). Tillage machine600illustratively includes a variety of tillage implements602which generally include rotating disks that form trenches in a ground surface as machine600moves in a direction generally indicated by arrow610. It is noted that tillage machine600is one representative view of a tillage machine and the calibration correction mechanisms discussed herein may be implemented on a variety of other tillage machines as well. Tillage machine600also illustratively includes guide wheels604and a mechanism for connecting to a powered mobile machine, the mechanism generally depicted at reference numeral606. Tillage machine600may also include a variety of operational parameter sensors. For example, a tillage machine600includes tillage depth sensors608which may be disposed in a variety of locations on the machine but which are particularly illustratively disposed at or near disks602. The tillage depth sensors608may sense a depth of the disk602within a ground surface, and provide the sensed data to other monitoring logic130of operational parameter monitoring system118. As discussed above with respect to agricultural machine100, the sensed parameter data may be utilized by calibration correction system138to determine a bias correction factor between the individual sensors608on machine600, and/or the calibration bias between machine600and a variety of other sensors disposed on other tillage machines performing a similar or same operation.

Other soil conditions may also be monitored by machine600or any other agricultural machine described herein. These other soil conditions may include, but are not limited to soil moisture, soil temperature, organic matter composition, soil nutrient levels, bulk soil density, and planter down pressure with respect to a ground surface, among others.

FIG. 7is a pictorial view of one example in which agricultural machine100is shown as a planter700. Planter700illustratively includes a supporting structure702which houses a plurality of planting row units706. Planter700may also include an interface for connecting to a power mobile machine, the interface generally being represented by704. As similarly discussed with respect agricultural machine100and tillage machine600, planting machine700may include a variety of sensors disposed at a variety of locations on the machine and configured to sense parameters such as, but not limited to, a planting depth. In one particular example, machine orientation sensors708may sense the height of a planting depth sensor with respect to the ground. These machine orientation sensors may include the planting depth sensors that provide planting depth data in accordance with planting depth monitoring logic128and operational parameter monitoring system118. The obtained planting depth data may be utilized by calibration correction system138to determine an offset either between individual sensors on planter700, and/or between sensors and a determined calibration of machine700and a variety of other similar planting machines.

Other example agricultural machines (or any of the machines described above), in accordance with embodiments described herein, may include sprayers or applicators for applying, for instance, fertilizers, pesticides, or other nutrient formulas. Operational parameter monitoring may be performed to obtain information regarding the rate at which said formulas are applied. As such, the calibration correction system and methods described herein may be used accordingly in determining a proper calibration and reducing calibration bias for chemical application operations, and specifically for operations that include measuring the rate at which a formula is applied during operation. As such, the agronomic data that can be monitored and adjusted with embodiments of a calibration correction system discussed herein includes data associated with grain moisture, grain loss, grain quality, residue yield, residue quality, unthreshed properties, chemical application rate, soil moisture, soil temperature, organic matter composition, soil nutrient levels, bulk soil density, and planter down pressure with respect to a ground surface, among others.

The present discussion has mentioned processors and/or servers. In one example, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. They are functional parts of the systems or devices to which they belong and are activated by, and facilitate the functionality of the other components or items in those systems.

A number of data stores have also been discussed. It will be noted they can each be broken into multiple data stores. All stores can be local to the systems accessing them, all can be remote, or some can be local while others are remote. All of these configurations are contemplated herein.

FIG. 8is a block diagram of agricultural machine100, shown inFIG. 1, except that it communicates with elements in a remote server architecture101. In an example embodiment, remote server architecture101can provide computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various embodiments, remote servers can deliver the services over a wide area network, such as the internet, using appropriate protocols. For instance, remote servers can deliver applications over a wide area network and they can be accessed through a web browser or any other computing component. Software or components shown inFIG. 1as well as the corresponding data, can be stored on servers at a remote location. The computing resources in a remote server environment can be consolidated at a remote data center location or they can be dispersed. Remote server infrastructures can deliver services through shared data centers, even though they appear as a single point of access for the user. Thus, the components and functions described herein can be provided from a remote server at a remote location using a remote server architecture. Alternatively, they can be provided from a conventional server, or they can be installed on client devices directly, or in other ways.

In the example shown inFIG. 8, some items are similar to those shown inFIG. 1and they are similarly numbered.FIG. 8specifically shows that data visualization system148, remote systems160and data store114can be located at a remote server location103. Therefore, harvester100accesses those systems through remote server location103.

FIG. 8also depicts another example of a remote server architecture.FIG. 8shows that it is also contemplated that some elements ofFIG. 1are disposed at remote server location103while others are not. By way of example, data store114or agricultural machines162,164can be disposed at a location separate from location103, and accessed through the remote server at location103. Regardless of where they are located, they can be accessed directly by harvester100, through a network (either a wide area network or a local area network), they can be hosted at a remote site by a service, or they can be provided as a service, or accessed by a connection service that resides in a remote location. Also, the data can be stored in substantially any location and intermittently accessed by, or forwarded to, interested parties. For instance, physical carriers can be used instead of, or in addition to, electromagnetic wave carriers. In such an embodiment, where cell coverage is poor or nonexistent, another mobile machine (such as a fuel truck) can have an automated information collection system. As the combine or machine comes close to the fuel truck for fueling, the system automatically collects the information from the harvester using any type of ad-hoc wireless connection. The collected information can then be forwarded to the main network as the fuel truck reaches a location where there is cellular coverage (or other wireless coverage). For instance, the fuel truck may enter a covered location when traveling to fuel other machines or when at a main fuel storage location. All of these architectures are contemplated herein. Further, the information can be stored on the harvester until the harvester enters a covered location. The harvester, itself, can then send the information to the main network.

It will also be noted that the elements ofFIG. 1, or portions of them, can be disposed on a wide variety of different devices. Some of those devices include servers, desktop computers, laptop computers, tablet computers, or other mobile devices, such as palm top computers, cell phones, smart phones, multimedia players, personal digital assistants, etc.

FIG. 9is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a user's or client's hand held device16, in which the present system (or parts of it) can be deployed. For instance, a mobile device can be deployed in the operator compartment of machine100for use in generating, processing, or displaying the data.FIGS. 10-12are examples of handheld or mobile devices.

FIG. 9provides a general block diagram of the components of a client device16that can run some components shown inFIG. 1, that interacts with them, or both. In the device16, a communications link13is provided that allows the handheld device to communicate with other computing devices and under some embodiments provides a channel for receiving information automatically, such as by scanning. Examples of communications link13include allowing communication though one or more communication protocols, such as wireless services used to provide cellular access to a network, as well as protocols that provide local wireless connections to networks.

Under other embodiments, applications can be received on a removable Secure Digital (SD) card that is connected to an interface15. Interface15and communication links13communicate with a processor17(which can also embody processor102fromFIG. 1) along a bus19that is also connected to memory21and input/output (I/O) components23, as well as clock25and location system27.

I/O components23, in one embodiment, are provided to facilitate input and output operations. I/O components23for various embodiments of the device16can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors and output components such as a display device, a speaker, and or a printer port. Other I/O components23can be used as well.

FIG. 10shows one example in which device16is a tablet computer1000. InFIG. 10, computer1000is shown with user interface display screen1002. Screen1002can be a touch screen or a pen-enabled interface that receives inputs from a pen or stylus. It can also use an on-screen virtual keyboard. Of course, it might also be attached to a keyboard or other user input device through a suitable attachment mechanism, such as a wireless link or USB port, for instance. Computer1000can also illustratively receive voice inputs as well.

FIG. 11provides an additional example of devices16that can be used, although others can be used as well. InFIG. 11, a feature phone, smart phone or mobile phone45is provided as the device16. Phone45includes a set of keypads47for dialing phone numbers, a display49capable of displaying images including application images, icons, web pages, photographs, and video, and control buttons51for selecting items shown on the display. The phone includes an antenna53for receiving cellular phone signals. In some embodiments, phone45also includes a Secure Digital (SD) card slot55that accepts a SD card57.

FIG. 13is one example of a computing environment in which elements ofFIG. 1, or parts of it, (for example) can be deployed. With reference toFIG. 13, an example system for implementing some embodiments includes a general-purpose computing device in the form of a computer810. Components of computer810may include, but are not limited to, a processing unit820(which can comprise processor102), a system memory830, and a system bus821that couples various system components including the system memory to the processing unit820. The system bus821may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Memory and programs described with respect toFIG. 1can be deployed in corresponding portions ofFIG. 13.

The computer810may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,FIG. 13illustrates a hard disk drive841that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive851, nonvolatile magnetic disk852, an optical disk drive855, and nonvolatile optical disk856. The hard disk drive841is typically connected to the system bus821through a non-removable memory interface such as interface840, and magnetic disk drive851and optical disk drive855are typically connected to the system bus821by a removable memory interface, such as interface850.

The computer810is operated in a networked environment using logical connections (such as a local area network—LAN, or wide area network WAN) to one or more remote computers, such as a remote computer880.

When used in a LAN networking environment, the computer810is connected to the LAN871through a network interface or adapter870. When used in a WAN networking environment, the computer810typically includes a modem872or other means for establishing communications over the WAN873, such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device.FIG. 13illustrates, for example, that remote application programs885can reside on remote computer880.

Example 1 is a method of reducing a sensing system bias across a first agricultural machine and a second agricultural machine, comprising:accessing a collection of agronomic data indicative of an estimated crop yield, the collection including at least a first set of data sensed by the first agricultural machine and a second set of data sensed by the second agricultural machine;scaling the first and second sets of data based on a yield correction factor;determining a bias between the scaled first set of data and the scaled second set of data;performing a smoothing operation, according to smoothing logic, on the scaled first and second sets of data, wherein performing the smoothing operation includes:generating a calibration correction factor based on the determined bias;reducing the bias between the scaled first set of data and the scaled second set of data to obtain a corrected set of crop yield data; andoutputting the calibration correction factor to the first and second agricultural machines for use in sensing the first set of data on the first agricultural machine and the second set of data on the second agricultural machine; andgenerating a field map view indicative of the corrected set of crop yield data.

Example 2 is the method of any or all previous examples, wherein scaling the first and second sets of data comprises:determining a first offset in the first set of data and a second offset in the second set of data; andgenerating the yield correction factor based on the determined first and second offsets.

Example 3 is the method of any or all previous examples, wherein scaling further comprises:applying the yield correction factor to the first set to generate the scaled first set of data; andapplying the yield correction factor to the second set to generate the scaled second set of data.

Example 4 is the method of any or all previous examples, wherein determining the first and second offsets comprises, respectively:identifying a first average deviation in the first set, wherein the first average deviation is greater than a minimum deviation threshold that is required to perform the scaling; andidentifying a second average deviation in the second set, wherein the second average deviation is greater than a minimum deviation threshold that is required to perform the scaling.

Example 5 is the method of any or all previous examples, wherein the first set of data is sensed by the first agricultural machine during a harvesting operation that is performed relative to a first portion of a harvesting environment, and wherein the second set of data is sensed by the second agricultural machine during a harvesting operation that is performed relative to a second portion of a harvesting environment.

Example 6 is the method of any or all previous examples, wherein performing the smoothing operation, according to the smoothing logic, comprises executing an additive smoothing function with respect to the estimated crop yield data.

Example 7 is the method of any or all previous examples, wherein performing the smoothing operation further comprises:identifying at least one transient characteristic in the corrected set of crop yield data; andreducing the transient characteristic using artifact removal logic.

Example 8 is the method of any or all previous examples, wherein the at least one transient characteristic is indicative of a delayed speed correction.

Example 9 is the method of any or all previous examples, wherein performing the smoothing operation further comprises:obtaining an indication of ground truth.

Example 10 is the method of any or all previous examples, wherein the indication of ground truth identifies an actual crop yield measurement.

Example 11 is a method of correcting a sensing system bias in an agricultural machine, comprising:accessing a collection of agronomic data sensed by the agricultural machine, the collection including at least a first set and a second set;adjusting the first and second sets based on a parameter correction factor;determining a sensing system bias between the adjusted first and second sets;performing a smoothing operation on the adjusted first and second sets, the smoothing operation including:generating a calibration correction factor; andapplying the calibration correction factor to the adjusted first and second sets to reduce the sensing system bias; andgenerating a visual data representation of the adjusted first and second sets with the sensing system bias reduce.

Example 12 is the method of any or all previous examples, wherein the first set includes agronomic data that is sensed during a first pass of the agricultural machine in an operation environment, and wherein the second set includes agronomic data that is sensed during a second pass of the agricultural machine in the operation environment.

Example 13 is the method of any or all previous examples, wherein performing the smoothing operation comprises executing at least one of:an additive smoothing operation; ora localized regression smoothing operation.

Example 14 is the method of any or all previous examples, wherein the agronomic data is indicative of at least one of:a yield parameter;a planting depth parameter;a soil moisture parameter;a tillage parameter; ora geospatial parameter.

Example 15 is the method of any or all previous examples, wherein adjusting the first and second sets based on a parameter correction factor comprises:identifying an offset within each of the first and second sets;generating a parameter correction factor based on the identified offset; andapplying the parameter correction factor to the first and second sets.

Example 16 is the method of any or all previous examples, wherein the visual data representation comprises a corrected field map view.

Example 17 is the method of any or all previous examples, further comprising:executing pre-processing logic that includes:filtering the first and second sets of data based on a filter criterion, wherein the filter criterion is indicative of a machine state of the agricultural machine.

Example 18 is an agricultural machine comprising:a yield monitoring system that includes a mass flow sensor and a geospatial sensor, wherein the yield monitoring system is configured to obtain a collection of yield density information;a calibration correction system configured to determine a calibration offset factor, whereinthe calibration correction system includes:pre-processing logic that scales the collection of yield density information; andbias correction logic that performs a smoothing operation, based on the calibration offset factor, on the scaled collection of yield density information to remove a sensing system bias; anda data visualization system that generates a field map view of the collection of yield density information with the sensing system bias removed.

Example 19 is the agricultural machine of any or all previous examples, further comprising:a calibration system configured to provide the calibration offset factor to the yield monitoring system; andwherein the yield monitoring system uses the calibration offset factor in obtaining the collection of yield density information.

Example 20 is the agricultural machine of any or all previous examples, wherein the bias correction logic comprises a data verification component, and wherein the data verification component is configured to:obtain an indication of a ground truth measurement; anduse the ground truth measurement in performing the smoothing operation with the bias correction logic.