Method and apparatus for culling directly on a harvesting apparatus

A combine culling apparatus for selecting product test samples has a novel culling apparatus mounted on a known combine or the like. The culling apparatus includes a collector, e.g., a cyclone, for receiving product, e.g., seeds, from the combine and a measuring compartment for receiving product from the collector. Soybeans, or other plants are arranged in a plurality of plots to be harvested. Test samples from these plots are sequentially collected by the apparatus. Test data pertaining to the plants is stored a computer system on the combine culling apparatus. Current data pertaining to a current test sample, e.g., such as yield, is collected on board the combine. The computer system on the combine selects whether to discard or to save a current test sample while on board the combine based on the current data and said stored test data. The method enables culling soybean lines and other crops directly on the combine by using a computer to calculate a check index from a set of check varieties compared to indexes of lines of interest.

BACKGROUND OF THE INVENTION 
This invention pertains to methods for culling plants and the like and, 
more particularly, to a method for culling soybeans. The present invention 
teaches a method and apparatus for culling plants, such as soybeans, etc., 
directly on a harvesting apparatus, such as a combine or other crop 
harvesting equipment. 
Soybean breeders typically make a cross between two varieties which are 
genetically dissimilar and advance the seed through generations of self 
pollination to a point were a single plant can be selected, which is, for 
the most part, homozygous at most loci and breeds true for the major 
traits of interest. Seeds from this plant and its subsequent generations 
are then tested for traits of interest including quantity and composition 
of protein and oil, resistance to pests (e.g., phytophthora root rot, 
soybean cyst nematode (SCN), brown stem rot, white mold, sudden death 
syndrome, stem canker, charcoal rot, etc.), appearance (e.g., lodging, pod 
load, and plant health), yield (usually by grain weight, volume of the 
grain or visual appearance), emergence (e.g., depth at which a line will 
emerge from or speed at which an individual emerges), nutrient deficiency 
or toxicity (e.g., iron deficiency chlorosis, zinc toxicity, maturity, 
height, molecular marker data (which are usually linked to one of the 
above traits of interest)), and other factors. The best individuals are 
kept and their progeny are tested in subsequent years until the few 
remaining lines are released for sale to farmers for growing. The most 
important factor is yield. Nevertheless, without considering the other 
factors, the variety may be undesirable or unusable. For example, without 
considering factors other than yield, the variety may not have the 
necessary protein or oil composition, or may produce unstable yields as 
pests attack the plants in different environments, or the plants may lodge 
so much that a farmer can not efficiently harvest the grain or finds that 
its appearance is objectionable. 
To select lines, soybean breeders grow the progeny of a plant from a bulk 
population in a row called a progeny row or in a hill called a hill plot. 
Several such plots are grown from a population (the progeny from any one 
cross), and the best are selected by visual selection or by some measure 
of yield. The measure of yield is referred to in this present disclosure 
as a progeny row yield test (PRYT). Visual selection based on physically 
observing, or looking, at each row, is very subjective and requires a high 
degree of artistic talent by the breeder. Many experienced breeders 
believe that this technique is inefficient. In either case, a yield test 
of some type will be conducted in subsequent generations. A breeding 
program may have hundreds of thousands of individual genotypes to evaluate 
each year. PRYTs and other yield tests are highly laborious. The labor and 
expense involved in cataloging, planting, note taking, harvesting, 
transporting, analyzing, culling, and discarding this large number of 
samples is very high. Another disadvantage of the current yield test 
approach is the time required for harvest, analysis, selection, and 
getting the seed ready for shipment for replant in winter nurseries. At 
present, the seed industry is extremely competitive with new genotypes and 
traits emerging each year. The first company or entity to incorporate 
these traits into new varieties and to bring them to market receives a 
competitive advantage. Many seed companies, utilize winter nurseries in, 
e.g., Chile, Argentina, and Puerto Rico. To conduct meaningful tests when 
growing in these countries, seeds must be delivered to a testing site 
within a couple of weeks after they are harvested. Each soybean research 
station can have, e.g., 20,000 or more samples. As a result, it has been 
impossible to analyze, select, and cull these samples in the requisite 
time period with conventional means without a critical year loss in cycle 
time. 
Current techniques include a crude culling technique wherein the yield of a 
sample, based on volume or weight of seeds, is compared to the yield of 
checks planted beside the same area as the samples of interest. The checks 
are harvested first, and a threshold value is marked on the cylinder or 
written down. All samples below the level of this threshold value are 
discarded. This technique has a number of disadvantages. In most crosses, 
the days required for soybean lines to mature varies. Lines taking longer 
to mature will usually have more growth and therefore out-compete earlier 
lines. Occasionally, the reverse can be true. Earlier lines can out-yield 
later maturity lines due to poor late season growing conditions or frost. 
For these reasons, very narrow maturity crosses are generally used with 
this technique. Another disadvantage is that although the checks may be 
planted in the general areas as the unselected lines, the size of the 
blocks may keep checks some distance from several lines that are selected. 
This means the checks do not adequately represent all the variation in the 
field, and the yield of the checks may therefore be unusable to compare to 
the unselected lines. One primary disadvantage of this technique is that 
selection is based only on yield and no other factors are taken into 
account. The price of the combines used to harvest these lines ranges from 
about $30,000 to well over $150,000. With the current method, a good 
operator can harvest one plot every 20-30 seconds and can do about 1000 
plots/day. A common rule of thumb used in the industry is that the midwest 
will have 10 days fit for harvest. Therefore, one can expect to harvest 
10,000 plots per year with each combine. 
Selection indexes have been used by several individuals in several crops to 
select lines after all data has been collected, but not to select as data 
is being collected on a harvest apparatus. Computer programs which can 
generate selection indexes exist, but these programs have not been used to 
make selections while the data is being generated. 
SUMMARY OF THE INVENTION 
The present invention overcomes the above-noted and other problems 
pertaining to existing culling methods. This invention provides a 
significant improvement over existing methods and apparatuses for culling 
plants and the like and, more particularly, for culling soybeans. The 
present invention enables, among other things, the culling of plants, such 
as soybeans, corn, sorghum, wheat, cotton, etc., directly on a harvesting 
apparatus. 
The present invention enables a substantial amount of lines to be discarded 
in the field. Preferably, as much as about eighty to ninety percent of the 
lines can be discarded in the field. The preferred embodiments of the 
present invention can enable most of the handling and culling to be 
eliminated, and the seed can be packaged directly in the field. The 
harvesting machine operator can work at a much higher rate and the harvest 
time can be greatly reduced. For example, the harvesting time can be cut 
in half, from about 20-30 seconds per plot to about 8-15 seconds per plot. 
According to a first aspect of the invention, a method of culling product 
directly on a harvesting apparatus includes: sequentially collecting test 
samples from a plurality of plots that are harvested; using a computer to 
calculate a threshold index from a set of check varieties in certain 
plots; using the computer to calculate an index of a current test sample 
at a current plot on board the harvesting apparatus; using the computer to 
select whether to discard or to save the current test sample while on 
board the combine based on a comparison of the current index and the 
threshold index. 
According to a second aspect of the invention, a method of culling test 
samples on a harvesting apparatus includes: designating a plurality of 
plots of product to be harvested; sequentially collecting test samples 
from the plots on a harvesting apparatus; storing test data pertaining to 
the product in a computer system on the harvesting apparatus; collecting 
current data pertaining to a current test sample at a current plot on 
board the harvesting apparatus; selecting with the computer whether to 
discard or to save the current test sample while on board the harvesting 
apparatus based on the current data and the stored test data. 
Additional aspects of this latter method, include, e.g.: 
a) designating a plurality of test areas having a plurality of rows and a 
plurality of tiers (ranges), each of the plots being at a specific row and 
tier; designating certain plots as checks; selecting whether to save or 
discard samples based on data collected at at least some of the checks, 
the stored test data including the data collected at the checks; and 
b) calculating a selection index value for each test sample based on at 
least some of the stored test data and the collected data, and the step of 
selecting including comparing the selection index for each test sample 
with a corresponding check index value that is based on the data collected 
at the checks. 
According to a third aspect of the invention, a harvesting culling 
apparatus for selecting harvested test samples is provided which includes: 
a harvesting apparatus; a collector for receiving product from the 
harvesting apparatus; a measuring compartment for receiving product from 
the collector; a first valve between an outlet of the collector and the 
measuring compartment; a second valve at a discharge end of the measuring 
compartment; and a computer system for 1) inputting parameters pertaining 
to a plurality of plots of product to be harvested, 2) storing test data 
pertaining to the product, 3) collecting current data pertaining to a 
current test sample at a current plot on board the harvesting apparatus, 
and 4) selecting whether to discard or to save the current test sample 
while on board the harvesting apparatus based on the current data and the 
stored test data. 
The above and other advantages, features and aspects of the present 
invention will be more readily perceived from the following description of 
the preferred embodiments thereof taken together with the accompanying 
drawings and claims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
The preferred embodiments of the present invention use check placement in 
plots, selection indexes, computer programs, and hardware to automatically 
select and cull samples directly on a combine or the like based on any 
number of traits or inputs. 
Lines are organized in tests small enough to be planted in a uniform 
environment, e.g., where the check variations adequately reflect the 
environmental variations. Preferably, entries are randomly distributed 
throughout the test areas, while checks are restricted to certain rows. 
The tests are preferably stacked next to each other (aligned with one 
another) so that the checks all align in the same sets of rows. This 
allows rows with checks to be harvested first and dumping of samples to 
begin as quickly as possible. Preferably, at least, about fifty to ninety 
percent of the lines are discarded in the field. As a result, most of the 
handling and culling is eliminated, and the lines can be packaged directly 
in the field. Because computers preferably control all relays, the 
operator OP can work even quicker and harvest time can be greatly 
reduced--e.g., approximately cut in half, from about 20-30 seconds per 
plot to about 8-15 seconds per plot. In addition, automatic bagging 
equipment, shown generally at AB in FIG. 1(C), can also be used which 
would allow continuous harvesting. A variety of automatic bagging systems 
are known and could be incorporated. 
One preferred embodiment of the invention is illustrated in FIGS. 
1(A)-1(D). As shown, the culling apparatus 1 includes a combine 10 or the 
like device. The combine 10 is a power operated harvesting machine that, 
e.g., cuts, threshes, and cleans grain, such as soybean lines 11. The 
combine 10 can include wheels 12, a driver's seat 13, a steering wheel 14, 
and a collector 15. The combine operates in a known manner. 
Cleaned grain from the combine 10 is supplied into a unique culling 
apparatus 20 which is supported on the combine. The culling apparatus 20 
preferably includes a) a collector 100 (preferably a cyclone) which 
receives product (e.g., seed) from the combine 10 by way of the input seed 
delivery duct 110, b) a measuring compartment 200 (preferably a cylinder) 
which receives product from the collector 100, c) a sensor 210 (preferably 
an ultrasonic sensor probe) which senses the depth of product in the 
measuring compartment 200, d) a valve V1 (see FIG. 1(D)) which allows 
grain to flow from the collector into the measuring compartment, and e) a 
valve V2 which opens to discharge product to a bagging location, to an 
automatic bagger, or to a discarding chute 230. 
The measuring compartment 200 is preferably at least partially transparent 
so the level of product therein is visible. Preferably, the compartment 
200 has horizontal indicia 215 so that the volume therein can be visually 
observed by the operator OP. The volume, or yield, of the grain can easily 
be calculated, e.g., by multiplying the depth of the grain by the base 
area of the compartment when the compartment has a constant vertical 
cross-sectional area or measured by ultrasonic sensors detecting matter 
and seed weights from load cell. Although a cylindrical cross-sectional 
shape is preferred, e.g., a measuring cylinder, other less preferred 
configurations and cross-sections of the measuring compartment can be 
used. 
The seed or grain is blown by air into the cyclone 100, leaving seed or 
grain inside the cyclone (to exit from the bottom) while air is discharged 
through the top outlet 120. Although a cyclone is the preferred type of 
collector 100, other collectors, such as various collecting tanks and 
product delivery means and the like can be used. As shown in FIG. 1(B), 
the cyclone is preferably supported via a support shaft 140. The cyclone 
has a bottom outlet that communicates with an inclined passage 150 that 
extends horizontally to the top of the measuring cylinder 200. The 
inclined passage 150 provides a suitable position above the measuring 
cylinder 200 for the placement of a sensor 210. As shown in FIGS. 
1(A)-1(C), the device can also include at least one saved bag container 
160 for holding saved test bags, etc., of seeds or grain. The container(s) 
160 can include, for example, an upper metal loop member 165 and a 
flexible bag body 166. 
The valve V1, best shown in FIG. 1(D), preferably includes a windshield 
wiper motor and a flapper valve which opens to allow grain to flow from 
the cyclone 100 to the measuring cylinder 200. And, the valve V2 
preferably includes a linear actuator and a knife valve which opens to 
discharge seed to an automatic bagger, to a discharge chute, etc. The 
valve V1 separates the cyclone 100 from the measuring cylinder 200, and 
the valve V2 closes the bottom of the measuring cylinder V2. As should be 
readily understood, it is contemplated that other known valve mechanisms 
can be used. Although computer controlled valves are clearly most 
preferred, it is even contemplated that manually operated valves could be 
used in alternate embodiments. As shown in FIG. 1(A), the discharge chute 
230 can be connected by a conduit 235 to a discharge bag 236 located, 
e.g., beneath the seat 13 of the operator. 
The combine apparatus 20 also includes a control system S, which, as shown 
in FIG. 1(A), preferably includes a control computer S100 and an analytic 
computer S200. As discussed further below, the control computer S100 is 
preferably an interface computer which controls relays (e.g., inside the 
relay box S300), senses switch closure (e.g., closure of a foot switch 
25), converts analogue signals to digital signals (e.g., such as signals 
from an ultrasonic sensor probe), and communicates with the analytical 
computer S200 via a serial interface, and the analytical computer S200 is 
preferably a computer which runs various programs in the operation of the 
apparatus, such as, e.g., a selection program, and houses data, such as, 
e.g., a harvest order file, and index files. The files and programs are 
preferably loaded in the analytical computer via a desk top computer (not 
shown), or can be entered into a keypad, etc., of the analytic computer 
itself. 
One exemplary arrangement of the control system is illustrated in FIGS. 
2(A)-2(C). FIG. 2(A) shows the control computer S100, as well as a 12V-24V 
DC to DC converter power supply 310. The DC to DC converter can be used 
to, e.g., power the ultrasonic sensor and the control computer. FIGS. 
2(B)-2(C) illustrate exemplary wiring of, e.g., the linear actuator relay 
and the windshield wiper relay which operate the respective valves V2 and 
V1. The relay box S300 contains relays for cycling the hoppers, via valves 
V1 and V2, and provides a connector to get power to the control computer 
and signals back therefrom (FIG. 2(B)). 
In a most preferred embodiment, manual overrides are provided in order to 
allow the operator to continue harvesting upon failure of the computer 
system. 
In summary, upon entering the field, the operator OP sets up the analytical 
computer S200 with the size of the area or block to be harvested and the 
proper range, row coordinates, and desired harvest options. The operator 
then harvests the first plot, and the first plot is stored in the cyclone 
100 until it is cycled down by the operator. The second plot is then 
harvested. The volume of grain is either a) keyed in by the operator 
(e.g., into a keypad S210 of the analytic computer) directly or b) sensed 
by the control computer (e.g., via the ultrasonic sensor 210) and passed 
to the analytical computer via a serial port. A selection index (see 
below) is then calculated using the information from data and index files. 
If the sample is a check, the data is recorded and the check selection 
index is updated (see below); the analytical computer then sends a signal 
back to the controller to dump the sample and cycle the next plot down 
from the cyclone. If the sample is not a check, selection is attempted 
based on the selection scheme for the test (see below). If insufficient 
checks have been harvested, or if the sample is above the calculated 
threshold, it is classified as a save and the operator is instructed 
(e.g., on a screen S220 of the analytic computer) to bag the sample (or 
the sample is sent to an automatic bagger). The control computer S100 then 
waits for the foot pedal switch 25 to be closed, and sends a signal 
indicative of this closure to the analytical computer S200. In turn, the 
analytic computer S200 signals the controller computer S100 to dump the 
sample and to cycle the next plot from the cyclone 100. If the sample is 
below a threshold value (see below), the sample is dumped, and the next 
sample is cycled from the cyclone. After sending the signal to dump or 
save a sample, the analytic computer advances itself to the next plot. 
Data from each sample is stored in a single record of a combine data file 
(.DAC) including the selection index. The analytic and control computers 
allow the operator to adjust the cycle to allow the most efficient 
harvest; relays are timed to open and close the valves V1 and V2 so that 
the last of the previous sample will be delivered to the cyclone as the 
next plot enters the combine. Then, just before the two would contaminate 
each other, the valve V1 at the bottom of the cyclone is closed. This 
process and the on board dumping greatly increase the speed of harvesting 
plots. 
Field Plan Setup: 
In order to use the present method and apparatus, a field area which is to 
be tested is set up, or arranged, appropriately. FIGS. 3(A)-3(C) 
illustrate three different examples of field areas to be harvested which 
include a plurality of plots arranged in rows and tiers (lines are 
arranged in rows). The colored plots represent checks. As shown, the field 
area includes a plurality of test areas, which include corresponding 
checks (three checks are shown for each test area). In FIGS. 3(A)-3(C), 
the order of harvest is indicated by the pass number, and the direction of 
travel is indicated by the arrows illustrated. 
In creating an appropriate field plan set up, the analytical computer S200 
can be used to perform the following. First, files can be made which have 
one record entry for each line. Database records also work. Second, files 
can be made with one record for each check entry. Third, a first program, 
PRYTGEN, can be used to combine the above files so that check entries are 
properly positioned in the field. Each check is denoted with, e.g., a 
".about.". This is a rarely used character so that subsequent programs can 
identify the checks by the ".about." in the proper position. As shown in 
FIGS. 3(A)-3(C), a variety of different field arrangements are possible. 
For combines which cannot fit between passes, the checks can be placed on 
the outside pass, e.g., such as shown FIG. 3(B). For those who prefer to 
harvest all checks first so that all lines are subjected to selection, the 
checks can be placed, e.g., in the first three positions on the outside 
pass as shown in FIG. 3(C). A preferred method is to distribute the 
checks, e.g., to place the checks on the second, middle, and next to last 
passes to better sample the variation, e.g., such as shown in FIG. 3(A). 
It should be understood that other arrangements and placements of checks 
could be used, and more or less checks could also be used for each test 
area. Fourth, a second program, such as SAS, AgriBase or MSTAT can be used 
to generate the field planting plans assigning tier (or range) and row 
coordinates and to set up files needed for notes. From these plans, tests 
and blocks are set up and planted. Other variations of the method and 
apparatus for performing the field plan setup, which would be understood 
by those in the art, can also be used. 
Data Entry: 
As discussed below, the present invention can make selections based on 
analyses, etc., of various data. Data can be, for example, entered into 
the analytical computer S200. Data entry can involve, e.g., the use of a 
third program, DLBOOK, that runs on a hand-held computer or a desk top 
computer. This third program can be used to enter data into data files 
called ".DAT" files. 
Data can come from the field, greenhouse, molecular data, or can be edited 
from other sources using a standard text editor such as Kedit, SAS, 
Microsoft Excel or Word. Data can come from, e.g., on board testing (e.g., 
such as via an ultrasonic probe), on board entering of data, or 
pre-entering of data. The following descriptions will clarify the various 
types of data that can be stored and the various places such data can come 
from. 
Selection Methods And Selection Indexes: 
Selection of culling samples is conducted by comparing samples with the 
checks. "Selection indexes" of the samples are compared with corresponding 
indexes of the checks. As discussed below, there are a variety of 
"selection indexes" that can be used and a variety of methods for 
selecting samples, e.g., by comparing indexes, that can also be used. 
Based upon this comparison, among other things, an operator can determine 
whether to discard the sample or to save the sample, while on the 
harvesting apparatus. 
In one exemplary embodiment, the analytical computer S200 can run a fourth 
program, PRYTSET, a desk top program, which sets up selection criteria by 
the number of traits and a weighting of each trait used in the selection. 
The program can use, e.g., one or more of the following five methods of 
selection: (1) a mean of the checks method, (2) a high check method, (3) a 
low check method, (4) a save all method, or (5) a save the top number of 
selected lines method. If one of the first three methods is used, the 
program prompts for a threshold value of the checks (a percentage of the 
checks will be discarded, i.e., in relation to the mean, the highest, or 
the lowest check). The program then finds the position of all the checks 
by looking for the `.about.` in the proper column. For each block, an 
index (.IND) file is created which has one record for each test. Each 
record contains all the above information entered. Therefore, selection 
criteria can be varied with each test. 
As noted, selection can be based on several methods and, in one preferred 
embodiment, the programs presented here accommodate five methods: 1) the 
save all method; 2) the high check method; 3) the low check method; 4) the 
mean of the checks method; or 5) the save the top number of selected lines 
method. Each experiment within a block can use any of these methods, 
independently of the selection scheme used in neighboring tests. 
(1) The save all method is the simplest method wherein all entries are 
saved regardless of their index values. This method is usually used for 
tests in which all entries are saved for study. 
(2) The high check method based on the index value of the highest check can 
also be useful. Selection can begin as soon as the first check within a 
test is harvested. Each subsequent entry harvested within that test will 
be compared to that line until a new check is found which has a higher 
index value. At which time, this new check is used to set the selection 
criteria. 
(3) The low check method is probably the most conservative. All checks must 
be harvested before selection can begin. The one with the lowest index 
value is used to set the selection criteria. That is, within a certain 
test area, all three checks must be harvested before dumping begins. 
(4) The mean of the checks method is the one anticipated as being most 
commonly used in this invention. As with the low check method, selection 
begins after all checks are harvested. The mean of the checks index values 
is used to set the selection criteria. This can provide a more stable 
value because bad check plots make up only about 1/3 of the total. The 
mean also appears to help in selecting across a range of maturity, as long 
as the lines to be selected are within the same range as the checks. 
(5) The save the top number of selected lines method involves ranking 
entrees based on their index values. If the index value is not within the 
top number of entries specified, it is discarded. This method will likely 
save more lines than are specified because lines saved at first will 
likely later fall below the top spots after additional lines are 
harvested. This method begins when the selected number of entries has been 
harvested. 
It should be apparent that a variety of other selection methods could be 
utilized. For example, another method could include dumping after a 
certain total number of saves. For the selection methods 2-4, an 
additional parameter is preferably used to select a percentage of the 
index value which the entry must beat to be saved. This figure is 
expressed as a decimal--e.g., if only those lines which were 110% the 
value of the mean of the checks were selected, then this would be set at 
1.10. Then, only those entries with index values that were 10% greater 
than the mean of the checks index value would be saved. 
Selection indexes can be calculated so that several factors can be 
accounted for simultaneously when selecting individuals from a population. 
The index value can be the sum of the factors which a breeder considers 
important weighed by a coefficient which the breeder believes expresses 
the relative importance of that trait. Weighting coefficients can also be 
used to equalize differences between two factors due to differences in the 
units of measure. For example, if a breeder desires to breed for high % 
protein (P), % oil (O) and yield (Y), protein may be twice as important as 
oil. Accordingly, he may weight protein by a coefficient of 2, while 
leaving the oil coefficient at one and putting the yield coefficient at 
0.75. The selection index is then calculated by summing each factor by its 
coefficient weighting factor as follows. 
EQU Index value=(2*P)+(1*O)+(0.75*Y). 
If individual A has 42% protein, 18% oil, and yields 55 bushels/acre, then 
individual A's index value=(2*42)+(1*18)+(0.75*55)=84+18+41.25=143.25. 
If individual B has 41% protein, 19% oil, and yields 56 bushels/acre, then 
individual B's index value=(2*41)+(1*19)+(0.75*56)=82+19+42=143. 
Individual A has the higher index value and would be the better line to 
save under this selection index even though it had a lower yield and less 
oil. 
Operation: 
A detailed, step-by-step, summary of the operation of one exemplary 
embodiment of the invention is as follows: 
1. Upon entering the field, the operator OP initializes the analytical 
computer S200 with the following parameters: 
a) First and last tiers to harvest. 
b) First and last rows to harvest. 
c) Current tier-row. 
d) Direction of travel (tiers increasing or decreasing). 
e) Row start harvest option. 
1) One way--each row begins with the same tier and the cutting direction is 
always as specified in d. 
2) Both ways--each row begins on the last tier in the previous row 
harvested and the cutting direction specified in d is alternated with each 
row. 
f) Delay time #1 and delay time #2 in seconds. 
2. The operator then harvests the first plot, and the cleaned grain is 
blown into the cyclone 100. 
3. The operator then cycles the seed down to the measuring cylinder 200 by 
pressing a switch (e.g., a foot switch 25 in the preferred embodiment) 
which manually runs valve V1. 
4. The operator then enters the automatic harvest cycle on the analytical 
computer S200. Upon entry into this part of the program, the analytical 
computer S200 sends a signal to the control computer S100 which triggers 
it to sense the depth of grain in the measuring cylinder 200 using the 
sensor 210 (preferably an ultrasonic probe). The probe 210 provides an 
analog current that is proportional to the depth of grain in the measuring 
cylinder. The control computer S100 then converts this analog signal to a 
digital signal and passes it to the analytical computer S200 via serial 
ports on both computers. The program also allows the depth of grain to be 
keyed directly into the keypad S210 of the analytical computer S200 by the 
operator, such as by viewing indicia 215. 
5. The second plot can be harvested while step 6 below is conducted; step 6 
can also begin earlier, etc., as appropriate. 
6. From the data collected by the ultrasonic probe and the on board data 
from the data file(s), a selection index is calculated (as discussed 
above). 
a) If the sample is a check, the data is recorded and the selection index 
is updated. This check sample is then classified as a dump, and the 
program proceeds to step 7 below. 
b) If the sample is not a check, the program checks to see if the required 
number of checks have been harvested for the test. If so, selection is 
attempted based on the method of selection under the criteria selection 
setup discussed above. If not, the sample is classified as a save. 
1) If the calculated index is above the threshold value calculated from the 
checks, it is classified as a save. 
2) If the sample is below the threshold value, it is classified as a dump. 
7. A record is then written to the DAC file containing the following 
information. 
a) The tier and row. 
b) A bar code designation (e.g., samples can be bar coded, such as if an 
automatic bagger is used). 
c) The selection index value. 
d) The measuring cylinder reading. 
e) The save value (0=dumped, and 1=saved). 
8. If the sample is classified as a dump, step 10 below automatically 
starts. 
9. If the sample is classified as a save: 
a) the sample can be sent to an automatic packager equipped on the 
apparatus; or 
b) the operator can be instructed to bag the sample. For example, when the 
operator presses a foot pedal 25, indicating that he is ready to catch the 
sample, the control computer S100 can sense the switch closure and can 
send a signal to the analytical computer S200 indicating that a foot pedal 
switch has closed. 
10. The following sequence then occurs. 
a) The analytical computer S200 sends the delay time #1 and the delay time 
#2 to the controller. 
b) The controller computer S100 then: 
1) opens the valve V2; 
2) waits the specified delay time #1; 
3) closes the valve V2; 
4) uses the ultrasonic probe 210 to sense the depth of the cylinder 200 
without grain; 
5) opens the valve V1; 
6) delays the specified delay time #2; 
7) closes the valve V1; 
8) uses the ultrasonic probe 210 to sense the depth of the grain in the 
cylinder 200; and 
9) relays information to the analytical computer S200. 
c) The controller computer S100 then advances one tier. 
d) After step 10)b)2) above, the operator begins to harvest the next plot. 
If the sample is a save, and if no automatic bagger is used, then the 
operator holds the bag of seed as the next plot is harvested. Then, after 
the last plant enters the combine and while the combine is threshing and 
cleaning the grain, the operator ties a label on the bag with the sample 
from that prior plot and puts the bag in the save bin 160. 
11. After step 10, the process is repeated at step 6 for the new plot. 
12. After a record of the data is written to the DAC file in step 7, the 
tier is incremented, or decremented depending on direction of travel. If 
the resultant tier is outside the range specified in step 1. Then, the row 
is incremented to the next row specified in the harvest row order file, 
and the tiers are reset according to the condition of the parameter set in 
steps 1 a, d, and e. The cycle is then repeated at step 6. If the last row 
has been harvested, the computer prints, e.g., "That's all" and exits the 
program, closing all files. 
The analytical computer S200 and the control computer S100 allow the 
operator to adjust the cycle to allow the most efficient harvest. Relays 
are timed to open and close so that the last of the previous sample will 
be delivered to the cyclone 100 as the next plot enters the combine, and, 
then, just before the two would contaminate one another, the valve V1 
closes. The computer control of the valves V1 and V2 and the onboard 
dumping of the samples, greatly speeds up the rate at which plots can be 
harvested--even over systems where no data is recorded. With this system, 
preferably, at least about 80-90% of the samples are dumped in the field. 
Before this system, a new, untrained, operator could harvest about 500 
plots/day and could possibly advance to about 1000 plots/day within a 
couple of weeks. With this new system, a new operator can harvest 1000 
plots/day and could even obtain 1500 plots/day or more. 
It should be understood that the control system or computer system S can, 
alternatively, have a single computer for performing the functions of both 
the analytical and control computers, or, alternatively, additional 
computers or processors could be added for certain functions. In addition, 
the harvesting apparatus can include remote communication means for 
communicating to a computer at another location, which other computer can 
perform certain functions. 
Further Analyses: 
In the present invention, all data can be recorded (e.g., in the analytical 
computer) so that further analyses are possible--unlike other culling 
systems. 
The invention preferably includes a sixth program, SAS, that merges 
information from several files together including, for example, pedigree, 
index values, raw cylinder readings, molecular data, maturity, lodging, 
etc. The invention preferably also includes a seventh program, using SAS, 
that divides tests within a block file into individual test files. The 
invention preferably also includes another program using SAS, that 
calculates analysis of variance and means. 
Exemplary Embodiments: 
A number of exemplary embodiments of the invention are discussed in the 
following paragraphs. 
EXAMPLE 1 
Soybean cyst nematodes (SCN) are pests which cause the greatest loss of 
yield in soybeans. The most economical way to protect varieties from these 
pests is to obtain resistant varieties. SCN resistance is complicated and 
involves multiple loci. In the northern United States, SCN has not been 
viewed as a significant problem, but it is now becoming very prevalent. 
Therefore, the source of resistance must come from southern varieties 
where varietal development of SCN lines is more advanced or from plant 
introductions. Either of these are poorly adapted for the northern soybean 
growing areas. As a result, progress to bring high yielding SCN resistant 
cultivars to market has been slow. 
The following selection index was used to select high yielding lines and 
yet give a slight edge to those varieties which confirmed SCN resistance. 
INDEX=Yield+(-0.7)*Maturity+(-2)*General appearance rating+20* SCN rating, 
where, in this example: 
Yield: is a sample's volume as given by a 2" diameter cylinder with a scale 
of 0-100. 
Maturity: is given in 1/3 maturity groups--i.e., 16,19, 23, 26, 29, etc. A 
value of a 16 would represent a mid maturity group 1 variety, and a value 
of 23 would represent an early maturity group 2 variety. The effect of 
using different coefficients can be seen below. In the chart below, all 
cylinder readings within one column are regarded as equal after maturity 
correction by the factor given for that column. 
______________________________________ 
Correction Factor 
0.7 1.0 1.2 
______________________________________ 
Maturity 9 59 50 44 
13 62 54 49 
16 64 57 52 
19 66 60 56 
23 69 64 61 
26 71 67 64 
29 73 70 68 
33 76 74 73 
36 78 77 76 
39 80 80 80 
Cylinder Readings Corresponding To Maturity 
And Maturity Correction Factors 
______________________________________ 
FNT General appearance rating: is an appearance rating on a scale from 1-5 
where 1 is extremely attractive and 5 is very ugly and usually prostrate. 
FNT *SCN rating: is a rating of 1 if the sample is resistant or moderately 
resistant and a 0 if it is susceptible. 
This data was generally generated by a greenhouse screen after planting. 
All checks were given a "0" rating. Therefore, the last term will be "0" 
except for resistant lines. A value of 20 is added to the index value of 
resistant lines to ensure that they are saved if the other factors bring 
the index value close to the threshold value. This technique also helps 
select high yielding samples without resistance in SCN populations because 
this part of the expression will be zero and will not enter into the 
calculations, therefore, they are not penalized when compared to the 
susceptible checks. The maturity coefficient (-0.7) was calculated using 
regression on advanced yield plots, and has worked well, and it has been 
used for the maturity coefficient to select earlier lines. 
The general rating coefficient value of (-2) works well for most 
populations where most unattractive lines should be removed. Values of, 
e.g., (-1) or (0) can also be used in populations where less attractive 
lines could be tolerated. 
EXAMPLE 2 
This example has utility in, e.g., selecting individuals used in genetics 
studies, especially a mapping population. In this example, putative DNA 
markers have been correlated with quantitative linked loci (QTL) for 
higher yield. To test these markers, individuals are desired which have 
the markers in the favorable condition to compare to those that have the 
markers in the unfavorable condition. 
Before planting, nothing is known about the population except that the 
parents differ in QTL composition so that the progeny will segregate for 
the markers of interest. After planting and yield tests in the 
configuration given above, DNA is taken from the leaves and tested for the 
markers associated with the QTL's. From the DNA work, several individuals 
are identified which have the marker in the favorable and unfavorable 
condition. Individuals at both extremes of marker condition are 
selected--both must be harvested to test this theory in future years. Data 
files are created which have the tier and row coordinates for each of the 
individuals. Data are then added from the molecular markers and from field 
observations including maturity and general rating. For the molecular 
data, a "1" is entered if the individual should be saved based on it's 
molecular data alone, and a "0" is entered otherwise. The checks all 
receive "0" s. 
The selection index is then generated using the PRYTSET program. The 
following 4 traits are involved: 
1. Yield: which is measured from the depth of grain in the cylinder (Y); 
2. Maturity: which is in 1/3 maturity groups (MA); 
3. General appearance: rating at harvest (G); 
4. Molecular: save 1, or 0 (MO); 
The weighting is as follows: 
Y is weighted as 1; 
MA is weighted as -0.7; 
G is weighted as -2; and 
MO is weighted as 200. 
In this example, the only selected lines were 110% of the mean of the 3 
checks. 
Therefore, the index value (1*Y)+(0.7*MA)+(-2*G)+(200*MO). Y is a number 
between 0 and 100. MA is a number between 03 and 56. G is a number from 1 
to 5. MO is a 0 or 1; MO for checks will always be 0. 
For checks and non-marker selected lines (where MO=0), the first 3 terms 
are the only ones which enter into the equation, and these lines are 
selected based only on these three factors. For marker selected lines 
(MO=1), the fourth term will equal 200. Because the range of values for 
the first 3 terms is between a minimum value of -49.2 and a maximum value 
of 95.9, the possible range of values for lines with MO=1 is from 150.8 to 
295.9. The largest mean of the checks value is 95.9. Therefore, the 
greatest selection index threshold is, e.g., 110% of 95.9, or 105.5, which 
is less than any possible values for MO=1 lines. Therefore, all MO=1 lines 
are automatically selected. 
EXAMPLE 3 
Use of the system where molecular marker data is known. The present 
assignee has developed systems to identify marker alleles which are linked 
to yield QTLs, and, from regression, the applicants have assigned these 
loci regression coefficients values that denote their importance in yield. 
Parents are selected which, when combined can accumulate as many of these 
alleles as possible (Table 1). 
TABLE 1 
______________________________________ 
Locus 
Locus Name 
1 2 3 4 5 6 7 8 9 10 
______________________________________ 
Parent A 1 1 1 0 1 0 1 0 1 1 
Parent B 1 0 1 1 1 0 1 1 1 1 
Ideal Progeny 
1 1 1 1 1 1 1 1 1 1 
Regression 
4.2 2.1 3.1 3.4 2.2 .2 6.2 12 1.0 1.2 
Coefficient 
______________________________________ 
The value "1" at a locus denotes that the favorable allele is present. 
While, a "0" indicates that the unfavorable allele is present. In the 
above example, loci 1, 3, 5, 7, 9, and 10 are said to be fixed in the 
favorable condition and will not segregate. Locus 6 is fixed in the 
unfavorable condition and also will not segregate. Loci 2, 4, and 8 will 
segregate. Crosses are made between parent A and B and the progeny are 
selfed by single pod, single seed, or some other method to a point where a 
large number of loci have been fixed. At this point, individual plants are 
harvested and the seed from each plant is used to plant one plot. Plants 
originating from the same or different parents are placed in a test along 
with the standard checks. These plots are arranged and planted as has 
previously been described. Leaf samples are taken from these plants at an 
early stage of development. DNA is extracted from these samples. The DNA 
is subjected to markers which identify loci 2, 4, and 8 which the parents 
do not have in common. The samples are scored for presence or absence of 
the marker allele. Examples are given in Table 2. 
TABLE 2 
______________________________________ 
Scores of Plant Rows for Selected Loci 
Locus 
Plant Row # 2 4 8 
______________________________________ 
1001 1 1 0 
1002 1 0 1 
1003 1 1 1 
1004 0 1 0 
1005 0 0 0 
1006 1 1 1 
1007 1 0 1 
1008 0 0 1 
1009 1 0 0 
______________________________________ 
A table or array is constructed similar to Table 2 in which rows represent 
the plot or individual plant row and the columns are the marker loci. For 
loci which have 2 alleles, the value in a cell will be a "1" if that 
individual has the favorable marker locus allele and a "0" if the marker 
locus is unfavorable. This information is then merged with agronomic 
information such as maturity and appearance. 
TABLE 3 
______________________________________ 
Scores of Plant Rows for Selected Loci 
Locus 
Plant Row # 
Maturity Appearance 
2 4 8 
______________________________________ 
1001 23 1 1 1 0 
Check 1 19 1 1 0 0 
1002 26 3 1 0 1 
1003 23 3 1 1 1 
1004 29 4 0 1 0 
Check 2 25 2 0 1 1 
1005 19 2 0 0 0 
1006 26 2 1 1 1 
1007 23 2 1 0 1 
1008 23 3 0 0 1 
Check 3 29 2 1 0 0 
1009 29 2 1 0 0 
etc. 
______________________________________ 
The resultant file will be called a data file, and it has one record for 
each plant row or check which is in the same order as the plants are in 
the field. Program PRYTSET can then be used to develop a selection index. 
Each locus is weighed by the coefficient calculated for that locus. This 
is illustrated in the following. Six traits, e.g., will be used in the 
selection index. Trait 1 is the yield value, which is obtained at harvest 
in the field, and which will have a multiplier of 1. Trait 2 is the 
maturity, which will have a multiplier of (-0.7). Trait 3 is the 
appearance, which will have a multiplier of (-2). Traits 4-7 will be loci 
2, 4, and 8. They will have multipliers 2.1, 3.4 and 12, respectively. 
Accordingly, the following formula expresses the index value for each 
line. 
Index value=(1*yield value)+(-0.7*maturity)+(-2*appearance)+(2.1*locus 2) 
+(3.4*locus 4)+(12*locus 8). 
The techniques of the present invention can easily be applied to other 
traits and crops with little or no modifications. To use molecular data 
for yield, an additional term is added to the index for each locus. They 
are weighted by their importance from an allele value determination 
calculation. Data files would have a "0" if the individual does not have 
the desired allele or a "1" if it has the desired allele. In this way, the 
value of the locus is added to equation only if the allele is present at 
that locus. Loci coefficients could also be used as in the SCN example if 
they were linked to disease resistance genes, or any disease ratings could 
be used as the general rating or maturity. 
With the technique of the present invention, there are no limitations as to 
where data can come from. Multiple inputs and/or multiple stored data can 
be combined into the selection index. Inputs could be from, e.g., such 
devices as global positioning (GPS) devices, digitized aerial photographs, 
moisture probes, load cells, soil probes, near infrared (NIR) devices, and 
other sources. 
Global positioning devices could be used to correct yield for variation in 
field productivity level. Productivity maps of the field can be stored in 
the computer and then accessed from information provided from the GPS 
unit, and then the trait of interest can be corrected. Soil probes could 
be used in a similar manner. Aerial photographs could be taken as the 
crops mature and scanned into the computer. The color of the plots as they 
mature could be used to calculate maturity, and GPS or placement in the 
field could be used to calculate maturity of plots. GPS units could be 
used to assign latitude and longitude coordinates to tract plots replacing 
tier row coordinates. NIR devices could be used to select lines with 
higher protein or oil levels, or to select lines with modified oils or 
protein, e.g., high or low oleic acid, palmitic acid, lenolinic acid, 
lysine, methionine, etc. Weight could be combined with volume and density 
to calculate seed size. (Density could be observed by lining the cylinder 
with metal strips and then using the controller computer to generate a 
high frequency current through these strips. The change in frequency could 
be observed with the frequency counter in the controller computer because 
the frequency will change with density.) 
The present techniques can also be used with all harvesting equipment, 
including, e.g., all types of combines, small bundle equipment, single 
plant threshers, corn ear sheller, etc. For those traits where NIR 
calibration have been established, a holding bin could be added below the 
single plant thresher which would contain an infrared sensing chamber. 
Below this could be a Y valve which would direct the seed to a dump 
container or a seed tray. A series of Y valves below this could be used to 
divert the seed to the proper cell within a row of a tray, and an 
advancement system similar to those used on plot planters could be used to 
advance to the next row. 
For corn these techniques are applied to inbred selection, when single ears 
are selected. Inbreds are generated by several cycles of inbreeding after 
an initial cross. In each cycle the progeny from a single ear will be 
planted in a single row. The best ears are usually selected and advanced. 
In the case of high oil or high protein corn, this selection may involve 
DNA analysis while the crop is growing, visual selection of the ears to 
determine yield, followed by laboratory analysis on the seed. A similar 
approach as the previous soybean example is used. An NIR device is mounted 
below a corn sheller. Samples are saved or discarded based on an index 
including DNA analysis and maturity recorded before harvest and the 
following parameters recorded at harvest: Moisture, weight, volume and NIR 
parameters such as protein and oil or the profile of each of these as 
determined by NIR. 
The present techniques could also be applied to animal harvesting 
selections. For example, several sources could be combined to select 
individuals that will be retained to breed from those that will be sold at 
market. 
While the present invention has been shown and described with reference to 
preferred embodiments presently contemplated as best modes for carrying 
out the invention, it is understood that various changes may be made in 
adapting the invention to different embodiments without departing from the 
broader inventive concepts disclosed herein and comprehended by the claims 
which follow.