U.S. patent application number 11/348866 was filed with the patent office on 2007-08-09 for method for recalibrating a material attribute monitor for a mobile vehicle.
Invention is credited to Noel Wayne Anderson, Stephen Michael Faivre, Mark William Stelford.
Application Number | 20070185672 11/348866 |
Document ID | / |
Family ID | 38335093 |
Filed Date | 2007-08-09 |
United States Patent
Application |
20070185672 |
Kind Code |
A1 |
Anderson; Noel Wayne ; et
al. |
August 9, 2007 |
METHOD FOR RECALIBRATING A MATERIAL ATTRIBUTE MONITOR FOR A MOBILE
VEHICLE
Abstract
A method for recalibrating a material attribute monitor for a
mobile vehicle includes accumulating an aggregate amount of
material from a plurality of material transfers; accumulating a
plurality of material attribute data sets via a series of data
transfers from at least one vehicle to another vehicle, wherein
each material attribute data set of the plurality of material
attribute data sets is associated with a corresponding material
transfer of the plurality of material transfers; measuring
aggregate material attributes of the aggregate amount of material;
and generating material attribute calibration data from the
accumulated plurality of material attribute data sets and the
measured aggregate material attributes.
Inventors: |
Anderson; Noel Wayne;
(Fargo, ND) ; Stelford; Mark William; (Sycamore,
IL) ; Faivre; Stephen Michael; (Kingston,
IL) |
Correspondence
Address: |
DEERE & COMPANY
ONE JOHN DEERE PLACE
MOLINE
IL
61265
US
|
Family ID: |
38335093 |
Appl. No.: |
11/348866 |
Filed: |
February 7, 2006 |
Current U.S.
Class: |
702/85 |
Current CPC
Class: |
A01D 41/1277 20130101;
A01D 41/1272 20130101 |
Class at
Publication: |
702/085 |
International
Class: |
G01D 18/00 20060101
G01D018/00 |
Claims
1. (canceled)
2. (canceled)
3. (canceled)
4. (canceled)
5. A method for recalibrating a material attribute monitor for a
mobile vehicle, comprising: accumulating an aggregate amount of
material from a plurality of material transfers; accumulating a
plurality of material attribute data sets via a series of data
transfers from at least one vehicle to another vehicle, wherein
each material attribute data set of said plurality of material
attribute data sets is associated with a corresponding material
transfer of said plurality of material transfers; measuring
aggregate material attributes of said aggregate amount of material;
and generating material attribute calibration data from the
accumulated plurality of material attribute data sets and the
measured aggregate material attributes for use in recalibrating the
material attribute monitor, wherein said plurality of material
transfers includes a plurality of initial material transfers from
at least one material harvesting vehicle to a first material
transfer vehicle, and at least one supplemental material transfer
by said first material transfer vehicle to a second material
transfer vehicle.
6. The method of claim 5, wherein: with said plurality of initial
material transfers from said at least one harvesting vehicle to
said first material transfer vehicle, each corresponding material
attribute data set of said plurality of material attribute data
sets is transferred to a first data transfer module on said first
material transfer vehicle; and with said at least one supplemental
material transfer by said first material transfer vehicle to said
second material transfer vehicle, each corresponding material
attribute data set of said plurality of material attribute data
sets in said first data transfer module is transferred to a second
data transfer module on said second material transfer vehicle.
7. The method of claim 6, wherein each of said first material
transfer vehicle and said second material transfer vehicle is one
of a material cart and a truck.
8. The method of claim 6, wherein: said plurality of material
transfers includes an additional material transfer from said second
material transfer vehicle to a sampling station; and with said
material transfer from said second material transfer vehicle to
said sampling station, each of said plurality of material attribute
data sets in said second data transfer module is transferred to a
third data transfer module at said sampling station.
9. The method of claim 8, wherein said sampling station is one of a
work site and a commercial material collection and distribution
center.
10. The method of claim 8, wherein said measuring aggregate
material attributes of said aggregate amount of material occurs at
said sampling station.
11. A method for recalibrating a material attribute monitor for a
mobile vehicle, comprising: accumulating an aggregate amount of
material from a plurality of material transfers; accumulating a
plurality of material attribute data sets via a series of data
transfers from at least one vehicle to another vehicle, wherein
each material attribute data set of said plurality of material
attribute data sets is associated with a corresponding material
transfer of said plurality of material transfers; measuring
aggregate material attributes of said aggregate amount of material;
and generating material attribute calibration data from the
accumulated plurality of material attribute data sets and the
measured aggregate material attributes for use in recalibrating the
material attribute monitor, wherein each receiver of a material
transfer contributing to said plurality of material transfers
includes a data transfer module, wherein with each successive
material transfer of said plurality of material transfers each
previously generated material attribute data set of said plurality
of material attribute data sets associated with said each
successive material transfer is transferred in a cascading fashion
to a next data transfer module on a material transfer vehicle that
receives said material transfer.
12. (canceled)
13. (canceled)
14. (canceled)
15. (canceled)
16. (canceled)
17. (canceled)
18. A method for recalibrating a grain attribute monitor for a
combine, comprising: monitoring grain attributes of harvested
grain; monitoring each transfer of said harvested grain that
contributes to an aggregate amount of grain loaded into a grain
transfer vehicle; generating a grain attribute data set associated
with said each transfer of said harvested grain; accumulating a
plurality of grain attribute data sets via a series of data
transfers, said plurality of grain attribute data sets
corresponding to said harvested grain and said each transfer of
said harvested grain; measuring aggregate grain attributes of said
aggregate amount of grain; and generating grain attribute
calibration data from the accumulated plurality of grain attribute
data sets and the measured aggregate grain attributes for use in
recalibrating the grain attribute monitor, wherein each receiver of
a grain transfer of at least a portion said harvested grain
includes a respective data transfer module, wherein with each grain
transfer of said plurality of grain transfers each previously
generated grain attribute data set of said plurality of grain
attribute data sets is transferred in a cascading fashion to the
data transfer module on a corresponding grain transfer vehicle that
receives said grain transfer.
19. The method of claim 18, wherein said each receiver is one of a
grain cart, a truck, and a sampling station.
20. The method of claim 19, wherein said sampling station is one of
a farmer's work site and a commercial grain elevator.
21. (canceled)
Description
FIELD OF THE INVENTION
[0001] The present invention relates to monitoring material
attributes, and more particularly, to a method for recalibrating a
material attribute monitor for a mobile vehicle.
BACKGROUND OF THE INVENTION
[0002] One type of material attribute monitor is a grain yield
monitor. Grain yield monitors require calibration to provide an
accurate record of grain yield and moisture by location across a
field. Calibration may be performed, for example, when field
conditions change, such as when moving between fields. This
involves operating the combine in the field while manually
collecting yield monitor data, weighing the harvested grain on a
scale, testing a sample for moisture content, and then applying a
correction based on actual grain attributes versus the sum of those
measured by the yield monitor. This approach has two major
drawbacks.
[0003] First, this approach is a time consuming process that
requires segregation of the grain by combine and manual recording
of which data are associated with the grain used in the calibration
process. In a word, the calibration procedure is inconvenient and
consequently does not get done as often as it should. Furthermore,
conditions may change within a field that should warrant a
recalibration of the yield monitor. Given the difficulty of
observing when recalibration should occur and the inconvenience of
recalibrating, it just doesn't get done.
[0004] Second, on large scale farms, it is not unusual to have
multiple combines, grain carts, and grain trucks simultaneously
operating in a field at a given time. The grain co-mingles from
different parts of the field as it moves from the field to the
trucks. For example, a typical Illinois corn field is 80 acres in
size with an average yield of 175 bushels per acre. The total
amount of grain in the field is then 80*175=14,000 bushels. The
combines have grain tanks of 100-200 bushel capacity. Grain carts
typically receive the grain from the combines, and have a capacity
typically in a range from 200-800 bushels. The grain carts then are
unloaded into grain trucks. Grain trucks are typically limited to
20,000 pounds/axel, so a four axel truck would have a maximum
weight of 80,000 pounds of which 20,000 are the truck itself. Corn
weighs about 60 lbs/bushel, so the truck can carry 60,000 pounds or
about 1000 bushels of corn. Thus, 14 truckloads of corn need to be
transported from the field.
[0005] Accordingly, providing segmentation of the grain for
calibration purposes by combine, grain cart, and truck has been
found to be inconvenient, and inefficient.
SUMMARY OF THE INVENTION
[0006] The invention, in one form thereof, is directed to a method
for recalibrating a material attribute monitor for a mobile
vehicle. The method includes accumulating an aggregate amount of
material from a plurality of material transfers; accumulating a
plurality of material attribute data sets via a series of data
transfers from at least one vehicle to another vehicle, wherein
each material attribute data set of the plurality of material
attribute data sets is associated with a corresponding material
transfer of the plurality of material transfers; measuring
aggregate material attributes of the aggregate amount of material;
and generating material attribute calibration data from the
accumulated plurality of material attribute data sets and the
measured aggregate material attributes.
[0007] The invention, in another form thereof, is directed to a
method for recalibrating a grain attribute monitor for a combine.
The method includes monitoring grain attributes of harvested grain;
monitoring each transfer of the harvested grain that contributes to
an aggregate amount of grain loaded into a grain transfer vehicle;
generating a grain attribute data set associated with the each
transfer of the harvested grain; accumulating a plurality of grain
attribute data sets via a series of data transfers, said plurality
of grain attribute data sets corresponding to the harvested grain
and each transfer of the harvested grain; measuring aggregate grain
attributes of the aggregate amount of grain; and generating grain
attribute calibration data from the accumulated plurality of grain
attribute data sets and the measured aggregate grain
attributes.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 is an exemplary material transfer diagram.
[0009] FIG. 2 is a block diagram of a data transfer module for use
in practicing a method of the present invention.
[0010] FIG. 3 is a diagrammatic representation of material
attribute data transfers in accordance with one embodiment of the
present invention.
[0011] FIG. 4 is a diagrammatic representation of material
attribute data transfers in accordance with another embodiment of
the present invention.
[0012] FIG. 5 is a flowchart of an exemplary method for
recalibrating a material attribute monitor for a mobile
vehicle.
DETAILED DESCRIPTION OF THE INVENTION
[0013] FIG. 1 is an exemplary material, e.g., grain, transfer
diagram for a given area, e.g., a field, represented by mobile
harvesting vehicles 10 and 12, e.g., a first combine CX and a
second combine C0; a first material transfer vehicle 14, e.g., a
grain transfer vehicle GTV1; a second material transfer vehicle 16,
e.g., a grain transfer vehicle GTV2; a third material transfer
vehicle 18, e.g., a grain transfer vehicle GTV3, and a sampling
station 20.
[0014] In the example of FIG. 1, grain transfer vehicles GTV1, GTV2
and GTV3 may be grain carts, trucks, or a combination thereof.
Sampling station 20 may be, for example, a farmer's work site,
e.g., a grain bin, or a commercial material collection and
distribution center, e.g., a commercial grain elevator. In the
example of FIG. 1, potential physical material, e.g., grain,
transfers are represented by solid arrows, dashed arrows and
dash-dotted arrows, which are used for convenience to demonstrate
numerous alternative material transfer paths. The direction of
material transfer is in the direction that a respective arrow
points.
[0015] Each of combines CX and C0 include a corresponding material,
e.g., grain, attribute monitor 22, 24, respectively, that generate
a material, e.g., grain, attribute data set for each load, e.g.,
grain tank, of material, e.g., grain, harvested. Each grain
attribute data set may include a plurality of data points, with
each data point including data corresponding to, for example, one
or more of grain volume mass, moisture content, impurities, cracked
seeds, protein, oil, starch, etc., for the particular grain tank
load of grain being transferred. Each data point may be associated
with geo-reference information, e.g., including a global
positioning system (GPS) location. Accordingly, the grain attribute
data set includes the locations, e.g., longitude/latitude, where
the grain was harvested.
[0016] As a more specific example, assume that the initial data
generated by material, e.g., grain, attribute monitors 22 and 24 of
combines CX and C0, respectively, is geo-referenced, such as by a
latitude and longitude, from a GPS receiver. Also, assume that
combine CX is operating in a field producing 200 bushels of corn
per acre, and that combine CX has a 200 bushel grain tank and a 40
foot wide combine header, and is traveling at 3 miles per hour
(about 176 sq ft harvested/second) and wherein one data point,
i.e., one geo-referenced grain attribute data record, is generated
per second. In this example, the grain attribute data set
associated with the full load of grain in the grain tank of combine
CX will have about 248 data points.
[0017] Co-mingling of material transfers, e.g., grain transfers,
occurs, for example, any time that one of the grain transfer
vehicles GTV1, GTV2 and GTV3 receives grain from multiple sources.
For example, each of grain transfer vehicles GTV1 and GTV3 may
receive grain from combines CX and C0. Further, an intermediate
transfer of grain may occur between grain transfer vehicles GTV1
and GTV3. Likewise, grain transfer vehicle GTV2 may receive grain
transferred from grain transfer vehicles GTV1 and GTV3.
Accordingly, one or more grain attribute data sets may be
transferred each time grain is transferred from one vehicle to
another.
[0018] Referring to FIG. 2, in order to keep track of grain
attribute data sets, a data transfer module 32 may be incorporated
into material attribute monitors 22, 24 of combines CX and C0, and
provided at each grain transfer vehicle GTV1, GTV2 and GTV3, and at
sampling station 20. Data transfer module 32 may be, for example, a
computer, and includes a processing device 34, a program storage
device 36, a data storage device 38, and a communication device 40.
Processing device 34 is communicatively coupled to program storage
device 36, data storage device 38, and communication device 40 via
communication links 42, 44, and 46, respectively. Communication
links 42, 44, and 46 may be established, for example, by a direct
cable or bus connection, or wireless connection.
[0019] Program storage device 36 stores the program instructions
used for operating data transfer module 32. Processing device 34
includes a microprocessor and associated memory for executing the
program instructions retrieved from program storage device 36.
Typical instruction sequences establish communication links with
one or more other data transfer modules via communication device
40, identify and authenticate the other module, and manage data
transfer to/from data storage device 38, including error detection
and correction (e.g., resending a set of data upon notification of
a failure), as well as optional encryption.
[0020] Communication device 40 is configured for bi-directional
communication, and includes a transmission link 48, with an
antenna, that facilitates wireless communication with an external
device, such as a combine grain attribute monitor, or another data
transfer module. Communication device 40 may operate, for example,
using short range wireless technology (e.g., Bluetooth or IEEE
802.11) or long range wireless technology (e.g., cell phone).
Communication device 40 may alternatively be in the form of a
removable storage device, such as a USB memory stick, compact flash
card, or other portable storage device which is physically moved to
transfer data.
[0021] In one embodiment, illustrated in FIG. 3, a plurality of the
data transfer modules 32, individually identified as CXDTM, C0DTM,
GTV1DTM, GTV2DTM, and GTV3DTM, may be used to cascade data from one
vehicle to a receiving vehicle, e.g., combine to grain cart to
truck, and ultimately to data transfer module SSDTM of sampling
station 20. In another embodiment, illustrated in FIG. 4, the
plurality of the data transfer modules 32, individually identified
as CXDTM, C0DTM, GTV1DTM, GTV2DTM, GTV3DTM and SSDTM, may be used
to send the data directly to a central data repository 50. Central
data repository 50 may be, for example, a computer having a
microprocessor with associated memory for providing processing
capability, and serves as a location where the accumulated data is
concentrated for grain tracking and recalibration. Those skilled in
the art will recognize that variants of these two data transfer
approaches may be used, in accordance with the principles of the
present invention. The embodiments illustrated in FIGS. 3 and 4
will be discussed in more detail below.
[0022] Referring to FIG. 5, there is shown an exemplary method for
recalibrating a material attribute monitor for a mobile vehicle, in
accordance with an embodiment of the present invention.
[0023] At step 100, an aggregate amount of material from a
plurality of material transfers is accumulated. As shown in FIGS.
1, 3 and 4, for example, grain transfer vehicle GTV2, such as a
truck, receives the aggregate amount of material, e.g., grain,
formed by a plurality of grain tank loads of grain supplied by one
or both of combines CX and C0, the material transfer being
represented by solid arrows. Grain transfer vehicles GTV1 and/or
GTV3, such as grain carts, may provide transfer of the grain to
grain transfer vehicle GTV2. Grain transfer vehicle GTV2 then
transfers the aggregate amount of grain to sampling station 20,
such as a grain bin or grain elevator.
[0024] At step S102, a plurality of material attribute data sets is
accumulated via a series of data transfers from at least one
vehicle to another vehicle.
[0025] Consider the example where the material is grain and each
material attribute data set is a grain attribute data set. Each
grain attribute data set of a plurality of grain attribute data
sets is associated with a corresponding grain transfer of the
plurality of grain transfers. Each grain attribute data set may
include a plurality of data points, with each data point including
data corresponding to, for example, one or more of grain volume
mass, moisture content, impurities, cracked seeds, protein, oil,
starch, etc., for the particular grain tank load of grain being
transferred. Each data point may be associated with geo-reference
information, e.g., including a global positioning system (GPS)
location. Accordingly, each grain attribute data set includes the
locations, e.g., longitude/latitude, where the grain was
harvested.
[0026] FIGS. 3 and 4 illustrate two embodiments representing the
operations of steps S100 and S102. Those skilled in the art will
recognize that hybrids of these two embodiments may also be
implemented.
[0027] In the embodiment illustrated in FIG. 3, each of combine CX,
combine C0, grain transfer vehicle GTV1, grain transfer vehicle
GTV2, grain transfer vehicle GTV3, and sampling station 20 includes
a respective data transfer module (DTM), such as data transfer
module 32 illustrated in FIG. 2, individually identified as CXDTM,
C0DTM, GTV1DTM, GTV2DTM, GTV3DTM and SSDTM, respectively. In this
example, the physical grain transfer is represented by solid arrows
and the associated data transfer is represented by dotted
arrows.
[0028] In the embodiment illustrated in FIG. 3, data is transferred
to follow the physical material, i.e., grain transfer from vehicle
to vehicle using, for example, a short range wireless
communications. Optionally, before grain and data is transferred,
the receiving vehicle may be required to identify and authenticate
itself. Identification and authentication may make use of RFID
tags. As grain is transferred to the receiving vehicle, the data
associated with the grain is also transferred. When data is
aggregated on the truck which goes to sampling station 20, e.g. the
elevator, for weighing and sampling, the grain data on the truck's
data transfer module is associated with the elevator data.
[0029] Several options exist for this step. For example, the data
on the truck may be transferred to a wireless access point at the
sampling station 20, e.g., elevator, where it is electronically
combined with elevator data and forwarded to where it will be
stored and analyzed. As another example, the elevator data may be
downloaded to the truck for combination with the load data, and the
combined load/elevator data may be transferred from the truck
wirelessly or with a data storage device to where it will be
analyzed. Also, the elevator data may be on a piece of paper and
later manually combined with the truck data via a keyboard
entry.
[0030] Combine CX and combine C0 may each include, for example,
yield, moisture, and other crop attribute sensors associated with
material attribute monitors 22, 24. The sensors are typically
mounted on the clean grain elevator that deposits newly harvested
grain on top of the grain that is in the combine grain tank. There
is a delay, e.g., about 10 seconds, between grain entering the
combine header and the yield or other attribute measurement that is
being made. An estimated compensation for this delay may be made,
if desired. Once the grain enters the grain tank, it is spread out
over the grain preexisting in the grain tank.
[0031] As illustrated in Fig, 3, the grain attribute data sets
cascade from one data transfer module (DTM) to the next DTM in
succession to follow the physical grain transfer from one vehicle
to the next. More particularly, as shown in the example of FIG. 3,
combine CX transfers a grain tank of grain to grain transfer
vehicle GTV1 and transfers the corresponding grain attribute data
set from data transfer module CXDTM to data transfer module GTV1
DTM. This process is repeated for each grain tank of grain that
combine CX transfers to grain transfer vehicle GTV1. Likewise,
combine C0 transfers a grain tank of grain to grain transfer
vehicle GTV3 and transfers the corresponding grain attribute data
set from data transfer module C0DTM to data transfer module
GTV3DTM. This process is repeated for each grain tank of grain that
combine C0 transfers to grain transfer vehicle GTV3.
[0032] Ideally, all the grain in the grain tank is emptied into the
grain transfer vehicle, e.g., grain cart, each time a transfer is
made. However, typically this is the exception rather than the
rule. Detailed modeling of grain entering the combine grain tank on
top and leaving through the auger at the bottom is a difficult bulk
material flow problem. Thus, a simple First-In-First-Out (FIFO)
assumption for grain and its associated data may be utilized. If a
model and processing mechanism to run the model are available, they
can be used in place of the FIFO assumption. A measurement device
on the combine auger (e.g., auger mass flow sensor) or in the grain
cart (e.g., a bulk material volume sensor) may be used to measure
the amount of grain that is transferred, and the data associated
with the transferred grain may be appended to the corresponding
grain attribute data set to generate a new grain attribute data
set, or a separate grain attribute data set specific to the present
grain transfer may be generated.
[0033] Grain transfer vehicle GTV1 later transfers its accumulated
grain to grain transfer vehicle GTV2, such as a grain truck, e.g.,
semi-tractor trailer, and data transfer module GTV1DTM transfers
its accumulated grain attribute data sets to data transfer module
GTV2DTM. Again, the ideal case is that the whole grain load is
loaded into the grain truck. However, again this case may be the
exception. A mechanism for measuring the amount of grain
transferred on the grain cart auger (e.g., auger mass flow sensor)
or in the grain truck bed (e.g., a bulk material volume sensor) may
be used to monitor the grain transfer. A material flow assumption
such as FIFO, or a more detailed bulk material flow model, may be
used to identify the grain transferred from grain transfer vehicle
GTV1 to the grain transfer vehicle GTV2. The data associated with
the transferred grain may be appended to the corresponding grain
attribute data sets, or a separate grain attribute data set
specific to the present grain transfer may be generated.
[0034] Likewise, grain transfer vehicle GTV3 later transfers its
accumulated grain to grain transfer vehicle GTV2, and data transfer
module GTV3DTM transfers its accumulated grain attribute data sets
to data transfer module GTV2DTM. Data transfer module GTV2DTM now
has the total accumulated grain attribute data sets for the
aggregate amount of grain loaded in grain transfer vehicle GTV2,
e.g., a truck. A material flow assumption such as FIFO, or a more
detailed bulk material flow model, may be used to identify the
grain transferred from grain transfer vehicle GTV3 to the grain
transfer vehicle GTV2. The data associated with the transferred
grain may be appended to the corresponding grain attribute data
sets, or a separate grain attribute data set specific to the
present grain transfer may be generated.
[0035] Grain transfer vehicle GTV2 later delivers, e.g., transfers,
its load of accumulated grain to sampling station 20, such as at a
farmer's work site, e.g., grain bin, or at a commercial grain
elevator. Also, data transfer module GTV2DTM transfers the total
accumulated grain attribute data sets for the aggregate amount of
grain loaded in grain transfer vehicle GTV2 to data transfer module
SSDTM of sampling station 20.
[0036] Not all grain goes directly from a field to the elevator or
other location for weighing and sampling. This other grain
typically goes to a grain bin. This step can be handled much as the
case of the grain cart, i.e., grain goes on top of material already
present. Typically, grain is removed from the bottom in a measured
fashion, such as by using an auger mass flow sensor. A simple
model, such as FIFO, or a more complex bulk material flow model,
may be used to identify grain and its data which is being
transferred from the bin to a truck for transport to an
elevator.
[0037] In the embodiment of FIG. 4, the physical grain transfers
are the same as described above with respect to the embodiment of
FIG. 3. The physical grain transfers are represented by solid
arrows and the associated data transfer is represented by dotted
arrows. However, in this embodiment, each time the grain is
transferred, the associated grain attribute data sets are
transferred to the central data repository 50. In other words, this
embodiment does not use inter-vehicle communications; but instead,
uses time and/or location stamping of transfer actions. For
example, geo-referenced data is collected about the harvested
grain. The grain goes into the grain tank on the combine. When a
grain cart comes along, the combine records the transfer of the
number of bushels of grain at a given time and/or location
interval. The grain cart records receipt of grain (amount unknown)
at a given time and/or location interval. When grain is transferred
from the grain cart to truck, the grain cart records a number of
bushels of grain that are transferred at a given time and/or
location interval. The truck records receipt of grain (amount
unknown) at a given time and/or location interval.
[0038] Each of the transfer records is either sent to central data
repository 50 using long range wireless communication, or
alternatively, may be delivered to central data repository 50 via a
portable data storage device and downloaded. Once all the
transaction data is together, a material flow model such as a FIFO
or a more detailed bulk material flow model may be used to identify
the grain transferred from the combine to the grain cart, and which
grain cart it was, based on the time and/or location of the
transfer. Each transfer of the grain is tracked in a similar
manner.
[0039] At step S104, aggregate material attributes of the aggregate
amount of material are determined by physical measurement. For
example, for each truck load of grain, aggregate grain attributes
(e.g., one or more of grain volume mass, moisture content,
impurities, cracked seeds, protein, oil, starch, etc.) may be
determined through load sampling and sample analysis. This load
sampling and sample analysis may occur, for example, at grain
sampling station 20, which may be, for example, at the farmer's
work site or at a commercial grain elevator. The aggregate grain
attributes, for example, may be represented as an electronic
record, or a written record.
[0040] At step S106, material attribute calibration data is
generated from the accumulated plurality of material attribute data
sets of step S102 and the measured aggregate material attributes of
the aggregate amount of material determined at step S104. In the
embodiment of FIG. 3, for example, data transfer module SSDTM may
execute program instructions for generating the material attribute
calibration data. In the embodiment of FIG. 4, for example, central
data repository 50 may execute program instructions for generating
the material attribute calibration data.
[0041] Again, consider the example where the material is grain and
each material attribute data set is a grain attribute data set. In
implementing step S106, equations and constraints are generated for
one or more truckloads of grain, carried for example by grain
transfer vehicle GTV2, generated by one or more combines CX, C0. As
an example, consider the bushels of grain in two truckloads
harvested by two combines CX and C0. Each grain yield monitor,
i.e., sensor, has a calibration factor associated with it for each
load. Thus: Y(truckload 1)=.SIGMA.(combine C0 yield data set 1)*
y(C01)+.SIGMA.(combine CX yield data set 1)* y(CX1) (Equation 1)
Y(truckload 2)=.SIGMA.(combine C0 yield data set 2)*
y(C02)+.SIGMA.(combine CX yield data set 2)* y(CX2) (Equation 2)
where: Y(truckload 1) is the total yield for the truckload 1;
[0042] Y(truckload 1) is the total yield for the truckload 2;
[0043] y(C01) is the yield calibration factor for combine C0 for
truckload 1 [0044] y(CX1) is the yield calibration factor for
combine CX for truckload 1 [0045] y(C02) is the yield calibration
factor for combine C0 for truckload 2; and [0046] y(CX2) is the
yield calibration factor for combine CX for truckload 2.
[0047] Accordingly, there are two equations with four unknowns:
y(C01), y(CX1), y(C02), and y(CX2) and the two equations have an
infinite number of solutions. Also, the grain attribute data sets
and grain from a combine going into a truck load may not be
contiguous. For example, in three contiguous harvest segments for a
combine, the grain from the first segment may go to a first grain
cart to a first truck. The grain from the second segment may go to
a second grain cart to a second grain truck. The grain from the
second segment may go to the first grain cart and then to the first
grain truck. Thus, many permutations are possible in the path from
the combine to a grain truck to form a truck load.
[0048] In this example, the similitude of context for the grain
making up the two truckloads (crop variety, soil conditions,
moisture conditions, etc.) is considered close enough that it can
be assumed that y(C01)=y(C02) and that y(CX1)=y(CX2). There are now
two equations and two unknowns which are easily solved using
algebra: Y(truckload 1)=.SIGMA.(combine C0 yield data set 1)*
y(C0)+.SIGMA.(combine CX yield data set 1)* y(CX) (Equation 3)
Y(truckload 2)=.SIGMA.(combine C0 yield data set 2)*
y(C0)+.SIGMA.(combine CX yield data set 2)* y(CX) (Equation 4)
[0049] The number of separate contexts and methods of solving for
calibration constants for those contexts will grow as experience
with the invention across crops and conditions grows.
[0050] At step S108, the material attribute calibration data, e.g.,
calibration factors, is applied, for example, to calibrate a
material attribute monitor, e.g., material attribute monitor 22
and/or 24, such as a combine's grain attribute (e.g., yield)
monitor, and/or to calibrate a grain attribute (e.g., yield) map of
the region of interest, e.g., a field. For example, the calibration
constants may be used as inputs to a computer to adjust individual
data for grain yield and other attributes used to generate field
maps of those attributes for the region of interest by the
computer. The material attribute calibration data may be
transferred to the material attribute monitor, for example, via a
wireless communication link, e.g., from data transfer module SSDTM
or central data repository 50; from a wired connection, e.g., from
a data transfer module DTM via a communication cable; or
alternatively, via a portable memory device.
[0051] With the method described above, material attribute monitor
recalibration may be performed automatically for material attribute
monitor recalibration and/or field mapping of grain attributes, and
may be performed on a "per truck load" frequency, in contrast to a
"per grain tank" frequency. Also, the method supports multiple, as
well as single, harvesting vehicles, e.g., combines, and multiple
material transport vehicles, e.g., grain carts, and trucks.
[0052] Those skilled in the art will recognize the principles of
the invention described above with respect to a specific embodiment
wherein the material being transferred is grain may be readily
applied to the harvesting of other materials, such as for example,
and not by way of limitation, cotton, alfalfa/grass, sugar
cane/beets, root crops, fruits and vegetables, saw logs, soil,
etc.
[0053] For example, in applications where the material is cotton,
each material attribute data set may include a plurality of data
points, with each data point including data corresponding to, for
example, one or more of mass and impurities. In applications where
the material is alfalfa/grass, each material attribute data set may
include a plurality of data points, with each data point including
data corresponding to, for example, one or more of mass, moisture,
and protein. In applications where the material is sugar
cane/beets, each material attribute data set may include a
plurality of data points, with each data point including data
corresponding to, for example, one or more of mass, moisture, and
sugar content. In applications where the material is root crops,
each material attribute data set may include a plurality of data
points, with each data point including data corresponding to, for
example, one or more of mass, tare dirt, and rocks. In applications
where the material is fruits or vegetables, each material attribute
data set may include a plurality of data points, with each data
point including data corresponding to, for example, one or more of
mass, temperature, ripeness, diameter, and bruising. In
applications where the material is saw logs, each material
attribute data set may include a plurality of data points, with
each data point including data corresponding to, for example, one
or more of diameter and length. In applications where the material
is soil, each material attribute data set may include a plurality
of data points, with each data point including data corresponding
to, for example, one or more of mass, and contaminants such as
hydrocarbons, radiation, etc. In each of these applications, each
material attribute data set includes the locations, e.g.,
longitude/latitude, where the material was harvested.
[0054] Having described the preferred embodiment, it will become
apparent that various modifications can be made without departing
from the scope of the invention as defined in the accompanying
claims.
Assignment
[0055] The entire right, title and interest in and to this
application and all subject matter disclosed and/or claimed
therein, including any and all divisions, continuations, reissues,
etc., thereof are, effective as of the date of execution of this
application, assigned, transferred, sold and set over by the
applicant(s) named herein to Deere & Company, a Delaware
corporation having offices at Moline, Ill. 61265, U.S.A., together
with all rights to file, and to claim priorities in connection
with, corresponding patent applications in any and all foreign
countries in the name of Deere & Company or otherwise.
* * * * *