- Email: [email protected]
Soil Parameters Prediction with Soil Image Collected by Real-Time Soil Spectrophotometer
SOIL PARAMETERS PREDICTION WITH SOIL IMAGE COLLECTED BY REAL-TIME SOIL SPECTROPHOTOMETER
] Made Anorn S. Wijaya, S. Shibusawa, A. Sasao, K. Sakai, H. Sato
United Graduate School ofAgricultural Science Tokyo University of Agriculture and Technology Fuchu, Tokyo 183-8509, Japan Correspondent e-mail [email protected]
Abstract : In order to study the role of surface condition or surface texture of soil sample, the textural image analysis was applied to the soil images that were collected using the real-time soil spectrophotometer. The experiments were conducted both in laboratory and on the field. The results show that soil reflectance was significantly affected by the condition of the sample surface. The rough surfaces tended to reduce the soil reflectance. The changing of the reflectance can be predicted calculating the changing of correlation feature of the soil image, especially for the red channel of the image. The correlation feature of red channel also gave a good prediction for moisture content. Copyright @2001IFAC.
Keywords: precision farming, image processing, real-time sensor, soil parameter
Another approach is the spectroscopic approach. In our
1. INfRODUC110N
laboratory, the real time soil spectrophotometer has been Precision furming as an intensive data used in agricultural
developed (Shibusawa et al., 1999,
system practices, the gathering data tools which can provide
times both in the laboratory and on the field (I Made Anom et
accurate information about the field for making decision how
a/., 2(ffia, 2roll, 2001). The results showed, however, that if
to manage the field, is necessary. Image processing lechnique
the original reflectance were used to predict the soil parameters
is one of challenging methods, which can be used to collect the
without any pretrealrnents, the prediction accuracy were very
information related to the field. AI-Abed, Lewis and Samson
low. When pretreatments were applied to the retlectanre, the
(1989) used cob-splitting lechnique to separate soil moisture
prediction accuracy was increasing but the reflectance
group, and Bodun (2CXX) used the textural image analysis 10
wavelengths used for soil parameter prediction were changing
predict the moisture content of sludge. While \'arvel et al.
with time and soil type. To improve the accuracy and
(1999) tried to
repeatability of the prediction models, many efforts have been
u<;e
the aerial image for descnbing the spatial
variability of organic matter and phosphorus level of cornfield.
2CXX)
and tested several
done such as observing the effect of the surface angle, the
44
1.2 Field experiment
surface
The field experiment was conduded on an experimental field
affected by the surface
of Kyoto University from which the soil samples for
rule of surface
laboratory experiment were collected. The soil reflectance and
experimen~
the texturnl image analyses are
soil images were colleded using the real-time soil
applied to study the role of soil surface condition on the soil
spectrophotometer that is mounted on the trador. The soil
reflectance.
reflectances were colleded at 20 rns of scanning time with 20
needed. In this
times of integration times, and in a depth of 20 cm The The objectives of this study were to define the relationship
reflectance wavelength ranged between 400 nm and 1700 om
between textural features of soil image and soil reflectance in
From this field, 63 soil samples were also colleded at the same
which will be used for developing a correction factor to
location the same depth of scanning point, and then analyzed
determine the soil parameters using the real-time soil
in laboratory.
spectrophotometer, a<; well as to study the probability of using the textural image feature to predid the moisture content and
To select the sample for studying the relationship between soil
ather parameters of the underground soil
image features and soil reflectance, the colJeded soil samples were firstly sorted aa:urding to the moisture content (MC) followed by soil organic matter (SOM) conten~
2. MAIERJAlS AND METHODS
conten~
NOrN
pH and electric conductivity (EC). Only the samples
with relatively the same soil parameters were selected for
1.1. Labormory experiment
further analySL'i. In this experiment 10 soil samples with
relatively the same value of soil parameter (MC 43±0.2%, The laboratory experiment was conduded as a prelirninary
SOM 9±OS%, pH &.to.!, NO)-N 10±4 mgIL, EC 21±23
obseIVation in studying the effect of surface condition in tenn
mS/m) were seleded for image analysis.
of textural image features to the soil reflectance. The obseIVation wa<; perfonned at 9 levels of moisture content (3%, 7%,13%,18%,22%,28%,33%,39%, and 44%) and 5 levels
1.3. Textural image analysis
of surface condition (fla~ ditch, hollow, convex., and concave) using the soil samples that colleded from experimental funn
Figure 1 illustrated the procedure for textural image analysis.
(paddy field) of Kyoto University. The moisture content of the
Frrstly, the soil images that recorded in digital videotape were
soil samples wa<; simulated by adding certain amount of water
tran.<;{erred into image file using the Panasonic DV2 Studio
to the dried soil and equilibrated for one day in closed
software, and saved as JPG file. 1\.vo images were collected
container at room temperature after the water and the soil were
from each location of scanning point or sampling point. A
mixed well. By using these soils the soil surface was formed
redangular area of 192 pixels x 192 pixels was selected from
and the retlectance and the images were captured. The
the middle of the image and then divided into 9 segments. The
capturing of soil images and scanning of soil reflectance were
size of each segment was 64 pixels x 64 pixels (see Figure 2).
performed using the replication of soil sensor, which ha., the
The segmented portions were converted into text files by
same configuration as the real-time soil spedrophotometer that
saving them in "RAW' file format of the Adobe PhotoShop
used for field experiment The soil images were captured
software. The saved images were then quantized into their
through the micro
camera and recorded into the digital
respedive red (R), green (G), blue (B), and gray (Y) channels,
videotape. Finally, the spectral and textural image analyses
valued in the range 0-255 grey levels in a program coded in
were perfonned.
C++. By mapping the grey-level from 0-255 to 0-15, resulting
CXJ)
in 15 x 15 co-oa::urrence matrixes, the textural features of each
45
image was calculated. The textural features calculated in this
experiment, the soil reflectance of rough or non-flat surface
experiment included angular serond moment (ASM), entropy,
dropped up to 30% from the reflectance of flat surface.
contrast, and correlation feature. 60
Image acquisition
g
(recorded as video images)
axM:X
50
"u
IIal
u
c:ax:a\'e
B . 40
T
Image transfer
<1=
~ 30
citch
(saved as JPG file) T
950
1075 1200 1325 1450 1575 1700
Region segmentation and file
Wavelength (nm)
conversion FIgUre 3. Refledance response to the surface condition at MC
T
of39%
Image digitization and grey level
mapping T
On the other hand, the field experiment showed that the soil
Co-occurreoce matrix generation
reflectance was seemly affected by the soil surface texture.
(saved as JPG file)
From the 10 samples that selected from the field data in which
T
had same moisture and SOM content (43% and 9%~ the soil
Textural features calculation
reflectance varied according the surface texture. The reflectance variation of those soil samples was 165% in
average. FIgUre 1. Flowchart of soil surface image textural features 3.2 Correlation
analysis
of
textural soil image
feaJures
to soil
rejlectance From laboratory experiment the correlation analysis of textural image features to the soil refiedance slx>wed that all of the gray channel of ASM, contrast, and entropy features had higher correlation than the R, G, and B channel The absolute
correlation value of those features ranged between 0.45-053, 0.44-055, and 0.46-058 for ASM, contrast, and entropy
FIgUre 2 Segmentation of soil image
feature, respectively. The absolute correlation value of gray
channel of correlation feature, however, was much lower than
3. RESUITS AND DISaJSSIONS
correlation feature value of the R, G, and B channel The highest absolute correlation value was seen in the correlation
3.1. Rejlectance responses to soil surfoce texture
feature ofbIue channel (0.75-0.79). The results of laboratory experiment showed that the soil
refiedance shifted when the surface was simulated to be rough
ID contrast with the laboratory experiment results, the
(FIgUre 3). ID all levels of moisture content, the rough surfaces
correlation analysis results of field experiment slx>wed that the
temed to have lower refiedance value than flat surface. Only
entire gray channel of the textural features had lower absolute
the refledaoce of convex surface had a higher value. ID this
correlation value than the R, G, and B channel Also, the
46
correlation coefficient values of alJ features were much higher
higher the moisture contents the lower the correlation value.
than the laboratory resuJts. The highest correlation of ASM 50
and contrast feature appeared in blue channel, while for
~
entropy and correlation feature appeared in green and red
C
40
" 30 E
channel, respectively. The correlation coefficient of blue
8
channel of ASM and contrast feature ranged between 0.52-
" B .~
0.68 for ASM, and 0.69-0.78 for contrast feature. For green
::E
0
20 10
channel of entropy and red channel of correlation feature, the correlation coefficient range between 0.73-0.83 and 0.78-0.82,
Correlation value (x 10000)
respectively.
FJgUte 5. The relationship between correlation features to the moisture content
By using the data in which the highest correlations were observed, the relationship of textural image feature 10 the soil reflectance were calaIlated Figure 4 shows the relationship
The correlations of gray channel of contrast feature and
between the ratios of image feature difference 10 the mean
entropy, however, seemly have l\\Q regiom. Allow moisture
image feature against the ratio of reflectance difference to the
content «=22%), the moisture content bad positive correlation
overnll mean reflectance. It can be seen that all features bad
to the contrast and entropy feature. The higher the Imisture
linear relationship 10 the soil reflectance. The correlation
content, the higher the contrast and entropy value. In contrary,
feature of red channel gave the best linear relationship to the
at higher Imisture content (>=22%), the moisture content was
soil reflectance (Y =0.3645 X -
negatively correlated to the contrast and entropy feature. At thli
7 10.18,
R~ 0.65).
region, the higher the moisture contents the lower the contrast and entropy values (see Figure 6).
Ratio of reflectance difference to the overall mean reflectance
-2.5
-3.0
(a) • ASM-B
D
-2.0
-1.5
Contrast value
Con-B "Ent-G - Cor-R
FIgUte 4. Relationship between ratios of reflectance difference to overall mean reflectance against ratio of feature
difference to the mean feature
3.3. Moisture reltJtionship to the textural image feature From the correlation analysis of textural image feature 10 the
10.0
(b)
moisture content of the soil using the flat surface samples, it
20.0
30.0
Entropy value
was futmd that only correlation feature of R, G, and B channel
bad high linear correlation ~ r I = 0.84 for R, 0.83 for G and
Figure 6. Relationship between (a) contrast and (b) entropy features 10 the moisture content .
0.80 fur B channel). Figure 5 shows moisture relationship to the correlation feature of red channel It can be seen that the
47
Minnesota, USA, July 16-19.
4. mNCLUSION
Slubusawa S., li MZ, Sakai K, Sasao A, Sato H., Hirako S., Soil reflectance was significantly affected by the condition of
Otomo A 1999. Spectrophotometer for Real-Tnne
the sample surface. The rough surfaces tended to reduce the
UndergrOlmd Soil Sensing ASA.E Paper No. 993030,
soil reflectance. The changing of the reflectance can be
presented atAS4E meeting at Toronto. Varve~
predicted by calooating the changing of correlation feature of
G. E., Schlemmer, M. R and Schepers, 1. S. 1999.
the soil image, especialJy for the red channel of the image. The
Relationship between Spectral Data from an AeriaJ
correlation feature of red channel also gave a good prediction
Image and Soil Organic Matter and Phosporus Levels.
for moisture content
Precision Agriadtz.uT!, 1: p. 291-300.
REFERENCES AI-Abed, S. R, Lewis, D. T., and Samson, S. A 1989. Use of CoIOT Splitting Technique to Separate Soil Moisture
Groups. .50ilSci Soc.Am] 53:p. I812-I8I8. Bodun, P. O. 200J. Prediction of Trafficability on Drying
Sludge Materials from Lake Kasumigaura. PhD. Thesis (Unpublished). Graduate School of Bio-Applications and Systems Engineering, Tokyo University of
Agriooture and Technology, Tokyo, Japan. I Made Anom SW., Slubu<;awa S., Sasao A, Sakai K, Sato H., Hirako S. and Blackrnore S.
2(XX)a
Moisture, Soil
Organic Matter and Nitrate Nitrogen Content Maps Using
the
Real-Tnne
Proceeding of the
Soil
Spectrophotometer.
T IFAC/CIGR
International
Ubrkslwp on B10-ROBOTICS n, eds. S. Slubusawa, H.
Monta, H. Mura.'ie, November 25-26, 200J, Osaka, Japan: p. 305-310. I Made Anom SW, Slubusawa S., Sasao A, Sakai K, Sato H., Hirako S. and Blackrnore S. 2!XXJb. Soil Parameters Maps Using the Real-Tnne Soil Spectrophotometer. Proceeding of the :I' Intemaliono.l Conference on Precision Agriculture, eds. Pc. Raber!, RH. Rust, W. E. Larson, July 16-19, Bloomington, Minnesota, USA, compiled in CD-ROM. I Made Anom SW., Slubusawa S., Sasao A, and Hirako S. 2001 . Soil Parameters Maps in Paddy Field Using the Real-Tnne Soil Spectrophotometer. ]
of the Japanese
Society ofAgricultural Machinery, 63(3): p. 51-58.
Slubusawa S., Hirako S., Otomo A , and Blackrnore S. 2000. On-line Real-time Soil Spectrophotometer. Presented at
.s"' International Conference on Precision Agriculture,
48
] Made Anorn S. Wijaya, S. Shibusawa, A. Sasao, K. Sakai, H. Sato
United Graduate School ofAgricultural Science Tokyo University of Agriculture and Technology Fuchu, Tokyo 183-8509, Japan Correspondent e-mail [email protected]
Abstract : In order to study the role of surface condition or surface texture of soil sample, the textural image analysis was applied to the soil images that were collected using the real-time soil spectrophotometer. The experiments were conducted both in laboratory and on the field. The results show that soil reflectance was significantly affected by the condition of the sample surface. The rough surfaces tended to reduce the soil reflectance. The changing of the reflectance can be predicted calculating the changing of correlation feature of the soil image, especially for the red channel of the image. The correlation feature of red channel also gave a good prediction for moisture content. Copyright @2001IFAC.
Keywords: precision farming, image processing, real-time sensor, soil parameter
Another approach is the spectroscopic approach. In our
1. INfRODUC110N
laboratory, the real time soil spectrophotometer has been Precision furming as an intensive data used in agricultural
developed (Shibusawa et al., 1999,
system practices, the gathering data tools which can provide
times both in the laboratory and on the field (I Made Anom et
accurate information about the field for making decision how
a/., 2(ffia, 2roll, 2001). The results showed, however, that if
to manage the field, is necessary. Image processing lechnique
the original reflectance were used to predict the soil parameters
is one of challenging methods, which can be used to collect the
without any pretrealrnents, the prediction accuracy were very
information related to the field. AI-Abed, Lewis and Samson
low. When pretreatments were applied to the retlectanre, the
(1989) used cob-splitting lechnique to separate soil moisture
prediction accuracy was increasing but the reflectance
group, and Bodun (2CXX) used the textural image analysis 10
wavelengths used for soil parameter prediction were changing
predict the moisture content of sludge. While \'arvel et al.
with time and soil type. To improve the accuracy and
(1999) tried to
repeatability of the prediction models, many efforts have been
u<;e
the aerial image for descnbing the spatial
variability of organic matter and phosphorus level of cornfield.
2CXX)
and tested several
done such as observing the effect of the surface angle, the
44
1.2 Field experiment
surface
The field experiment was conduded on an experimental field
affected by the surface
of Kyoto University from which the soil samples for
rule of surface
laboratory experiment were collected. The soil reflectance and
experimen~
the texturnl image analyses are
soil images were colleded using the real-time soil
applied to study the role of soil surface condition on the soil
spectrophotometer that is mounted on the trador. The soil
reflectance.
reflectances were colleded at 20 rns of scanning time with 20
needed. In this
times of integration times, and in a depth of 20 cm The The objectives of this study were to define the relationship
reflectance wavelength ranged between 400 nm and 1700 om
between textural features of soil image and soil reflectance in
From this field, 63 soil samples were also colleded at the same
which will be used for developing a correction factor to
location the same depth of scanning point, and then analyzed
determine the soil parameters using the real-time soil
in laboratory.
spectrophotometer, a<; well as to study the probability of using the textural image feature to predid the moisture content and
To select the sample for studying the relationship between soil
ather parameters of the underground soil
image features and soil reflectance, the colJeded soil samples were firstly sorted aa:urding to the moisture content (MC) followed by soil organic matter (SOM) conten~
2. MAIERJAlS AND METHODS
conten~
NOrN
pH and electric conductivity (EC). Only the samples
with relatively the same soil parameters were selected for
1.1. Labormory experiment
further analySL'i. In this experiment 10 soil samples with
relatively the same value of soil parameter (MC 43±0.2%, The laboratory experiment was conduded as a prelirninary
SOM 9±OS%, pH &.to.!, NO)-N 10±4 mgIL, EC 21±23
obseIVation in studying the effect of surface condition in tenn
mS/m) were seleded for image analysis.
of textural image features to the soil reflectance. The obseIVation wa<; perfonned at 9 levels of moisture content (3%, 7%,13%,18%,22%,28%,33%,39%, and 44%) and 5 levels
1.3. Textural image analysis
of surface condition (fla~ ditch, hollow, convex., and concave) using the soil samples that colleded from experimental funn
Figure 1 illustrated the procedure for textural image analysis.
(paddy field) of Kyoto University. The moisture content of the
Frrstly, the soil images that recorded in digital videotape were
soil samples wa<; simulated by adding certain amount of water
tran.<;{erred into image file using the Panasonic DV2 Studio
to the dried soil and equilibrated for one day in closed
software, and saved as JPG file. 1\.vo images were collected
container at room temperature after the water and the soil were
from each location of scanning point or sampling point. A
mixed well. By using these soils the soil surface was formed
redangular area of 192 pixels x 192 pixels was selected from
and the retlectance and the images were captured. The
the middle of the image and then divided into 9 segments. The
capturing of soil images and scanning of soil reflectance were
size of each segment was 64 pixels x 64 pixels (see Figure 2).
performed using the replication of soil sensor, which ha., the
The segmented portions were converted into text files by
same configuration as the real-time soil spedrophotometer that
saving them in "RAW' file format of the Adobe PhotoShop
used for field experiment The soil images were captured
software. The saved images were then quantized into their
through the micro
camera and recorded into the digital
respedive red (R), green (G), blue (B), and gray (Y) channels,
videotape. Finally, the spectral and textural image analyses
valued in the range 0-255 grey levels in a program coded in
were perfonned.
C++. By mapping the grey-level from 0-255 to 0-15, resulting
CXJ)
in 15 x 15 co-oa::urrence matrixes, the textural features of each
45
image was calculated. The textural features calculated in this
experiment, the soil reflectance of rough or non-flat surface
experiment included angular serond moment (ASM), entropy,
dropped up to 30% from the reflectance of flat surface.
contrast, and correlation feature. 60
Image acquisition
g
(recorded as video images)
axM:X
50
"u
IIal
u
c:ax:a\'e
B . 40
T
Image transfer
<1=
~ 30
citch
(saved as JPG file) T
950
1075 1200 1325 1450 1575 1700
Region segmentation and file
Wavelength (nm)
conversion FIgUre 3. Refledance response to the surface condition at MC
T
of39%
Image digitization and grey level
mapping T
On the other hand, the field experiment showed that the soil
Co-occurreoce matrix generation
reflectance was seemly affected by the soil surface texture.
(saved as JPG file)
From the 10 samples that selected from the field data in which
T
had same moisture and SOM content (43% and 9%~ the soil
Textural features calculation
reflectance varied according the surface texture. The reflectance variation of those soil samples was 165% in
average. FIgUre 1. Flowchart of soil surface image textural features 3.2 Correlation
analysis
of
textural soil image
feaJures
to soil
rejlectance From laboratory experiment the correlation analysis of textural image features to the soil refiedance slx>wed that all of the gray channel of ASM, contrast, and entropy features had higher correlation than the R, G, and B channel The absolute
correlation value of those features ranged between 0.45-053, 0.44-055, and 0.46-058 for ASM, contrast, and entropy
FIgUre 2 Segmentation of soil image
feature, respectively. The absolute correlation value of gray
channel of correlation feature, however, was much lower than
3. RESUITS AND DISaJSSIONS
correlation feature value of the R, G, and B channel The highest absolute correlation value was seen in the correlation
3.1. Rejlectance responses to soil surfoce texture
feature ofbIue channel (0.75-0.79). The results of laboratory experiment showed that the soil
refiedance shifted when the surface was simulated to be rough
ID contrast with the laboratory experiment results, the
(FIgUre 3). ID all levels of moisture content, the rough surfaces
correlation analysis results of field experiment slx>wed that the
temed to have lower refiedance value than flat surface. Only
entire gray channel of the textural features had lower absolute
the refledaoce of convex surface had a higher value. ID this
correlation value than the R, G, and B channel Also, the
46
correlation coefficient values of alJ features were much higher
higher the moisture contents the lower the correlation value.
than the laboratory resuJts. The highest correlation of ASM 50
and contrast feature appeared in blue channel, while for
~
entropy and correlation feature appeared in green and red
C
40
" 30 E
channel, respectively. The correlation coefficient of blue
8
channel of ASM and contrast feature ranged between 0.52-
" B .~
0.68 for ASM, and 0.69-0.78 for contrast feature. For green
::E
0
20 10
channel of entropy and red channel of correlation feature, the correlation coefficient range between 0.73-0.83 and 0.78-0.82,
Correlation value (x 10000)
respectively.
FJgUte 5. The relationship between correlation features to the moisture content
By using the data in which the highest correlations were observed, the relationship of textural image feature 10 the soil reflectance were calaIlated Figure 4 shows the relationship
The correlations of gray channel of contrast feature and
between the ratios of image feature difference 10 the mean
entropy, however, seemly have l\\Q regiom. Allow moisture
image feature against the ratio of reflectance difference to the
content «=22%), the moisture content bad positive correlation
overnll mean reflectance. It can be seen that all features bad
to the contrast and entropy feature. The higher the Imisture
linear relationship 10 the soil reflectance. The correlation
content, the higher the contrast and entropy value. In contrary,
feature of red channel gave the best linear relationship to the
at higher Imisture content (>=22%), the moisture content was
soil reflectance (Y =0.3645 X -
negatively correlated to the contrast and entropy feature. At thli
7 10.18,
R~ 0.65).
region, the higher the moisture contents the lower the contrast and entropy values (see Figure 6).
Ratio of reflectance difference to the overall mean reflectance
-2.5
-3.0
(a) • ASM-B
D
-2.0
-1.5
Contrast value
Con-B "Ent-G - Cor-R
FIgUte 4. Relationship between ratios of reflectance difference to overall mean reflectance against ratio of feature
difference to the mean feature
3.3. Moisture reltJtionship to the textural image feature From the correlation analysis of textural image feature 10 the
10.0
(b)
moisture content of the soil using the flat surface samples, it
20.0
30.0
Entropy value
was futmd that only correlation feature of R, G, and B channel
bad high linear correlation ~ r I = 0.84 for R, 0.83 for G and
Figure 6. Relationship between (a) contrast and (b) entropy features 10 the moisture content .
0.80 fur B channel). Figure 5 shows moisture relationship to the correlation feature of red channel It can be seen that the
47
Minnesota, USA, July 16-19.
4. mNCLUSION
Slubusawa S., li MZ, Sakai K, Sasao A, Sato H., Hirako S., Soil reflectance was significantly affected by the condition of
Otomo A 1999. Spectrophotometer for Real-Tnne
the sample surface. The rough surfaces tended to reduce the
UndergrOlmd Soil Sensing ASA.E Paper No. 993030,
soil reflectance. The changing of the reflectance can be
presented atAS4E meeting at Toronto. Varve~
predicted by calooating the changing of correlation feature of
G. E., Schlemmer, M. R and Schepers, 1. S. 1999.
the soil image, especialJy for the red channel of the image. The
Relationship between Spectral Data from an AeriaJ
correlation feature of red channel also gave a good prediction
Image and Soil Organic Matter and Phosporus Levels.
for moisture content
Precision Agriadtz.uT!, 1: p. 291-300.
REFERENCES AI-Abed, S. R, Lewis, D. T., and Samson, S. A 1989. Use of CoIOT Splitting Technique to Separate Soil Moisture
Groups. .50ilSci Soc.Am] 53:p. I812-I8I8. Bodun, P. O. 200J. Prediction of Trafficability on Drying
Sludge Materials from Lake Kasumigaura. PhD. Thesis (Unpublished). Graduate School of Bio-Applications and Systems Engineering, Tokyo University of
Agriooture and Technology, Tokyo, Japan. I Made Anom SW., Slubu<;awa S., Sasao A, Sakai K, Sato H., Hirako S. and Blackrnore S.
2(XX)a
Moisture, Soil
Organic Matter and Nitrate Nitrogen Content Maps Using
the
Real-Tnne
Proceeding of the
Soil
Spectrophotometer.
T IFAC/CIGR
International
Ubrkslwp on B10-ROBOTICS n, eds. S. Slubusawa, H.
Monta, H. Mura.'ie, November 25-26, 200J, Osaka, Japan: p. 305-310. I Made Anom SW, Slubusawa S., Sasao A, Sakai K, Sato H., Hirako S. and Blackrnore S. 2!XXJb. Soil Parameters Maps Using the Real-Tnne Soil Spectrophotometer. Proceeding of the :I' Intemaliono.l Conference on Precision Agriculture, eds. Pc. Raber!, RH. Rust, W. E. Larson, July 16-19, Bloomington, Minnesota, USA, compiled in CD-ROM. I Made Anom SW., Slubusawa S., Sasao A, and Hirako S. 2001 . Soil Parameters Maps in Paddy Field Using the Real-Tnne Soil Spectrophotometer. ]
of the Japanese
Society ofAgricultural Machinery, 63(3): p. 51-58.
Slubusawa S., Hirako S., Otomo A , and Blackrnore S. 2000. On-line Real-time Soil Spectrophotometer. Presented at
.s"' International Conference on Precision Agriculture,
48