Patent Description:
There are a wide variety of different types of agricultural machines, such as seeding or planting machines, tillage machines, material application machines, etc. Tillage machines till or otherwise engage the soil. The material application machines apply material, such as fertilizer, herbicide, pesticide, or other material to the soil. The seeders and planters can include row crop planters, or the like. The seeding or planting machines place seeds at a desired depth within a plurality of parallel seed trenches that are formed in the soil. As one example of a planting machine, a row unit is often mounted to a planter with a plurality of other row units. The planter is often towed by a tractor over soil where seed is planted in the soil, using the row units. The row units on the planter follow the ground profile by using a combination of a down force assembly that imparts a down force to the row unit to push disk openers into the ground and gauge wheels to set the depth of penetration of the disk openers. The mechanisms that are used for moving the seed from the seed hopper to the ground often include a seed metering system and a seed delivery system.

The seed metering system receives the seeds in a bulk manner, and divides the seeds into smaller quantities (such as a single seed, or a small number of seeds - depending on the seed size and seed type) and delivers the metered seeds to the seed delivery system. In one example, the seed metering system uses a rotating mechanism (which is normally a disc or a concave or bowl-shaped mechanism) that has seed receiving apertures, that receive the seeds from a seed pool and move the seeds from the seed pool to the seed delivery system which delivers the seeds to the ground (or to a location below the surface of the ground, such as in a trench). The seeds can be biased into the seed apertures in the seed metering system using air pressure (such as a vacuum or a positive air pressure differential).

There are also different types of seed delivery systems that move the seed from the seed metering system to the ground. One seed delivery system is a gravity drop system that includes a seed tube that has an inlet position below the seed metering system. Metered seeds from the seed metering system are dropped into the seed tube and fall (via gravitational force) through the seed tube into the seed trench. Other types of seed delivery systems are assistive systems, in that they do not simply rely on gravity to move the seed from the metering mechanism into the ground. Instead, such systems actively capture the seeds from the seed meter and physically move the seeds from the meter to a lower opening, where the seeds exit into the ground or trench.

Row units can also be used to apply material to the field (e.g., fertilizer, herbicide, insecticide, or pesticide, etc.) over which they are traveling. In some scenarios, each row unit has a valve that is coupled between a source of material to be applied, and an application assembly. As the valve is actuated, the material passes through the valve, from the source to the application assembly, and is applied to the field. In other scenarios, each row unit has a commodity tank and a commodity delivery system that delivers a commodity (such as fertilizer, herbicide, insecticide, pesticide, etc.) to the soil.

Tillage machines are often towed behind a towing vehicle, such as a tractor. The tillage machines can include soil engaging elements such as disks, plows, rippers, cultivators, chisel plows, etc. The soil engaging elements can be controlled to control characteristics of soil engagement, such as depth of engagement, angle of engagement, among other things.

Material application machines can include a side-dress bar, a sprayer, or other material application systems. Some such machines can open a furrow in the soil, apply material, and close the furrow. Such machines can also apply material as seed is planted or in other ways.

All of these types of agricultural machines use sensors to sense different parameters or characteristics or conditions (sensed values). Some of the sensed values include geospatial data in that the values are correlated to a geographic location. However, it can be difficult to obtain instantaneous sensed values that are meaningful. Therefore, sensed values are often aggregated (e.g., averaged) to obtain an aggregated value corresponding to a geographic location. Agricultural systems sensing a characteristic of an agricultural operation performed by an agrultural machine are also known from <CIT>, <CIT>, <CIT>, <CIT> and <CIT>.

A signal processor in an agricultural system aggregates sensor samples to obtain an aggregated sensor value. A localization system identifies sensor samples used to obtain the aggregated sensor value and generates a localized sensor value. The agricultural system generates an action signal based on the localized sensor value.

The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background.

As discussed above, many different types of agricultural machines have a plurality of different sensors that sense values of different variables. The sensed values can be representative of signals or values responsive to or derived from soil characteristics, planting characteristics, machine characteristics, machine operation values, material characteristics (such as crop characteristics, seed characteristics, fertilizer characteristics, other commodity characteristics, etc.), characteristics of the task or operation being performed, and a wide variety of other parameters, characteristics, and/or conditions. Also, many of the sensed values are geospatial in that they are correlated to a geographic location on the field over which the agricultural machine is traveling.

However, it is difficult to obtain an instantaneous sensed value that is meaningful, because of noise, or simply because of the nature of the value being sensed. By way of example, a sensor on a row unit may be an accelerometer or an inertial measurement unit that generates an output during operation of the planting machine that is indicative of accelerations of the row unit which are, themselves, indicative of the ride quality of the row unit (and thus indicative of whether the row unit may be bouncing out of contact with the ground, etc.). The instantaneous values generated by the sensor may vary widely, and be less meaningful than an aggregated value (such as an averaged value that is a rolling average of a plurality of different samples). Therefore, for such sensed values, a plurality of different sensor samples are aggregated, over time (or distance), and used as a value that is correlated to a particular geographic location.

This can lead to inaccuracies and errors. For instance, assume that a geospatial data point is generated for a sensed value by aggregating twenty sensor samples taken as an agricultural machine travels over a field. Assume further that the agricultural machine travels one hundred feet while the twenty samples are taken. Such an aggregated value may not represent the value that occurred over the first ten feet of the distance travelled. Instead, the aggregated value is aggregated over one hundred feet and is geospatially correlated to that particular one hundred foot distance or to a point within that one hundred foot distance. Assume that the third through fifth sensor samples contain an aberrant spike in the sensor signal that is not seen in the other seventeen sensor samples. The aberrant spike in samples three-five will affect the aggregated value generated from the twenty data samples, even though it is aberrant, and even though it can be correlated to a geographic location that is geographically localized to an area within the geographic location for which the aggregated sensor value is being generated. Thus, an aberrant spike at one point in the field can deleteriously affect the accuracy of an aggregated sensor value that corresponds to a different location or area in the field. As discussed herein, a geographic location can be a point, a linear distance (such as a portion of a route traveled by a machine), or an area.

The present description thus proceeds with respect to a system that obtains a first aggregated sensor value obtained by aggregating sensor samples taken over a first geographic location and then analyzes the sensor samples that were used to generate the first aggregated sensor value to obtain a localized sensor value (an aggregated or non-aggregated value) that more accurately represents a second geographic location that is different from (e.g., within) the first geographic location. The localized sensor value is thus more localized, and thus more accurately reflects, the sensor samples taken at the second geographic location.

Further, the system can analyze the sensor samples that were used to generate the first aggregated value to identify any aberrant samples. The geographic location of the aberrant sample can be compared to the first geographic location of the first aggregated sensor value to determine whether the aberrant sample should be removed (or its effect mitigated) from the first aggregated sensor value. A corrected aggregated sensor value is then generated.

A control system generates action signals based upon the localized sensor value and/or the corrected aggregated sensor value.

<FIG> is a partial pictorial, partial schematic top view of one example of an architecture <NUM> that includes agricultural planting machine <NUM>, towing vehicle <NUM>, that is operated by operator <NUM>, and control system <NUM> that, itself, includes material application control system <NUM>, planting control system <NUM>, and other items <NUM>. Control system <NUM>, can be on one or more individual parts of machine <NUM> (such as on each row unit, or set of row units), centrally located on machine <NUM>, distributed about the architecture <NUM>, or on towing vehicle <NUM>, or located (completely or partially) in a remote location, such as on the cloud. Operator <NUM> can illustratively interact with operator interface mechanisms <NUM> to manipulate and control vehicle <NUM>, control system <NUM>, and some or all portions of machine <NUM>.

Machine <NUM> is one example of a row crop planting machine that illustratively includes a toolbar <NUM> that is part of a frame <NUM>. <FIG> also shows that a plurality of planting row units <NUM> are mounted to the toolbar <NUM>. Machine <NUM> can be towed behind towing vehicle <NUM>, such as a tractor. Seeds can be carried by containers on row units <NUM> or in a more centralized container and delivered to row units <NUM>. Row units <NUM> open furrows in the field, plant the seeds, and close the furrows. Planting control system <NUM> can receive sensor signals and control the planting systems on the row units. For instance, system <NUM> can control downforce, seed metering, seed delivery, furrow depth, propulsion and/or steering systems on the towing vehicle <NUM>, etc..

<FIG> shows that material can be stored in a tank <NUM> and pumped through a supply line <NUM> so the material (fertilizer, insecticide, herbicide, pesticide, etc.) can be dispensed in or near the rows being planted. In one example, a set of devices (e.g., actuators) <NUM> is provided to perform this operation. For instance, actuators <NUM> can be individual pumps that service individual row units <NUM> and that pump material from tank <NUM> through supply line <NUM> so the material can be dispensed on the field. In such an example, material application control system <NUM> receives sensor signals and other inputs and controls the pumps <NUM>. In another example, actuators <NUM> are valves and one or more pumps <NUM> pump the material from tank <NUM> to valves <NUM> through supply line <NUM>. In such an example, material application control system <NUM> controls valves <NUM> by generating valve or actuator control signals. The control signal for each valve or actuator can, in one example, be a pulse width modulated control signal. The flow rate through the corresponding valve <NUM> can be based on the duty cycle of the control signal (which controls the amount of time the valve is open and closed). The flow rate can be based on multiple duty cycles of multiple valves or based on other criteria. Further, the material can be applied in varying rates. For example, fertilizer may be applied at one rate when it is being applied at a location where it will be spaced from a seed location and at a second, higher, rate when it is being applied at a location closer to the seed location. These are examples only.

In addition, each row unit <NUM> can have a commodity tank <NUM> that stores material to be applied. A commodity delivery system <NUM> can have a motor that drives a commodity meter that dispenses an amount of the material. The motor can be controlled by material application control system <NUM> to dispense the material at desired locations or in another desired way.

<FIG> is a side view showing one example of a row unit <NUM> (or a portion of row unit <NUM>) in more detail. <FIG> shows that each row unit <NUM> illustratively has a frame <NUM>. Frame <NUM> is illustratively connected to toolbar <NUM> by a linkage shown generally at <NUM>. Linkage <NUM> is illustratively mounted to toolbar <NUM> so that it can move upwardly and downwardly (relative to toolbar <NUM>).

Row unit <NUM> also illustratively has a seed hopper <NUM> that receives or stores seed. The seed is provided from hopper <NUM> to a seed metering system <NUM> that meters the seed and provides the metered seed to a seed delivery system <NUM> that delivers the seed from the seed metering system <NUM> to the furrow or trench generated by the row unit. In one example, seed metering system <NUM> uses a rotatable member, such as a disc or concave-shaped rotating member, and an air pressure differential to retain seed on the disc and move the seed from a seed pool of seeds (provided from hopper <NUM>) to the seed delivery system <NUM>. Other types of meters can be used as well. Row unit <NUM> can also include an additional hopper that can be used to provide additional material, such as a fertilizer or another chemical.

Row unit <NUM> includes furrow opener <NUM> and a set of gage wheels <NUM>. In operation, row unit <NUM> moves generally in a direction indicated by arrow <NUM>. Furrow opener <NUM> has blades or disks that open a furrow on the soil. Gage wheels <NUM> control a depth of the furrow, and seed is metered by seed metering system <NUM> and delivered to the furrow by seed delivery system <NUM>. A downforce/upforce generator (or actuator) <NUM> can also be provided to controllably exert downforce/upforce to keep the row unit <NUM> in desired engagement with the soil. Downforce/upforce generator <NUM> can be a double acting actuator, such as a double acting hydraulic cylinder, a pneumatic actuator, or another actuator that transfers downforce (and/or upforce) from toolbar <NUM> to row unit <NUM>.

Therefore, in one example, the downforce acting on row unit <NUM> includes the row unit downforce (or upforce) generated by downforce/upforce actuator <NUM> represented by arrow <NUM> in <FIG>. The downforce acting on row unit <NUM> also includes the self-weight of row unit <NUM> and the components of row unit <NUM> as represented by arrow <NUM> in <FIG>. The downforces <NUM> and <NUM> are countered by the force that the ground exerts on the blades on furrow opener <NUM> that are opening the furrow in the soil, as represented by arrow <NUM> in <FIG>. The downforces <NUM> and <NUM> are also countered by the force that the ground exerts on the gage wheels <NUM> (the gage wheel reaction force) indicated by arrow <NUM> in <FIG>.

<FIG> also shows that row unit <NUM> includes closing wheels <NUM>. Closing wheels <NUM> close the furrow that is opened by furrow opener <NUM>, over the seed. In the example shown in <FIG>, the downforce exerted on row unit <NUM> is also countered by the upwardly directed force imparted on closing wheels <NUM>, as represented by arrow <NUM> in <FIG>.

<FIG> shows that row unit <NUM> can also include a row cleaner <NUM>. Row cleaner <NUM> generally cleans the row ahead of the opener <NUM> to remove plant debris and other items from the previous growing season. Therefore, the downforce on row unit <NUM> is also countered by an upwardly directed force that the ground exerts on row cleaner <NUM>, as indicated by arrow <NUM>. Row units <NUM> can be configured differently than that shown in <FIG> and row unit <NUM> shown in <FIG> is just one example.

<FIG> is a perspective view showing one example of a mobile agricultural tillage machine <NUM> that includes a towing vehicle <NUM>, illustratively a tractor, and a tillage implement <NUM>. Towing vehicle <NUM> includes an operator compartment <NUM>, which may have various operator input mechanisms, a propulsion subsystem <NUM>, such as a powertrain (e.g., engine or motor, transmission, etc.), and a set of ground engaging elements <NUM>, illustratively shown as wheels, but in other examples, could be tracks. The towing vehicle <NUM> is coupled to and tows tillage implement <NUM>. A tillage control system <NUM> receives sensor signals and generates control signals to control tillage machine <NUM>. Tillage implement <NUM> includes a plurality of wheels <NUM> which support a frame <NUM> of a main section <NUM> above the field. Tillage implement <NUM> also includes a subframe <NUM>. Tillage implement <NUM> further includes a plurality of wing sections <NUM> which are coupled to main section <NUM>. The wing sections <NUM> include tool frames <NUM>, coupled to the subframe <NUM>, and tillage tools <NUM> (shown as disks) coupled to the tool frames <NUM>. The wing sections <NUM> are actuatable (and deployable) by respective actuators <NUM> which are controllable by system <NUM> to change a position of wing sections <NUM> but are also controllable by system <NUM> to apply a downforce to the wing sections <NUM> and thus the tools <NUM>. Implement <NUM> can also include a variety of other tools which are coupled to the main frame <NUM> or the subframe <NUM> via respective tool frames. For example, implement <NUM> includes a plurality of tillage tools <NUM> (shown as ripper shanks) which are coupled to respective tool frames <NUM>, a plurality of tillage tools <NUM> (shown as closing disks) which are coupled to respective tool frames <NUM>, as well as a plurality of tillage tools <NUM> (shown as rolling or finishing baskets) coupled to respective tool frames <NUM>.

<FIG> is a perspective view showing tillage implement <NUM> in further detail. Tillage implement <NUM> is towed by towing vehicle <NUM> (not shown in <FIG>) in the direction indicated by arrow <NUM> and operates at a field <NUM>. Tillage implement <NUM> includes a plurality of tools that can engage the surface <NUM> of the ground or penetrate the sub-surface <NUM> of the ground. As illustrated, implement <NUM> may include a connection assembly <NUM> for coupling to the towing vehicle <NUM>. Connection assembly that includes a mechanical connection mechanism <NUM> (shown as a hitch) as well as a connection harness <NUM> which may include a plurality of different connection lines, which may provide, among other things, power, fluid (e.g., hydraulics or air, or both), as well as communication. In some examples, implement <NUM> may include its own power and fluid sources. The connection lines of connection harness <NUM> may form a conduit for delivering power and/or fluid to the various actuators on implement <NUM>.

As illustrated in <FIG>, implement <NUM> can include a plurality of actuators. Actuators <NUM> are coupled between subframe <NUM> and hinge or pivot assembly <NUM> and are controllably actuatable by system <NUM> to change a position of the subframe <NUM> relative to the mainframe <NUM> in order to change a position of the disks <NUM> relative to the mainframe <NUM> as well as to apply a downforce to the disks <NUM>.

Actuators <NUM> are coupled between a wheel frame <NUM> and main frame <NUM> and are controllably actuatable by system <NUM> to change a position of the wheels <NUM> relative to the main frame <NUM> and thus change a distance between main frame <NUM> and the surface <NUM> of the field <NUM> as well as to apply a downforce to the wheels <NUM>. Thus, actuators <NUM> can be used to control the depth of the various tools of implement <NUM>. Additionally, each wheel <NUM> can include a respective actuator <NUM> that is separately controllable by system <NUM> such that the implement <NUM> can be leveled across its width. For instance, where the ground near a left wheel <NUM> is lower than the ground by a right wheel, the left wheel can be extended farther, by controllably actuating a respective actuator <NUM>, than the right wheel <NUM> to level the implement <NUM> across its width. Additionally, a tillage implement <NUM> may include a plurality of wheels <NUM> across both its width and across its fore-to-aft length such that both side-to-side leveling and fore-to-aft (e.g., front-to-back, or vice versa) leveling can be achieved by variably controlling the separate wheels. These additional wheels can be coupled to the main frame or to subframes such that wing leveling can also occur. Additionally, it will be noted that actuators <NUM>, shown in <FIG>, can also act to level the wing sections <NUM>.

Actuators <NUM> are coupled between tool frame <NUM> and main frame <NUM> or subframe <NUM> and are controllably actuatable to change a position of tools <NUM> as well as to apply a downforce to tools <NUM>. While tools <NUM> are shown as ripper shanks, in other examples a tillage implement <NUM> may include other tools, alternatively or in addition to ripper shanks, such as tines.

Actuators <NUM> are coupled between tool frame <NUM> and tool subframe <NUM> and are controllably actuatable to change a position of tools <NUM> as well as to apply a downforce to tools <NUM>. While tools <NUM> are shown as ripper shanks, in other examples a tillage implement <NUM> may include other tools, alternatively or in addition to ripper shanks, such as tines.

Actuators <NUM> are coupled between tool frame <NUM> and tool frame <NUM> and are actuatable to change a position of tools <NUM> as well as apply a downforce to tools <NUM>.

<FIG> is a side view of an example of an agricultural system <NUM> which includes an agricultural implement, in particular an air or pneumatic seeder <NUM>. In the example shown in <FIG>, the seeder <NUM> comprises a tilling implement (or seeding tool) <NUM> (also sometimes called a drill) towed between a tractor (or other towing vehicle) <NUM> and a commodity cart (also sometimes called an air cart) <NUM>. The commodity cart <NUM> has a frame <NUM> upon which a series of product tanks <NUM>, <NUM>, <NUM>, and <NUM>, and wheels <NUM> are mounted. Each product tank has a door (a representative door <NUM> is labeled) releasably sealing an opening at its upper end for filling the tank with product, most usually a commodity of one type or another. A metering system <NUM> is provided at a lower end of each tank (a representative one of which is labeled) for controlled feeding or draining of product (most typically granular material) into a pneumatic distribution system <NUM>. The tanks <NUM>, <NUM>, <NUM>, and <NUM> can hold, for example, a material or commodity such as seed or fertilizer to be distributed to the soil. The tanks can be hoppers, bins, boxes, containers, etc. The term "tank" shall be broadly construed herein. Furthermore, one tank with multiple compartments can also be provided instead of separated tanks.

The tilling implement or seeding tool <NUM> includes a frame <NUM> supported by ground wheels <NUM>. Frame <NUM> is connected to a leading portion of the commodity cart <NUM>, for example by a tongue style attachment (not labeled). The commodity cart <NUM> as shown is sometimes called a "tow behind cart," meaning that the cart <NUM> follows the tilling implement <NUM>. In an alternative arrangement, the cart <NUM> can be configured as a "tow between cart," meaning the cart <NUM> is between the tractor <NUM> and tilling implement <NUM>. In yet a further possible arrangement, the commodity cart <NUM> and tilling implement <NUM> can be combined to form a unified rather than separated configuration. These are just examples of additional possible configurations. Other configurations are also possible and all configurations should be considered contemplated and within the scope of the present description.

In the example shown in <FIG>, tractor <NUM> is coupled by couplings <NUM> to seeding tool <NUM> which is coupled by couplings <NUM> to commodity cart <NUM>. The couplings <NUM> and <NUM> can be mechanical, hydraulic, pneumatic, and electrical couplings and/or other couplings. The couplings <NUM> and <NUM> can include wired and/or wireless couplings as well. The pneumatic distribution system <NUM> includes a fan (not shown) connected to a product delivery conduit structure having multiple product flow passages <NUM>. The fan directs air through the flow passages <NUM>. Each product metering system <NUM> controls delivery of product from its associated tank at a controllable rate to the transporting airstreams moving through flow passages <NUM>. In this manner, each flow passage <NUM> carries product from the tanks to a secondary distribution tower <NUM> on the tilling implement <NUM>. Typically, there will be one tower <NUM> for each flow passage <NUM>. Each tower <NUM> includes a secondary distributing manifold <NUM>, typically located at the top of a vertical tube. The distributing manifold <NUM> divides the flow of product into a number of secondary distribution lines <NUM>. Each secondary distribution line <NUM> delivers product to one of a plurality of ground engaging tools <NUM> (also known as ground openers) that define the locations of work points on seeding tool <NUM>. The ground engaging tools <NUM> open a furrow in the soil <NUM> and facilitate deposit of the product therein. The number of flow passages <NUM> that feed into secondary distribution may vary from one to eight or ten or more, depending at least upon the configuration of the commodity cart <NUM> and tilling implement <NUM>. Depending upon the cart and implement, there may be two or more distribution manifolds <NUM> in the air stream between the meters <NUM> and the ground engaging tools <NUM>. Alternatively, in some configurations, the product is metered directly from the tank or tanks into secondary distribution lines that lead to the ground engaging tools <NUM> without any need for an intermediate distribution manifold. The product metering system <NUM> can be configured to vary the rate of delivery of seed to each work point on tool <NUM> or to different sets or zones of work points on tool <NUM>. The configurations described herein are only examples. Other configurations are possible and should be considered contemplated and within the scope of the present description.

A firming or closing wheel <NUM> associated with each ground engaging tool <NUM> trails the tool and firms the soil over the product deposited in the soil. In practice, a variety of different types of tools <NUM> are used including, but not necessarily limited to, tines, shanks and disks. The tools <NUM> are typically controllably moveable between a lowered position engaging the ground and a raised position riding above the ground. Each individual tool <NUM> may be configured to be raised by a separate actuator. Alternatively, multiple tools <NUM> may be mounted to a common component for movement together. In yet another alternative, the tools <NUM> may be fixed to the frame <NUM>, the frame being configured to be raised and lowered with the tools <NUM>.

Examples of air or pneumatic seeder <NUM> described above should not be considered limiting. The features described in the present description can be applied to any seeder configuration, or other material application machine, whether specifically described herein or not.

<FIG> also shows that agricultural system <NUM> can include various other systems, such as, for example, look-ahead planting control system <NUM>. System <NUM> senses the yaw rate on tractor <NUM> and uses that yaw rate to predict the yaw rate across the frame <NUM> of implement <NUM>, at the different work points where seeds are delivered to the furrows.

It will be appreciated, that different portions of system <NUM> can reside on tractor <NUM>, on tool or implement <NUM>, and/or on air cart <NUM>, or all of the elements of system <NUM> can be located at one place (e.g., on tractor <NUM>). Elements of system <NUM> can be distributed to a remote server architecture or in other ways as well. The sensed yaw rate can be used to control various actuators on the air or pneumatic seeder <NUM>.

<FIG> is a side perspective view of an applicator unit <NUM>. Applicator unit <NUM> can be attached to a tillage machine, a planting machine, or another machine. Briefly, in operation, applicator unit <NUM> attaches to a side-dress bar that is towed behind a towing vehicle <NUM>, so unit <NUM> travels between rows (if the rows are already planted). However, instead of planting seeds, applicator unit <NUM> applies material at a location between rows of seeds (or, if the seeds are not yet planted, between locations where the rows will be, after planting). When traveling in the direction indicated by arrow <NUM>, disc opener <NUM> (in this example, it is a single disc opener) opens furrow <NUM> in the ground <NUM>, at a depth set by gauge wheel <NUM>. When actuator <NUM> is actuated, material is applied in the furrow <NUM> and closing wheels <NUM> then close the furrow <NUM>.

As unit <NUM> moves, material application control system <NUM> controls actuator <NUM> to dispense material. The dispensing of material can be done relative to seed or plant locations, if they are sensed or are already known or have been estimated. The dispensing of material can also be done before the seed or plant locations are known. In this latter scenario, the locations where the material is applied can be stored so that seeds can be planted later, relative to the locations of the material that has been already dispensed.

<FIG> shows that actuator <NUM> can be mounted to one of a plurality of different positions on unit <NUM>. Two of the positions are shown at <NUM> and <NUM>. These are examples and the actuator <NUM> can be located elsewhere as well. Similarly, multiple actuators can be disposed on unit <NUM> to dispense multiple different materials or to dispense the material in a more rapid or more voluminous way than is done with only one actuator <NUM>.

It should also be noted that portions of the present discussion proceed with respect to a planting machine and sensors on the planting machine that generate sensor signals that are used to generate geospatial data. However, it will be appreciated that the present system can be used with any of a wide variety of different types of agricultural machines, such as tillage machines, material application machines, those described above, and others, that have sensors that are used to generate geospatial data.

<FIG> is a block diagram of one example of an architecture <NUM> in which an agricultural system <NUM> can communicate with one or more other machines <NUM> and other systems <NUM> over network <NUM>. Some items are similar to those shown in previous FIGS. and they are similarly numbered. Network <NUM> can be a wide area network, a local area network, a near field communication network, a cellular communication network, a WIFI network, a Bluetooth network, or any of a wide variety of other networks or combinations of networks. In <FIG>, operator <NUM> can also interact with certain portions of agricultural system <NUM>. Also, in the example shown in <FIG>, the items in agricultural system <NUM> can be on a row unit <NUM>, agricultural machine <NUM>, a towing vehicle <NUM>, any of machines <NUM>, <NUM>, <NUM>, a remote system (such as in the cloud), or elsewhere. The items in agricultural system <NUM> can be dispersed at different locations and on different machines and systems, or all of the items in agricultural system <NUM> can be located at a single location.

In the example shown in <FIG>, agricultural system <NUM> includes one or more processors or servers <NUM>, data store <NUM>, one or more sensors <NUM>, signal processor <NUM>, control system <NUM>, controllable systems <NUM>, and other items <NUM>. Sensors <NUM> can include position sensor <NUM>, planting characteristic sensors <NUM>, material application characteristic sensors <NUM>, tillage characteristic sensors <NUM>, machine sensors <NUM>, and other sensors <NUM>. Signal processor <NUM> includes conditioning system <NUM>, sampling system <NUM>, weighting/filtering system <NUM>, aggregation system <NUM>, sample geospatial correlation system <NUM>, correction system <NUM>, sample localization system <NUM>, action signal generator <NUM>, and other items <NUM>. Correction system <NUM> includes geospatial correlation component <NUM>, sample aberration identification component <NUM>, sample correction component <NUM>, and other items <NUM>. Sample localization system <NUM> includes sample isolation component <NUM>, sample window identification component <NUM>, sample window re-aggregation component <NUM>, and other items <NUM>. Control system <NUM> can include planting control system <NUM>, material application control system <NUM>, tillage control system <NUM>, and other systems <NUM>. Controllable systems <NUM> can include communication system <NUM>, operator interface mechanisms <NUM>, planting system <NUM>, material application system <NUM>, tillage systems <NUM>, machine systems (propulsion/steering/other) <NUM>, and any of a wide variety of other systems <NUM>. Before describing the overall operation of agricultural system <NUM> in more detail, a description of some of the items in system <NUM>, and their operation, will first be provided.

Position sensor <NUM> can be a global navigation satellite system (GNSS) receiver, a cellular triangulation system, or any of a wide variety of other sensors or sensing systems that provide an output indicative of the location of sensor <NUM> in a global or local coordinate system. Planting characteristic sensors <NUM> can be any of a wide variety of different types of sensors that sense characteristics and/or parameters and/or conditions of the planting operation being performed and generate a signal responsive to the variable being sensed. Some such sensors can include downforce sensors that sense the downforce on row units <NUM>, furrow sensors that sense the depth and/or quality of the furrow opened by the row units <NUM>, residue sensors that sense residue, soil characteristic sensors that sense soil characteristics (such as soil type, soil moisture, etc.), seed sensors that sense such things as the seed position and the seed orientation within the furrow, seed-to-soil contact sensors that sense the seed-to-soil contact within the furrow, the number of seed skips or multiples, or a wide variety of other planting characteristics, parameters or conditions. Material application characteristic sensors <NUM> can sense characteristics and/or parameters and/or conditions of material being applied (such as seeds, herbicide, pesticide, fertilizer, etc.) and generate a signal responsive to the variable being sensed. Thus, material application characteristic sensors <NUM> can sense the viscosity or density of the material being applied, the temperature of the material being applied, the velocity of material as it exits an application nozzle, the pressure drop across a nozzle that is applying material, the performance of the application (such as whether material is being applied at a desired location), and/or any of a wide variety of other material application characteristics. Tillage characteristic sensors <NUM> can sense characteristics and/or parameters and/or conditions of the tillage operation being performed by a tillage system and generate signals responsive to the sensed variables. For instance, sensors <NUM> can sense whether the tillage implement is level, the depth of soil engagement, the distribution of soil by the tillage systems, residue, soil type/moisture, forces external on the tillage system, etc. Machine sensors <NUM> can sense characteristics, or parameters, and/or conditions of the planting machine and/or the planting operation and generate a signal responsive to the sensed variable. For instance, machine sensors <NUM> can sense machine settings, fuel consumption or fuel efficiency, power usage, ride quality (which may be indicative of whether the row unit maintains consistent ground contact), machine speed, machine direction, the speed and/or position of the seed metering system, and/or the seed delivery system, and/or other systems, machine orientation (such as whether the machine is operating on a side hill, etc.), or any of a wide variety of other characteristics. The sensors <NUM> generate sensor signals responsive to the sensed variables which are provided to signal processor <NUM>.

Some examples of values that can be sensed or generated in response to sensed values can include the following:
When the agricultural machine is a planting machine, then the following values can be obtained based on or responsive to values sensed relative to the singulation system:.

The following values can be obtained based on or responsive to values sensed relative to a liquid metering system:.

The following values can be obtained based on or responsive to values sensed relative to a ground engaging element:.

When the agricultural machine is an air seeding machine, then the following values can be obtained based on or responsive to values sensed relative to a dry volumetric metering system:.

The following values can be obtained based on or responsive to values sensed relative to a seed distribution system and/or a ground engaging element:.

When the agricultural machine is a tillage machine, then the following values can be obtained based on or responsive to values sensed relative to ground engaging elements:.

Signal processor <NUM> processes the signals and generates an output which can be used by control system <NUM> in controlling the various controllable systems <NUM>. Signal conditioning system <NUM> can perform various types of signal conditioning on the sensor signals. Such conditioning can include amplifying, linearizing, normalizing, etc. Sampling system <NUM> samples the sensor signals in a desired way defined by sampling parameters. For instance, it may be that sampling system <NUM> samples the signals at a sampling rate so that a desired number of samples are obtained over a given time period. In another example, it may be that sampling system <NUM> samples the sensor signals a desired number of times per unit of distance traveled by the machine. By way of example, it may be that the sensor signal is to be sampled every six inches of machine travel. Sampling system <NUM> may sample the sensor signals in another time-based or distance-based way as well. Also, sampling may be based on the sensed value. For example, if the sensed value is changing quickly relative to the sampling rate, the sampling rate may be increased. If the sensed value is changing slowly relative to the sampling rate, than the sampling rate may be reduced.

Sampling system <NUM> obtains a value of the sensor signal being sampled, and saves that sensor signal value as a sample. The signal samples can also be weighted by weighting system <NUM>, as desired. For instance, it may be that signal samples taken more recently are weighted higher than those taken less recently. Further, it may be that signals taken under certain conditions (such as when the machine is operating faster or slower) may be weighted differently than those taken under other conditions. The signal samples and the weighted samples can be stored in data store <NUM> or elsewhere where they can be accessed by aggregation system <NUM>. Aggregation system <NUM> obtains multiple different signal samples taken at different times and/or at different locations, and aggregates the signal samples to obtain an aggregated sensor value. For instance, it may be that aggregation system <NUM> generates an average sensor value for the eight most recent weighted signal samples to obtain an aggregated sensor value. Sample geospatial correlation system <NUM> then correlates the aggregated sensor value to a geographic location to obtain a geospatial value that identifies the aggregated sensor value correlated to a geographic location. For instance, each of the signal samples may include a geographic stamp or a timestamp or other indicator indicating where/when the samples were taken. In another example, the sample geospatial correlation system <NUM> can obtain a position indicator from position sensor <NUM> when the aggregated sensor value is generated. System <NUM> can assign a geographic location to the aggregated sensor value, or can map the aggregated sensor value to a geographic map, or can generate a correlation between the aggregated sensor value and a geographic location in other ways, thus generating a geospatial sample.

As discussed above, it may be that some of the sample values used to generate the aggregated sensor value may be aberrant. The values of such an aberrant sample may be aberrations for any of a wide variety of different reasons. For instance, the sensors may be sensing in a noisy environment which can cause the sensor signals to spike, or to drop out, or to otherwise indicate an erroneous value. Therefore, correction system <NUM> analyzes the samples used to generate the aggregated sensor value to identify whether any of them are aberrant and if so, corrects the aggregated sensor value for the aberration. Sample aberration identification component <NUM> identifies sample values that were considered in generating the aggregated sensor value, that are deemed to be aberrant. In one example, an aberration can be identified if the value of the sample under analysis deviates from the values of samples on either side of it by a threshold amount. In another example, an aberrant sample can be identified if the value of the sample under analysis deviates from the aggregated sample value by a threshold amount. The sensor values can be identified as aberrant values in any of a wide variety of other ways as well.

Once sample aberration identification component <NUM> identifies particular samples that are aberrations, then geospatial comparison component <NUM> can determine how close the geographic location of the aberrant sample is to the geographic location assigned to the aggregated sensor value. By way of example, <FIG> shows an example in which an agricultural machine is traveling in the direction of travel indicated by arrow <NUM> along a route <NUM>. As the agricultural machine is traveling along the route <NUM>, a sensor (such as a planting characteristic sensor <NUM>), is generating a sensor signal indicative of a planting characteristic and sampling system <NUM> is generating signal samples corresponding to different geographic locations along route <NUM>. Assume also that the aggregation system <NUM> aggregates eight signal samples (such as by averaging them) to generate the aggregated sensor value. In that case, aggregation system <NUM> aggregates the sensor samples <NUM>-<NUM> (shown in <FIG>) and averages them to obtain an aggregated value A. Geospatial correlation system <NUM> assigns the geographic location corresponding to sensor value A on route <NUM> to the aggregated sensor value. If sample <NUM> is identified by sample aberration identification component <NUM> as an aberrant sample, it can be seen in <FIG> that the geographic location from which sample <NUM> was taken is significantly separated from the geographic location of aggregated sensor value A. Thus, the aberrant sample <NUM> may be more readily disregarded from the aggregated sensor value A because it is geospatially removed from the geographic location of the aggregated sensor value A by a significant distance.

<FIG>, on the other hand, shows an example in which the agricultural machine is first traveling along a route <NUM> in the direction indicated by arrow <NUM> and then makes a headland turn as indicated by arrow <NUM>, and begins traveling along route <NUM> in the direction indicated by arrow <NUM>, thus making an adjacent pass in the same field. In the example shown in <FIG>, it can be seen that the first four samples were taken at the end of route <NUM>, while the last four samples were taken at the beginning of route <NUM>. Thus, samples <NUM> and <NUM> are taken adjacent one another in the field. Geospatial comparison component <NUM> thus compares the locations of value A (which is also the geographic location corresponding to the aggregated sensor value) and the geographic location of sample <NUM> (the aberrant sample). Since the two geographic locations are relatively close to one another, then the aberrant sample <NUM> may be handled in a different way than in the example shown in <FIG>. For instance, in the example shown in <FIG>, the aberrant sample <NUM> may simply be discarded from the aggregated sample or replaced with another value because it is so far removed from the geographic location of the aggregated sensor value. However, in the example shown in <FIG>, the aberrant sample <NUM> may continue to be included in the aggregated sensor value A, because it is closely adjacent the geographic location for the aggregated sensor value.

After sample aberration identification component <NUM> has identified an aberrant sample, and after geospatial comparison component <NUM> has compared the geographic locations corresponding to the aggregated sensor value and the aberrant sample, sample correction component <NUM> can implement a correction to the aggregated sample. Again, if the aberrant sample is closely proximate the geographic location of the aggregated sensor value (e.g., immediately adjacent the aggregated senor value), the sample correction component <NUM> may make no correction, or may make a modest correction (such as by reducing the weight of the aberrant sample but still including it in the aggregated sensor value). However, if the aberrant sample is geographically removed from the geographic location of the aggregated sensor value by a significant distance (such as a threshold distance), then sample correction component <NUM> may correct the aggregated sensor value in a different way, such as by significantly de-weighting the aberrant sensor value, removing the aberrant sample from consideration in the aggregated sample, or in other ways.

Also, as discussed above, it may be that an aggregated sensor value is aggregated over a relatively large number of samples, but the operator or another system may be interested in a more localized value, such as a value which corresponds to only a subset of the sensor samples considered in generating the aggregated sensor value. Further, it may be that the aggregated sensor value is aggregated from samples taken over a first, relatively large geographic location, but the operator or another system may be interested in obtaining a more localized value which is taken from samples generated over a smaller location, such as a location that is within the first geographic distance. It will be noted that localization can be performed in terms of time as well so that the localized value is generated using samples generated during a time window that is smaller than the time window over which the samples were generated to obtain the aggregated sensor value. The present discussion proceeds with respect to localizing in terms of geographic location, but this is only one example. Sample localization system <NUM> identifies the desired sample window for which a localized, aggregated sample is to be generated and identifies the particular sample values that are to be considered in generating the localized, aggregated sensor value.

For instance, <FIG> is similar to <FIG>, and similar items are similarly numbered. It can be seen in <FIG> that the aggregated sensor value A is generated from samples taken over a first geographic region (or sample window) <NUM>. It may be, however, that the operator or another system wishes to obtain a more localized sensor value for a geographic region (or sample window) <NUM> that is within the geographic region <NUM>. In that case, sample isolation component <NUM> isolates the individual samples <NUM>-<NUM> that are used to obtain the aggregated sensor sample A. The samples <NUM>-<NUM> may be isolated based on the geographic locations assigned to the samples, based on a times when the samples were generated or in other ways. Sample window identification component <NUM> identifies the sample window <NUM> for the localized region of interest. For instance, the sample window <NUM> may be identified by geographic location, or by another indicator that serves to indicate which individual samples <NUM>-<NUM> are to be used in generating the localized sensor value for sample window <NUM>. Sample window re-aggregation component <NUM> then aggregates the number of samples corresponding to sample window <NUM> to generate a new aggregated sensor value (the localized sensor value) that corresponds to sample window <NUM>. In one example, sample window re-aggregation component <NUM> can obtain the sample values for samples <NUM> and <NUM> (in <FIG>) and provide them to aggregation system <NUM> which aggregates the sample values for sensor samples <NUM> and <NUM> to obtain the localized aggregated sensor value for sample window <NUM>. In another example, sample window re-aggregation component <NUM>, itself, aggregates the value of samples <NUM> and <NUM> to obtain a localized aggregated sensor value for sample window <NUM>. Thus, sample localization system <NUM> can obtain an aggregated sensor value for a geographic area (or sample window) <NUM> that is localized within the geographic area <NUM> corresponding to the aggregated sensor value A.

Action signal generator <NUM> can then generate an action signal based upon the corrected sensor value and/or the localized sensor value. Action signal generator <NUM> can generate a signal to store the corrected and/or localized sensor value in data store <NUM>. Action signal generator <NUM> can also generate an output to control system <NUM> which can be used to generate control signals to control the controllable systems <NUM> based on the corrected and/or localized values. Control system <NUM> can generate a control signal to control communication system <NUM> to communicate the corrected and/or localized value to other machines <NUM>, other systems <NUM> (which may, for instance include cloud systems such as a mapping system or other systems), etc. Control signal generator <NUM> can generate control signals to control operator interface mechanisms <NUM> to surface the corrected and/or localized value (e.g., display the corrected and/or localized value) to operator <NUM> along with the magnitude of any correction that has been applied, and along with any other information that is desirable. Planting control system <NUM> can generate control signals to control planting system <NUM> based on the corrected sample and/or localized value. For instance, planting control system <NUM> can generate control signals to control downforce actuators to control the downforce or upforce applied to a row unit <NUM> based upon the corrected and/or localized sensor value. Planting control system <NUM> can generate control signals to control seed metering system, the seed delivery system, or any of a wide variety of other controllable mechanisms in planting system <NUM>.

Machine application control system <NUM> can generate control signals to control machine application systems <NUM> based upon the action signal output by action signal generator <NUM>. For instance, material application control system <NUM> can control the valves or other actuators <NUM>, to control the timing and quantity of application of material based upon the corrected and/or localized sensor value. Look-ahead planting control system <NUM> can generate control signals to predictively control controllable systems <NUM>, such as to level a planting machine, to control the rate of seed delivery, etc., based on the corrected and/or localized sensor value. Tillage control system <NUM> can generate control signals to control tillage systems <NUM> based on the corrected and/or localized sensor values, such as to control tillage depth, soil distribution, etc. Control system <NUM> can also generate other control signals to control other machine systems <NUM> and other items <NUM> based upon the action signal generated by action signal generator <NUM> (which itself is based on the corrected and/or localized sensor value). By way of example, control system <NUM> can generate control signals to control the propulsion system of the towing vehicle, the steering system of the towing vehicle, or any of a wide variety of other machine systems <NUM>.

<FIG> and <FIG> (collectively referred to has <FIG>) show a flow diagram illustrating one example of the operation of agricultural system <NUM>. It is first assumed that signal processor <NUM> obtains data indicative of how the data from sensors <NUM> is to be sampled. This data can be referred to as the sampling parameters. Obtaining the sampling parameters is indicated by block <NUM> in the flow diagram of <FIG>. The sampling parameters may be stored in data store <NUM> or received as an input to signal processor <NUM> in other ways. The sampling parameters may include the sampling period (in terms of time or distance, etc.) as indicated by block <NUM> in the flow diagram of <FIG>. The sampling parameters may include any weighting or other filtering that is applied to the samples, as indicated by block <NUM>, or any of a wide variety of other parameters that indicate how the data is sampled, as indicated by block <NUM>.

The planting machine then begins to perform an operation (planting, tillage, material application, etc.), as indicated by block <NUM> in the flow diagram of <FIG>. Signal processor <NUM> then detects machine operation characteristics, as indicated by block <NUM>. Such characteristics can include, for instance, the direction of travel of the machine, as indicated by block <NUM>, and the travel speed of the machine, as indicated by block <NUM>. Signal processor <NUM> then detects location-specific values (represented by the values of the sensor signals generated by sensors <NUM>). Detecting location-specific values is indicated by block <NUM> in the flow diagram of <FIG>. There are a wide variety of different location-specific values that can be detected, some of which are described elsewhere herein and some of which may include the geographic location or position generated by position sensor <NUM>, as indicated by block <NUM>, fuel consumption <NUM>, ride quality <NUM>, the detection of seed skips or multiples as indicated by block <NUM>, material application characteristics generated by material application characteristic sensors <NUM> as indicated by block <NUM>, tillage characteristics <NUM> generated by tillage characteristic sensor(s) <NUM>, or any of a wide variety of other characteristics <NUM> (parameters, characteristics, conditions, etc.), or other items <NUM>. The sensors generate signals responsive to the sensed parameters, characteristics, conditions, etc..

Signal processor <NUM> can process the signals from sensors <NUM> simultaneously (e.g., in parallel) or serially. For purposes of the present discussion, it will be assumed that signal processor <NUM> processes one of the location-specific values generated by sensors <NUM> at a time. This discussion is provided for the sake of clarity only, and it is just one example. It will be understood that processing the sensor signals in groups, or in other ways, is contemplated herein as well.

Therefore, signal processor <NUM> selects a signal value to be processed, as indicated by block <NUM> in the flow diagram of <FIG>. Signal processor <NUM> then processes the selected signal, as indicated by block <NUM>. For instance, signal conditioning system <NUM> conditions the signal (such as by normalizing it, linearizing it, amplifying it, etc.), as indicated by block <NUM>. Sampling system <NUM> then obtains a sample value from the signal as indicated by block <NUM> and weighting/filtering system <NUM> performs any desired weighting or filtering of that sample, as indicated by block <NUM>. Aggregation system <NUM> aggregates samples (such as by adding them together, averaging them, etc.) as indicated by block <NUM> to obtain an aggregated sensor value and sample geospatial correlation system <NUM> assigns the aggregated sensor value to a geographic location within the field, as indicated by block <NUM>. Signal processor <NUM> can process the signal in other ways as well, as indicated by block <NUM>.

The present discussion proceeds with respect to the aggregated sensor values being corrected for aberrant sample values and the aggregated sensor values being processed to generate a sensor value that is localized to a geographic location within the geographic location represented by the aggregated sensor value. It will be appreciated that the aggregated senor value can be processed to either correct it or to obtain a localized sample value, but both correction and localization are described with respect to <FIG> for the sake of example only.

Therefore, correction system <NUM> analyzes the aggregated sensor value in order to perform any desired correction on that aggregated value. Geospatial comparison component <NUM> analyzes the geographic location corresponding to each of the samples used in generating the aggregated sensor value, to identify a relationship between those geographic locations. For instance, if the planting machine is traveling along a route <NUM> (shown in <FIG>), then geospatial comparison component <NUM> will identify the fact that the geographic location corresponding to the first sample used in generating the aggregated sensor value is furthest away from the geographic location that is assigned to the aggregated sensor value (which would correspond to a geographic location near aggregated sensor value A in <FIG>). However, if the planting machine has made a headland turn as shown in <FIG>, then geospatial comparison component <NUM> will identify that the geographic locations corresponding to the first sample and the aggregated sensor value A are relatively close to one another. These are just examples of the different relationships that can be identified by geospatial comparison component <NUM>. Identifying the geospatial correlation of sample values used to obtain the aggregated sensor value under analysis is indicated by block <NUM> in the flow diagram of <FIG>. The correlation can be generated based on the direction of travel <NUM> of the planting machine and the speed of travel <NUM>, and based on a wide variety of other items <NUM>.

Sample aberration identification component <NUM> identifies any aberrant sample values that were used to obtain the aggregated sensor value under analysis, as indicated by block <NUM> in the flow diagram of <FIG>. As discussed above, component <NUM> can identify aberrant sample values by comparing the sample values to threshold values. The threshold values may be based upon the other values used to generate the aggregated sensor value under analysis, or the threshold values can be obtained in other ways. Identifying aberrant values by comparing them to threshold values is indicated by block <NUM> in the flow diagram of <FIG>. Aberrations can be identified in other ways as well, as indicated by block <NUM>.

Sample correction component <NUM> then performs correction on the current aggregated sensor value under analysis based upon the geospatial correlation and the aberrant sample values that were used to make up the aggregated sensor value. Performing correction is indicated by block <NUM> in the flow diagram of <FIG>. The correction can be performed in any of a wide variety of different ways. For instance, the aberrant value can be replaced in the calculation of the aggregated sensor value by some of the other samples that are used to make up the aggregated sensor value. In another example, the aberrant value can be removed from the calculation or replaced by a default value or another value.

Sample localization system <NUM> performs localization to identify a sample corresponding to a geographic area that is within the geographic area corresponding to the current aggregated sample under analysis. For instance, sample isolation component <NUM> isolates the samples that were used to generate the current aggregated sensor value under analysis, as indicated by block <NUM> in the flow diagram of <FIG>. By way of example, sample isolation component <NUM> can identify the geographic location corresponding to each of the samples that were used in generating the current aggregated sensor value under analysis. Referring, for instance, to <FIG>, sample isolation component <NUM> identifies each of the samples <NUM>-<NUM> and the corresponding geographic location of each of the samples <NUM>-<NUM> along route <NUM>.

Sample window identification component <NUM> then identifies the sample window for which a new, localized sensor value is to be generated. For instance, referring again to <FIG>, sample window identification component <NUM> identifies sample window <NUM> as being the geographic area for which a new, localized sample is to be generated, within the geographic area <NUM> corresponding to the current aggregated sensor value under analysis. Identifying the sample window to be used is indicated by block <NUM> in the flow diagram of <FIG>. The sample window can be identified based upon an operator input or another user input, such as by a diagnostics display or otherwise. The sample window may be identified based upon an input from another automated or semi-automated system that desires to obtain a localized sensor value for the identified sample window <NUM>, in addition to, or instead of, the current aggregated sensor value for the sample window <NUM>.

Sample window re-aggregation component <NUM> then obtains the samples corresponding to the identified sample window (e.g., samples <NUM> and <NUM> in sample window <NUM> in <FIG>) and re-aggregates those values (e.g., averages them, weights them, or performs another type of aggregation) to obtain the localized sensor value corresponding to the identified sample window <NUM>. Generating the localized sensor value is indicated by block <NUM> in the flow diagram of <FIG>. Re-aggregating the samples within the identified sample window to generate the localized value is indicated by block <NUM>. It will be noted that the localized sensor value can be generated in other ways as well, as indicated by block <NUM>. The type of aggregation used to generate the localized sensor value may be specified by the user or system requesting the localized value. The localized sensor value may be generated using the same type of aggregation (albeit using fewer samples) used by aggregation system <NUM>, or a different algorithm that may be stored in data store <NUM>, or input in other ways.

Action signal generator <NUM> then generates an action signal based upon the corrected and/or localized sensor values, as indicated by block <NUM>. Action signal generator <NUM> can generate an output to control system <NUM> so control system <NUM> can generate control signals to control controllable systems <NUM>, as indicated by block <NUM>. Communication system <NUM> can be controlled to communicate the corrected aggregated sensor value and/or the localized sensor values to remote mapping systems or other systems <NUM>, as indicated by block <NUM>. Operator interface mechanism <NUM> can be controlled to surface the corrected and/or localized sensor value to operator <NUM> along with any other desirable information, as indicated by block <NUM>. Other controllable systems <NUM> can be controlled, and the information can be stored in data store <NUM> or output to other machines <NUM> and used to control or inform future operations, as indicated by block <NUM>. Other action signals can be generated as well, as indicated by block <NUM>. Until the operation is complete, as indicated by block <NUM>, processing reverts to block <NUM> where the machine continues to perform the operation and samples are continuously or intermittently detected and corrected and/or localized.

It can thus be seen that the present description describes a system that can be used to back out individual samples that are used to generate an aggregated sample of a sensor signal. The backed out samples can be analyzed to determine whether there are any aberrant values, and to determine how close those aberrant values were taken in time, or distance, to the geographic location assigned to the aggregated value. The aggregated value can then be corrected. The backed out samples can also be used to generate a localized value that is localized to a geographic location (or time) that is different from the geographic location (or time) assigned to the aggregated value. An action signal is generated based upon the corrected and/or localized value.

The present discussion has mentioned processors, processing systems, controllers and/or servers. In one example, these can include computer processors with associated memory and timing circuitry, not separately shown. The processors, processing systems, controllers, and/or servers 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.

Also, a number of user interface displays (UIs) have been discussed. The UIs can take a wide variety of different forms and can have a wide variety of different user actuatable input mechanisms disposed thereon. For instance, the user actuatable input mechanisms can be text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The mechanisms can also be actuated in a wide variety of different ways. For instance, the mechanisms can be actuated using a point and click device (such as a track ball or mouse). The mechanisms can be actuated using hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc. The mechanisms can also be actuated using a virtual keyboard or other virtual actuators. In addition, where the screen on which they are displayed is a touch sensitive screen, the mechanisms can be actuated using touch gestures. Also, where the device that displays them has speech recognition components, the mechanisms can be actuated using speech commands.

It will be noted that the above discussion has described a variety of different systems, components, sensors, and/or logic. It will be appreciated that such systems, components, sensors, and/or logic can be comprised of hardware items (such as processors and associated memory, or other processing components, some of which are described below) that perform the functions associated with those systems, components, sensors, and/or logic. In addition, the systems, components, sensors, and/or logic can be comprised of software that is loaded into a memory and is subsequently executed by a processor or server, or other computing component, as described below. The systems, components, sensors and/or logic can also be comprised of different combinations of hardware, software, firmware, etc., some examples of which are described below. These are only some examples of different structures that can be used to form the systems, components, sensors, and/or logic described above.

<FIG> is a block diagram of one example of the agricultural machine architectures, shown in <FIG> and <FIG>, where agricultural machine <NUM>, <NUM>, <NUM>, and/or towing vehicle <NUM> communicates with elements in a remote server architecture <NUM>. In an example, remote server architecture <NUM> can 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 examples, 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 in <FIG> and <FIG> as 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 in <FIG>, some items are similar to those shown in <FIG> and <FIG> and they are similarly numbered. <FIG> specifically shows that signal processor <NUM> and other systems <NUM> and data store <NUM> can be located at a remote server location <NUM>. Therefore, agricultural machine <NUM>, <NUM>, <NUM>, and/or towing vehicle <NUM> access those systems through remote server location <NUM>.

<FIG> also depicts another example of a remote server architecture. <FIG> shows that it is also contemplated that some elements of <FIG> and <FIG> are disposed at remote server location <NUM> while others are not. By way of example, data store <NUM> can be disposed at a location separate from location <NUM>, and accessed through the remote server at location <NUM>.

Regardless of where the items in <FIG> are located, they can be accessed directly by agricultural machines <NUM>, <NUM>, <NUM>, <NUM>, through a network (either a wide area network or a local area network), the items can be hosted at a remote site by a service, or the items 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 example, where cell coverage is poor or nonexistent, another mobile machine (such as a fuel truck) can have an automated information collection system. As the agricultural machine comes close to the fuel truck for fueling, the system automatically collects the information from the machine or transfers information to the machine 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 agricultural machine until the agricultural machine enters a covered location. The agricultural machine, itself, can then send and receive the information to/from the main network.

It will also be noted that the elements of <FIG> and <FIG>, 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> is one example of a computing environment in which elements of <FIG> and <FIG>, or parts of it, (for example) can be deployed. With reference to <FIG>, an example system for implementing some examples includes a computing device in the form of a computer <NUM> programmed to operate as described above. Components of computer <NUM> may include, but are not limited to, a processing unit <NUM> (which can comprise processors or servers from previous FIGS. ), a system memory <NUM>, and a system bus <NUM> that couples various system components including the system memory to the processing unit <NUM>. The system bus <NUM> may 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 to <FIG> and <FIG> can be deployed in corresponding portions of FIG.

The hard disk drive <NUM> is typically connected to the system bus <NUM> through a non-removable memory interface such as interface <NUM>, and optical disk drive <NUM> is typically connected to the system bus <NUM> by a removable memory interface, such as interface <NUM>.

The computer <NUM> is operated in a networked environment using logical connections (such as a local area network - LAN, or wide area network - WAN, or a controller area network - CAN) to one or more remote computers, such as a remote computer <NUM>.

<NUM> illustrates, for example, that remote application programs <NUM> can reside on remote computer <NUM>.

Claim 1:
An agricultural system, comprising:
a sensor (<NUM>) configured to sense a characteristic of an agricultural operation performed by an agricultural machine and to generate a sensor signal responsive to the sensed characteristic;
a signal processor (<NUM>) configured to aggregate a first plurality of samples of the sensor signal to obtain a first aggregated signal value;
a sample geospatial correlation system (<NUM>) configured to identify a first geographic location corresponding to the first aggregated signal value; characterized in that there is provided
a sample localization system (<NUM>) configured to generate a localized signal value based on a subset of the first plurality of samples, the sample geospatial correlation system (<NUM>) configured to identify a second geographic location corresponding to the localized signal value; and
an action signal generator (<NUM>) configured to generate an action signal based on the localized signal value.