AN ON-LINE PROTEINSENSOR – FROM RESEARCH TO PRODUCT
L. Thylén, and M. Gilbertsson
Swedish Institute of Agricultural and Environmental Engineering
Uppsala
Sweden
T. Rosenthal, and S. Wrenn
Zeltex
Hagerstown, Maryland
ABSTRACT
While yield monitors and yield-mapping equipment are widely used by farmers, the
possibility to measure grain protein on the combine harvester has only been discussed in
recent years. Knowing grain protein content makes it possible to sort the grain into
different fractions and thereby increase the overall value. Furthermore, knowing grain
yield and protein content makes it possible to calculate the removal of nitrogen with the
grain.
In this project the first trials to measure protein content on-line on a combine harvester
were carried out in 1999. In these experiments the work was based around an existing
protein sensor (Zeltex, ZX50). The measurements were intermittent and each sample took
one minute to analyze. Two valves controlled the grain flow through the sensor. The first
version of the sensor was not very successful. During winter a new version was built and
tested in a test rig. During these trials, effects of vibrations, dust, light etc. were
evaluated.
A third version was built based on previous experiences, and tested during the harvest
2000. This version worked well in the field and the system measured about 30 samples
per hectare. This version was later rebuilt for use in elevators. The main differences
between the combine version and the elevator version are that compressed air actuators
control the valves and that an external computer replaces the control device. The
intentions with this sensor are to study the possibilities to sort grain into different
fractions at grain dryers and elevators. Furthermore, this sensor is used to optimise the
amount of protein (soy) required when milling feed.
Since the previous versions of the sensor system were based around an existing
protein sensor, the required space for the sensor was rather large. Therefore, a
development aimed at a smaller sensor was initiated. The first version of this sensor was
developed for small grain crops and was tested in field during summer 2001. This version
managed about one reading every 10 seconds equalling about 60 samples per hectare.
Keywords:
Precision agriculture, grain quality, protein sensor
1
INTRODUCTION
Focus in precision agriculture is commonly on soil and yield mapping, remote sensing
and reflectance measurements. The possibility to map grain quality variations has not
been discussed widely. The high cost of sampling and grain analysis is probably one
reason for the lack of research within this area. Some studies have shown that protein
content within fields varies considerably (Mulla et al., 1992; Dawson, 1996; Algerbo and
Thylén, 1999; Reyns et al., 1999). This variability can greatly affect the economic value
of the crop, and some papers have shown the possibility to increase the crop value by
sorting the grain according to protein quality (Stafford, 1999; Thylén et al., 1999).
Measurement of protein content in combination with yield measurement will also make it
possible to calculate nitrogen uptake.
Several quality parameters can affect crop value. Parameters commonly measured
include specific grain weight and moisture and protein content. Here, we focus on
moisture and protein content.
Traditionally, grain samples are analyzed in the laboratory. Moisture content is usually
measured by drying the ground grain and registering the weight loss. Protein content was
normally measured by Kjeldahl analysis, but today the use of NIR/NIT (Near Infrared
Reflectance / Near Infrared Transmission) instruments has become more common. The
possibility to use NIT to measure protein content in whole grain was investigated by
Williams et al. (1985). They evaluated a previous version of the NIT sensor used in this
study. Williams et al. (1985) studied the effect on accuracy of varying grade, temperature,
dockage, kernel size, growing season, tempering and sampling error. Grain temperature
affects the results of the NIT measurement, and therefore a temperature correction has
been developed and is currently being used in most NIT analyzers. Dockages, foreign
materials, affect the results, but the effects are minor with dockage up to 5%. Kernel size
did not affect measurement on wheat, but readings for barley with large kernels tended to
be too high. The effects of different growing seasons and growing places can be resolved
with a large calibration set.
Jørgensen and Jørgensen (2001) developed a sampling mechanism that can collect
georeferenced grain samples on a combine harvester. The sampling mechanism collects
samples at the top of the clean grain elevator, and the sample is transported to a sample
cup by the use of vacuum. The grain sample is analyzed with traditional methods. The
system can collect about one sample every 20 seconds.
On-line measurement of grain moisture content is of interest for both grain dryers and
combine harvesters. The measurement of dielectric properties has been used to estimate
grain moisture content in both driers (Berbert and Stenning, 1996) and combine
harvesters. Combine harvesters with yield mapping systems are usually also equipped
with a dielectric sensor that measures moisture content. These sensors are commonly
installed in a bypass system on the clean grain elevator. Reyns et al. (2001) used a NIR
sensor on a combine harvester to measure moisture content on-line in corn during
harvest. The sensor was installed in a bypass system at the side of the clean grain
elevator. RMSEP for this sensor was calculated to 0.32%.
An approach to measure grain protein content on-line with near infrared reflectance
was described by Engel et al. (1997). Textron Systems and Case New Holland have
further developed this technology (von Rosenberg et al., 2000).
2
The objectives of this study were to:



Determine the required sampling intensity when mapping protein content.
Develop a system that can measure protein and moisture content on-line on a combine
harvester.
Test the system during harvest.
MATERIALS AND METHODS
Spatial variability of protein content
Spatial variability of protein content, by the means of semivariograms, is not referred
to in the literature as often as semivariograms for soil properties. To predict the required
sampling intensity for an on-line protein sensor, an average variogram was computed in a
similar way to McBratney and Pringle (1999). Data were used from ten studies in
Sweden, where nitrogen was evenly applied. In seven of these studies, a spherical model
was fitted to the calculated semivariances. In the other three, spherical models were also
fitted even though exponential models were fitted originally. The variograms were
transposed with a third root transformation, and an average variogram was calculated.
Finally, the average variogram was transposed back and a spherical model was fitted to
the data. The spherical model applied to the average variogram was used to calculate
kriging standard deviation with different block sizes and sampling intensities.
NIT-sensor
In this project we used a grain protein sensor that operates in the short-wavelength
near infrared, from 893 to 1045nm. The portable instrument, Zeltex ZX-50 (Zeltex,
Maryland, USA), transmits light in 14 wavelengths using light-emitting diodes and
filters. This construction can probably withstand vibrations common on combine
harvesters. The sensor can be calibrated to analyze moisture, protein and other constituent
contents from the absorption spectra of the grains being tested. Besides measuring the
spectra (Figure 1), the ambient and sample temperatures are also measured. The collected
data were used to calculate the moisture and protein content of the sample measured
according to equation 1. The optical data used in equation 1 were calculated as log (1/T),
described by Norris and Williams (1984).
Constituent = C0 + C1*OD1+.......C14*OD14+C15*T1+C16*T2
(1)
Where C0..C16 are constants, OD1..OD14 are registered optical data, T1 is grain sample
temperature, and T2 is sensor temperature.
3
0.4
Log (1/T)
0.35
0.3
0.25
0.2
0.15
890
910
930
950
970
990
1010
1030
1050
Wavelength, nm
Figure 1. An example showing optical data when analyzing a sample of barley.
When implementing NIT sensors on combine harvesters, special precautions must be
taken to avoid problems with grain flow-through, light, packing, static electricity
vibrations and wear. To do this, the NIT-sensor was installed in a sampling system
mounted on the side of the clean grain elevator. The analyzer was installed in a box that
ensured that no external light affected the NIT-sensor. Cutting a hole in the bottom of the
NIT sensor and letting grain pass through the sample cup enabled intermittent
measurement of grain protein content. Two flaps controlled the grain flow through the
sensor (Figure 2). A level sensor between the flaps ensured that the sample cup was
properly filled during the analysis. A control unit located next to the NIT-sensor
controlled both the flaps and the NIT-sensor. To achieve this, the software in the NITsensor was modified. The control unit also recorded the combine’s speed and the header
position. When using the system in the field, all data were transmitted to a handheld PC
in the combine’s cab. The PC was also connected to a GPS-receiver.
4
FIGURE 2. The sampling device was mounted beside the clean grain elevator. Two
flaps controlled the flow of grain through the NIT- sensor. The level sensor ensured
that the sample cup was properly filled during measurement. To the right, a picture
of the sensor installed on a combine harvester.
Uniform packing of the grain is necessary to obtain consistent readings. Grains and
seed such as oats and canola pack uniformly; however, barley is more difficult to pack
uniformly. For this reason, the level sensor (Figure 2) was placed about 100 mm above
the sample cup. In this way, the kernels above the sample cup packs grain kernels in the
sample cup. Like most electronics, NIT-sensors are susceptible to static electricity and
ground loops. To avoid such problems, the NIT-sensor, sampling and control device,
logger and GPS were powered from the same location, and the sample cup was grounded
to reduce static electricity. To avoid problems caused by vibrations, the sensor was built
into a unit, which dampened the vibrations. Vibrations do not affect the sensor directly,
but moving kernels during measurement lead to poor data (Table 1).
5
Table 1. A grain sample was analyzed 12 times with and without vibrations. With
strong vibrations, the collected data are not useful. Bad readings probably occur
when grain kernels move in the sample cup (Algerbo & Thylén, 2000).
Vibrations
No
Yes
min, %
10.61
8.46
average, %
10.69
10.92
max, %
10.78
12.55
standard deviation
0.05
1.35
During harvesting, the sample cup through which the grain flows, becomes scratched,
which affects the amount of transmitted light. To compensate for this wear, every fifth
reading was carried out with an empty sample cup.
Further development
During 2001 and 2002 the protein sensor system has been developed further. In 2001,
a specific combine grain analyzer from the ZX-50 Portable Grain Analyzer was
developed. This analyzer was designed to save space and speed up analysis time in the
field. Three of these prototypes were built and tested; one each in Sweden, Australia and
the U.S. In Australia, the prototype (figure 3) was mounted to a John Deere 9600
combine in association with the University of Sydney. Another combine analyzer was
built specifically for soybean and corn, and mounted on a Gleaner combine near Kansas
City, MO. This analyzer had a longer path-length than the wheat combine analyzer. The
first tests in the soybean fields were carried out in September of 2001, during harvest in
Missouri. These tests showed both promise and problems with the prototype. Under true
field analysis conditions, it was discovered that there was a problem with the chamber
filling with dust and dirt during the soybean harvest. This did not occur during the wheat
harvest. This problem was exaggerated by the fact that the combine analyzer was run,
open, two days before being turned on. The dust affected the full and empty sensors.
Therefore, we had to have a PROM specifically designed to eliminate these problems.
When the prototype combine analyzer was cleaned and set up, it took readings
approximately every 12 seconds in the first two fields that were harvested. This analysis
looked promising. In fact, analyzed samples put through the combine analyzer on the
combine had excellent results.
Therefore, the design was changed for the 2002 season (figure 4). The combine
analyzer has no longer an opening on the bottom that allows dust or dirt to fill the sensor.
The combine analyzer is now designed to be a closed system that will only allow grain or
other dust to enter the chamber during analysis. This sealed system should eliminate the
problems of the full and empty sensors fouling.
6
FIGURE 3. The design of the analyzer was changed till the season 2001.
FIGURE 4. The latest version of the protein sensor is designed to eliminate
problems discovered with previous versions.
7
Test bench trials
Since the NIT-sensor was modified, the sensor was recalibrated with grain samples of
known protein and moisture content that were analyzed on a Foss Tecator NIT sensor
(Foss Tecator AB, Sweden). This calibration was performed with the complete sampling
device. However, the sensor was filled with grain through a small hopper and not by the
clean grain elevator.
RESULTS
Spatial variability of crop quality
Calculated variograms and the average variogram for protein content are shown in
Figure 5. The model applied to the average variogram was used to calculate kriging
variance for different block sizes and sampling intensities. These calculations were
carried out assuming the collected data were in a square grid pattern. Results are shown
in Figure 6.
1.2
γ(h), protein²
1
0.8
0.6
0.4
0.2
0
0
50
100
150
200
250
300
350
Lag, metres
Figure 5. Variograms from ten field trials. The thick line shows the average
variogram. A spherical model with the following parameters was fitted to the
average variogram: C0 0.075, C1 0.39, and range 145 m.
8
0.4
Kriging variance, %²
0.35
0.3
0.25
point
10 metre block
20 metre block
40 metre block
0.2
0.15
0.1
0.05
0
0
20
40
60
80
100
120
Number of samples per hectare
Figure 6. Calculated kriging variances for different sampling intensities using
punctual kriging and three different block sizes. The model fitted to the average
variogram was used for the calculations.
Test bench trials
The calibration of the sensor, using multiple linear regression was performed with
wheat [Triticum aestivum (L).] and barley [Hordeum vulgare (L).]. The measured
constituents were protein and moisture content. To achieve a more accurate calibration
for field trials the calibration was performed at different temperatures. Examples of the
calibration curves for wheat are shown in Figure 7.
9
Wheat
Wheat
20
Predicted moisture content, %
Predicted protein content, %
16
15
14
13
12
11
10
9
8
18
16
14
12
10
8
8
9
10
11
12
13
14
15
16
8
10
Lab protein content, %
12
Barley
16
18
20
20
22
Barley
20
Predicted moisture content, %
15
Predicted protein content, %
14
Lab moisture content, %
14
13
12
11
10
9
8
18
16
14
12
10
8
9
10
11
12
13
14
15
16
10
Lab protein content, %
12
14
16
18
Lab moisture content, %
FIGURE 7. Results from calibration of a protein sensor with wheat and barley. The
standard error of prediction for wheat was 0.358 for protein content and 0.185 for
moisture content, and for barley 0.491 for protein content and 0.490 for moisture
content.
Field trials
Two sensor systems were tested during harvesting in 2000. During normal operation
the system managed about 30 readings per ha. About two percent of the collected data
were erroneous, but these were detected when studying the transmitted data. Using the
software Vesper (Minasny et al., 1999), the collected data were interpolated with block
kriging (20*20 m) using local semivariograms. Examples from the field ‘Gustavsson’ are
shown in Figure 9. The kriging variance was also calculated. Statistics of the crop are
presented in Table 2. Correlation between parameters is shown in Table 3. The moisture
content of the grain depends on the time of day during harvesting (Figure 8). The strong
correlation between yield and moisture content is mainly because the harvest started in
the high yielding area. This also affects the correlation between protein and moisture
content.
Table 2. Summary of variability of the crop harvested in field ‘Gustavsson’.
Yield
Protein content
Moisture content
Average
5.89
10.6
18.4
Min.
4.51
9.7
17.8
Max.
7.37
11.2
19.5
Kriging variance
0.006
0.019
0.018
10
Table 3. Correlation between yield, protein and moisture content in field
‘Gustavsson’.
Yield
Protein content
Moisture content
Yield
1
Protein content
-0.57
1
Moisture content
0.56
-0.56
1
20
Moisture content, %
19.5
19
18.5
18
17.5
17
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
Time
FIGURE 8. The moisture content of the harvested grain decreased during the day.
Time axis shows UTC time. For local time add two hours.
11
Y-axis, metres
Moisture content
%
6637400
19.5
6637300
19.0
6637200
18.5
6637100
18.0
1599300 1599500 1599700 1599900 1600100
17.5
X-axis, metres
17.0
Y-axis, metres
Protein
%
6637400
6637300
11.0
6637200
10.5
6637100
1599300 1599500 1599700 1599900 1600100
X-axis, metres
10.0
9.5
Y-axis, metres
Yield
ton/ha
6637400
6637300
7.0
6637200
6.0
6637100
1599300 1599500 1599700 1599900 1600100
5.0
X-axis, metres
4.0
FIGURE 9. Example of maps showing yield, protein and moisture content from field
‘Gustavsson’. The field (ca. 15.3 ha) was cropped with winter wheat [Triticum
aestivum (L).], cultivar Stava.
12
DISCUSSION AND CONCLUSIONS
The possibility of using on-line measurement of protein content during harvesting has
been discussed for several years. Knowing the protein quality, the grain can be sorted into
different fractions thereby increasing the economic value of the harvested crop. The
information can, in conjunction with yield data, also be used to calculate the nitrogen
uptake in the crop.
Here, we adapted an existing portable grain protein sensor for use on combine
harvesters. The standard error of prediction was 0.358 for wheat protein and 0.185 for
wheat moisture content, whereas it was 0.491 for barley protein and 0.490 for barley
moisture content. The lower accuracy for barley is probably due to less even compaction.
A large calibration set is required to achieve a high accuracy when calibrating NIR/NITsensors. This, in conjunction with keeping the calibration up to date with new cultivars in
different countries, will probably be a major challenge.
By fitting a model to a calculated average variogram, we concluded that for grain
protein content a sampling intensity of 20 samples/hectare is needed. Fewer samples
would create a map with a larger kriging variance. Here, we collected about 30 samples
per hectare, and the average kriging variance was 0.019 %². The correspondence between
the expected kriging variance calculated from the average variogram and the kriging
variance of the interpolated data is striking.
When testing the system in field during harvest 2000, blocking of the sensor occurred
when the moisture content was about 30 %. During the harvest we also had a hardware
problem with the valve that controlled the inlet grain flow. With the 2001 version we had
major problems with dust when combining soybeans. The new design uses a larger inlet
opening to get more input grain, thus filling faster. Further, there’s a grain guide to ensure
that any grain entering the inlet actually passes through the instrument. This will speed up
chamber filling and will tend to keep full/empty sensors cleaner. Further, the enclosed
inlet will block outside light, removing the requirement of an overall cover (unless
required for safety or protection from the elements).
One aim of this study was to develop an on-line protein sensor for combine harvesters.
If the sensor is to be used to delineate management zones based on protein variations and
nitrogen uptake, the sensor must be installed on the combine harvester. If the sensor is to
be used to sort grain into fractions with different qualities, the sensor can be used on the
combine, grain drier, or at the elevator. The logistics of sorting grain at the grain drier and
at an elevator are probably simpler than those of sorting grain in the field. The potential
for accurate sorting of grain is probably higher in the field. However, this depends on
whether the variability in protein content is present within fields, between fields or
between farms.
ACKNOWLEDGEMENT
This research was funded by The Swedish Farmers’ Foundation for Agricultural Research
(SLF). Thanks to Zeltex for their support.
13
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