- Email: [email protected]
Non-Destructive Growth Measurement by Machine Vision for Cabbage Plug Seedlings Population with Missing Plants
Copyright ® IF AC Bio-Robotics, Infonnation Technology and Intelligent Control for Bio-Production Systems, Sakai, Osaka, Japan , 2000
NON-DESTRUCTIVE GROWTH MEASUREMENT BY MACmNE VISION FOR CABBAGE PLUG SEEDLINGS POPULATION WITH MISSING PLANTS
T. SUZUKI', H. MURASE'
'Osaka Prefecture Agricultural & Forestry Research Center, 442, Shakudo, Habikino, Osaka, Japan 'Faculty of Agriculture, Osaka Prefecture University, 1-1, Gakuen, Sakai, Osaka, Japan
Abstract: This report examines an adaptation of a non-destructive growth measurement system for plug seedlings populations with various planting patterns that vary according to missing plants. A three-layered neural network was used to model the relationship between the total leaf area of a cabbage plug seedlings population and growth indices that were obtained by machine vision. It was confmned that the growth indices of the plug seedlings populations with missing plants varied similarly to the growth indices of plug seedlings populations without missing plants. The predicted total leaf areas for the plug seedlings population with missing plants fitted well with the actual total leaf areas. Copyright © 2000IFAC Keywords: machine vision, neural network model, cabbage plug seedlings population, missing plants
we regarded the plug seedlings in one plug container as a population of plug seedlings and examined the non-destructive growth measurement of such populations.
I. INTRODUCTION Large-scale systems for producing vegetable plug seedlings have been spreading in Japan. Raising plug seedlings in a large-scale production system requires the ability to objectively estimate the growth of seedlings. Destructive measurements of growth, such as determining the leaf area or the top fresh weight, are very laborious and reduce the number of seedlings. Non-destructive growth measurement by machine vision is suitable for objective estimation.
Suzuki et al. (1999a) have reported the non-destructive measurement of cabbage plug seedlings populations using the machine vision and soil coverage models. The images of cabbage plug seedlings populations were extracted from their backgrounds using the relative threshold values of the chromaticity G component. The relative threshold values could be selected objectively by a method of discriminant analysis (Suzuki and Murase, 2000).
There have been numerous studies on non-destructive growth measurement by machine vision. For example, Hack (1989), Iwao et al. (1991), and Shibata et al. (1993) have used machine vision to non-destructively measure plant growth, but for single plants only. Many plug seedlings are grown in one plug container, and the plug seedlings in the container are cultured integrally. Therefore,
Suzuki and Murase (1998) and Suzuki et al. (1999b) and applied machine vision and a neural network model to a non-destructive system of measuring the growth of cabbage plug seedlings populations, and reported that the neural network model was more
151
This image processor had a resolution of 512 x 512 pixel image frame. The chromaticity value of the G component was calculated from the 256 gray-tone levels of the RGB signals using equation 1.
suitable than the soil coverage model. In those reports, inputs to the neural network were the soil coverage and the standard deviation of the lightness of pixels measured by machine vision; the output was average leaf area. The predicted average leaf areas for the plug seedlings populations fitted well with the actual leaf areas and were impartial.
Cg = 256G/(R+G+B)
Where: Cg = chromaticity value of G component of each pixel R = Red component value of each pixel G = Green component value of each pixel B = Blue component value of each pixel
The model developed in those studies was trained and tested using a type of planting system without missing plants. However, missing plants occur frequently in raising seedlings periods, and patterns of planting cabbage plug seedlings populations often vary. This report examines an adaptation of the objective and non-destructive growth measurement system for plug seedlings populations raised under various planting pattern according to missing plants.
2.
GROWTH INDICES OBTAINED MACHINE VISION
(1)
The seedlings images were extracted from their backgrounds using the relative thresholds of the Cg. Those thresholds were selected by a method of discriminant analysis. However, objects of fewer than 10 pixels were excluded as noise.
BY
2.2 Growth Indices 2.1 Extraction of Cabbage Plug Seedlings Images
In this report, the soil coverage and the standard deviation of the images' pixel lightness were obtained by machine vision as growth indices. The soil coverage was determined by calculating the ratio of pixels to only the part of the image that represented the seedlings. The lightness value of each pixel was calculated using equation 2.
Plug seedlings populations of cabbage (Brassica oleracea L. var. capitata cv. Matsunami) were grown in a greenhouse in black plug containers filled with a growth medium. A plug seedlings population consisted of 64 plug seedlings. Digital color images of the populations were taken under artificial conditions with a video camera fixed about 1.5 m above the seedlings. Each image included the plug seedlings and a background, and each image captured a single population.
L
= O.2988R+O.586G+O.lI44B
(2)
Where: L = lightness value of each pixel The standard deviation of the lightness of an image was calculated using equation 3.
ccd camera
image processer
N
I......t=
(L: (L-Il/(N-l)
(3)
i= 1
about
artificial light
1.5m
Where:
o
I......t = The standard deviation of lightness of the cabbage plug seedlings image
[ = The average of the lightness values for
plug seedlings population
-- IlUlll'!?;
the plug seedlings image N computer
= The number of pixels for the plug seedlings image
black box 3. NEURAL NETWORK MODEL
Fig. I Image-capture equipment
The resulting RGB signals were resolved into 256 gray-tone levels by an image processor (LA555, PIAS Inc.). The image-capture equipment is illustrated in Fig. 1.
3.1 Structure of Neural Network Model A three-layered neural network was used to model the relationship between the total leaf area of a cabbage plug seedlings population and the growth
15 2
indices obtained by machine VISIon. The neural network contained two units in the input and one unit in the output layers. The inputs to the neural network were the soil coverage and the standard deviation of the lightness. A single output to the neural network model was the total leaf area.
0.8
leaf area output layer
0.2
o middle layer
o
500 1000 1500 2000 2500 total leaf area(cm2 )
input layer
Fig. 3 Relationship between soil coverage and total leaf area of training data sets
soil standard deviation coverage of lightness Fig. 2 Structure of neural network
'"'"
8
:i.....
3.2 Training Data Sets
0
.... :t . ...
32 30 28
.Q
Training data sets had been used in a previous report (Suzuki et a1.,2000) for the non-destructive system of measuring the average leaf area. Plug seedlings populations for training data sets were grown from 25 July 1996 to 21 August 1996 and did not include missing plants. Twenty-four data sets were obtained in a single growth period of a training cabbage plug seedlings population. Each of the data sets contained the soil coverage, the standard deviation of lightness, and the associated total leaf area of the population. Five plug seedlings were sampled from the population after the digital image was captured. The leaf area of the sampled plug seedlings was measured destructively by an image scanner (GT8oo, Epson Inc.) and an image processor (LA555, PIAS Inc.). The average leaf area of the sampled plug seedlings was multiplied by 64, the number of seedlings in a population; the resulting value represented the total leaf area of the plug seedlings population. The total leaf area of the data sets for training the neural network varied from 64.64 cm2 to 2493.44 cm2 per population.
.i;.
"0
••
•
c
26 24
'E ~ c
22
'"
20
S
0
500
1000 1500 2000 2500
total leaf area( cm2) Fig. 4 Relationship between standard deviation of lightness and total leaf area of training data sets
3.3 Training of Neural Network Model The number of units in the middle layer and the training times affect the performance of the neural network. The neural networks contained 2, 3, 4, and 5 hidden units with 50, 100, 250, and 500 training times, respectively, examined. The Kalman neuron training algorithm (Murase et al., 1994) was employed as the procedure for training the neural network. A suitable number of hidden units and training times were determined by the average absolute error between the estimated output and the actual output of the training data sets. As a result, the neural network containing 4 hidden units with 500 training times was used in this study.
The relationship between the soil coverage and the total leaf area is shown in Fig. 3. The soil coverage increased proportionally as the total leaf area increased to about 1,200 cm2 per population. After that it tended to increase slightly. The relationship between the standard deviation of lightness and the total leaf area is shown in Fig. 4. The standard deviation of lightness tended to increase as the total leaf area increased to about 1,000 cm 2 per population. After that it tended to decrease gradually
4. ADAPT ABILITY TO POPULA nONS WITH MISSING PLANTS
153
4.1 Various Planting Patterns According to Missing Plants
Fig. 6 shows original images of evaluation plug seedlings populations 4 days after seedlings emerged. Fig. 7 shows original images of evaluation populations 14 days after seedlings emerged.
In order to evaluate the adaptability of the non-destructive measurement system, four types of various planting patterns of evaluation plug seedlings populations were prepared by artificially omitting plants. The evaluation population contained the planting pattern 88a (planting density was 88% compared with the nonnal population without missing plants), 88b (planting density was the same as that of 88a but with different positions for the missing plants), 75a (planting density was 75% compared with the nonnal population without missing plants) and 75b (planting density was the same as that of 75a but with different positions for the missing plants). The various positions for the missing plants are illustrated in Fig. 5.
•• •• •• •• ••• • • • • •• •• • ••• ••••• • ••••••••• •••••••• ••• •• •88a• •• • ••• ••• ••• •• •• •• •• • •• •• •••••••• •••• ••• •••le • •• • •• •
88a
•• • •• • • •• •• • • •• ••• •• •• •• •• •• •• •• •• •• •• •• •• •• •• ••
75a
•• ••
4.2 Evaluation Data Sets
The populations for evaluation data sets were grown from 12 August 1998 to 9 September 1998. Each planting pattern contained 8 populations. Only one evaluation data set was made from each of the 8 populations at each growth stage. The evaluation data sets also consisted of the soil coverage, the standard deviation of lightness, and the associated total leaf area. The soil coverage and the standard deviation of lightness were measured in the same manner as the training data sets. Five plug seedlings were sampled after a digital image of the population was captured. The total leaf area of each planting pattern was calculated using equation 4.
• •
Lt = pLs
88a
75b
Fig. 7 Original images of populations 14 days after seedlings emerged
•• ••88b•• •• • • •• • • •••• • •• • •• • • •• •• • •• ••• •••• •• •• •••• ••
75a 75b Fig. 5 Various position for missing plants
88b
Where: Lt
88b
(4)
= The
total leaf area of plug seedlings population p = The number of plug seedlings in each population (planting pattern) Ls = The average leaf area of the sampled plug seedlings
75a
There were 32 evaluation data sets. The adaptability of the non-destructive measurement system was evaluated by comparing the predicted results with the actual total leaf area of the evaluation data sets.
75b
Fig. 6 Original images of populations 4 days after seedlings emerged
154
5. RESULTS AND DISCUSSION The relationship between soil coverage and total leaf area is shown in Fig. 8. The soil coverage of every planting pattern increased proportionally to increases in total leaf area until the middle raising stage of the seedlings; after that stage, coverage tended to increase slightly.
plants. Fig. 10 shows the total leaf areas predicted by the developed neural network, using the evaluation data sets of planting patterns 88a, 88b, 75a, and 75b. The relationship between the predicted total leaf area and the actual total leaf area is linear. The coefficient of determination, R2, is 0.93, and the standard error is 173.5. 2500
~~.
~ /0
0.8
a~
0.6
~ 0
u
0.4
r= 0
'"
f1
0.2
E
~
] 1500 ~
.88.
] 1000 .101
-,::)
088b
.. 75a f:j. 75b
2:! Co 500
.. 75a
o
b. 75b
o
o o
SEP*=173.5 m.d**=15.9 R2=().93 I
",'-'" 2000
500 1000 1500 2000 2500 total leaf area (cm2)
500
1000 1500 2000 2500 actual varue( cm2)
*:standard error of prediction **mean of difference between actual and predicted vaiJe Fig. 10 Predicted and actual value of total leaf area
Fig. 8 Relationship between soil coverage and total leaf area of evaluation data sets The relationship between the total leaf area and the standard deviation of lightness is shown in Fig. 9. The standard deviation of lightness of every planting pattern tended to increase with increases in the total leaf area until the middle raising stage of the seedlings; after that stage, the standard deviation tended to decrease gradually.
The predicted total leaf areas for the plug seedlings populations with missing plants fitted well with the actual leaf areas and were impartial. It was possible to apply the developed non-destructive measurement system, that was trained by the normal plug seedlings population without missing plants, to cabbage plug seedlings populations with various planting patterns according to missing plants.
38
'"'" 36
5
j
'0
c .Q
.i -8 ~
"E
1S '"
tJo~t1~.
34 32 30 28 26 24 22 20 0
.88a
088b
.. 75a
b. 75b
REFERENCE Hack., G (1989) . The use of image proces sing under greenh ouse conditi on for growth and climate control . Acta Hort, 230, 215-22 0. Iwao, K., Kageyama, H., Tanimura, Y., Ezaki, K., Kawai, Y. and Nakamura, S (1991). Study on plant factory with f10resc ent lamps (2). Develo pment of monito ring techniq ue of vegetable growth by image processing, (in Japanese with English summary). Environ. Contro l in Bioi., 29 (2), 89-96. Muras e, H., Koyam a, S. and Ishida, R (1994) . Kalma n neuro compu ting by person al Computers. (in Japanese). Morikitashuppan, 175pp . Shibat a, T ., Iwao, K. and Takan o, T (1993) . Develo pment of autom atic plant growth measurement system by image processing. (in Japanese with English summary). Environ. Contro l in Bioi., 31 (1),29 -35.
500 1000 1500 2000 2500 total1eaf area (cm2)
Fig. 9 Relationship between standard deviation of lightness and total leaf area of evaluation data sets In this study, it was confirmed that the soil coverage and standard deviation of lightness of plug seedlings populations with missing plants varied with changes in the total leaf area similarly to the training plug seedlings populations without missing
155
Suzuki, T and Murase, H (1998). Non-destructive growth measurement of cabbage plug seedlings population by machine vision, Proceeding of the 3rd IFAClCIGR Workshop on AI in Agriculture, 104-107. Suzuki, T., Murase, H. and Honami, N (1999a). Non-destructive growth measurement of cabbage plug seedlings population by image information (1). Measurement of top fresh weight by soil coverage model. (in Japanese with English summary) J.JSAM,61 (1), 45-52. Suzuki, T., Murase, H. and Honami, N (l999b). Non-destructive growth measurement of cabbage plug seedlings population by image information (2) . Growth measurement by neural network model. (in Japanese with English summary) J.JSAM,61 (2), 65-72 . Suzuki, T. and Murase, H (2000). Objective and non-destructive growth measurement of cabbage plug seedlings population using machine vision. (in Japanese with English summary). J.SHTA,12 (1) 4-9.
156
NON-DESTRUCTIVE GROWTH MEASUREMENT BY MACmNE VISION FOR CABBAGE PLUG SEEDLINGS POPULATION WITH MISSING PLANTS
T. SUZUKI', H. MURASE'
'Osaka Prefecture Agricultural & Forestry Research Center, 442, Shakudo, Habikino, Osaka, Japan 'Faculty of Agriculture, Osaka Prefecture University, 1-1, Gakuen, Sakai, Osaka, Japan
Abstract: This report examines an adaptation of a non-destructive growth measurement system for plug seedlings populations with various planting patterns that vary according to missing plants. A three-layered neural network was used to model the relationship between the total leaf area of a cabbage plug seedlings population and growth indices that were obtained by machine vision. It was confmned that the growth indices of the plug seedlings populations with missing plants varied similarly to the growth indices of plug seedlings populations without missing plants. The predicted total leaf areas for the plug seedlings population with missing plants fitted well with the actual total leaf areas. Copyright © 2000IFAC Keywords: machine vision, neural network model, cabbage plug seedlings population, missing plants
we regarded the plug seedlings in one plug container as a population of plug seedlings and examined the non-destructive growth measurement of such populations.
I. INTRODUCTION Large-scale systems for producing vegetable plug seedlings have been spreading in Japan. Raising plug seedlings in a large-scale production system requires the ability to objectively estimate the growth of seedlings. Destructive measurements of growth, such as determining the leaf area or the top fresh weight, are very laborious and reduce the number of seedlings. Non-destructive growth measurement by machine vision is suitable for objective estimation.
Suzuki et al. (1999a) have reported the non-destructive measurement of cabbage plug seedlings populations using the machine vision and soil coverage models. The images of cabbage plug seedlings populations were extracted from their backgrounds using the relative threshold values of the chromaticity G component. The relative threshold values could be selected objectively by a method of discriminant analysis (Suzuki and Murase, 2000).
There have been numerous studies on non-destructive growth measurement by machine vision. For example, Hack (1989), Iwao et al. (1991), and Shibata et al. (1993) have used machine vision to non-destructively measure plant growth, but for single plants only. Many plug seedlings are grown in one plug container, and the plug seedlings in the container are cultured integrally. Therefore,
Suzuki and Murase (1998) and Suzuki et al. (1999b) and applied machine vision and a neural network model to a non-destructive system of measuring the growth of cabbage plug seedlings populations, and reported that the neural network model was more
151
This image processor had a resolution of 512 x 512 pixel image frame. The chromaticity value of the G component was calculated from the 256 gray-tone levels of the RGB signals using equation 1.
suitable than the soil coverage model. In those reports, inputs to the neural network were the soil coverage and the standard deviation of the lightness of pixels measured by machine vision; the output was average leaf area. The predicted average leaf areas for the plug seedlings populations fitted well with the actual leaf areas and were impartial.
Cg = 256G/(R+G+B)
Where: Cg = chromaticity value of G component of each pixel R = Red component value of each pixel G = Green component value of each pixel B = Blue component value of each pixel
The model developed in those studies was trained and tested using a type of planting system without missing plants. However, missing plants occur frequently in raising seedlings periods, and patterns of planting cabbage plug seedlings populations often vary. This report examines an adaptation of the objective and non-destructive growth measurement system for plug seedlings populations raised under various planting pattern according to missing plants.
2.
GROWTH INDICES OBTAINED MACHINE VISION
(1)
The seedlings images were extracted from their backgrounds using the relative thresholds of the Cg. Those thresholds were selected by a method of discriminant analysis. However, objects of fewer than 10 pixels were excluded as noise.
BY
2.2 Growth Indices 2.1 Extraction of Cabbage Plug Seedlings Images
In this report, the soil coverage and the standard deviation of the images' pixel lightness were obtained by machine vision as growth indices. The soil coverage was determined by calculating the ratio of pixels to only the part of the image that represented the seedlings. The lightness value of each pixel was calculated using equation 2.
Plug seedlings populations of cabbage (Brassica oleracea L. var. capitata cv. Matsunami) were grown in a greenhouse in black plug containers filled with a growth medium. A plug seedlings population consisted of 64 plug seedlings. Digital color images of the populations were taken under artificial conditions with a video camera fixed about 1.5 m above the seedlings. Each image included the plug seedlings and a background, and each image captured a single population.
L
= O.2988R+O.586G+O.lI44B
(2)
Where: L = lightness value of each pixel The standard deviation of the lightness of an image was calculated using equation 3.
ccd camera
image processer
N
I......t=
(L: (L-Il/(N-l)
(3)
i= 1
about
artificial light
1.5m
Where:
o
I......t = The standard deviation of lightness of the cabbage plug seedlings image
[ = The average of the lightness values for
plug seedlings population
-- IlUlll'!?;
the plug seedlings image N computer
= The number of pixels for the plug seedlings image
black box 3. NEURAL NETWORK MODEL
Fig. I Image-capture equipment
The resulting RGB signals were resolved into 256 gray-tone levels by an image processor (LA555, PIAS Inc.). The image-capture equipment is illustrated in Fig. 1.
3.1 Structure of Neural Network Model A three-layered neural network was used to model the relationship between the total leaf area of a cabbage plug seedlings population and the growth
15 2
indices obtained by machine VISIon. The neural network contained two units in the input and one unit in the output layers. The inputs to the neural network were the soil coverage and the standard deviation of the lightness. A single output to the neural network model was the total leaf area.
0.8
leaf area output layer
0.2
o middle layer
o
500 1000 1500 2000 2500 total leaf area(cm2 )
input layer
Fig. 3 Relationship between soil coverage and total leaf area of training data sets
soil standard deviation coverage of lightness Fig. 2 Structure of neural network
'"'"
8
:i.....
3.2 Training Data Sets
0
.... :t . ...
32 30 28
.Q
Training data sets had been used in a previous report (Suzuki et a1.,2000) for the non-destructive system of measuring the average leaf area. Plug seedlings populations for training data sets were grown from 25 July 1996 to 21 August 1996 and did not include missing plants. Twenty-four data sets were obtained in a single growth period of a training cabbage plug seedlings population. Each of the data sets contained the soil coverage, the standard deviation of lightness, and the associated total leaf area of the population. Five plug seedlings were sampled from the population after the digital image was captured. The leaf area of the sampled plug seedlings was measured destructively by an image scanner (GT8oo, Epson Inc.) and an image processor (LA555, PIAS Inc.). The average leaf area of the sampled plug seedlings was multiplied by 64, the number of seedlings in a population; the resulting value represented the total leaf area of the plug seedlings population. The total leaf area of the data sets for training the neural network varied from 64.64 cm2 to 2493.44 cm2 per population.
.i;.
"0
••
•
c
26 24
'E ~ c
22
'"
20
S
0
500
1000 1500 2000 2500
total leaf area( cm2) Fig. 4 Relationship between standard deviation of lightness and total leaf area of training data sets
3.3 Training of Neural Network Model The number of units in the middle layer and the training times affect the performance of the neural network. The neural networks contained 2, 3, 4, and 5 hidden units with 50, 100, 250, and 500 training times, respectively, examined. The Kalman neuron training algorithm (Murase et al., 1994) was employed as the procedure for training the neural network. A suitable number of hidden units and training times were determined by the average absolute error between the estimated output and the actual output of the training data sets. As a result, the neural network containing 4 hidden units with 500 training times was used in this study.
The relationship between the soil coverage and the total leaf area is shown in Fig. 3. The soil coverage increased proportionally as the total leaf area increased to about 1,200 cm2 per population. After that it tended to increase slightly. The relationship between the standard deviation of lightness and the total leaf area is shown in Fig. 4. The standard deviation of lightness tended to increase as the total leaf area increased to about 1,000 cm 2 per population. After that it tended to decrease gradually
4. ADAPT ABILITY TO POPULA nONS WITH MISSING PLANTS
153
4.1 Various Planting Patterns According to Missing Plants
Fig. 6 shows original images of evaluation plug seedlings populations 4 days after seedlings emerged. Fig. 7 shows original images of evaluation populations 14 days after seedlings emerged.
In order to evaluate the adaptability of the non-destructive measurement system, four types of various planting patterns of evaluation plug seedlings populations were prepared by artificially omitting plants. The evaluation population contained the planting pattern 88a (planting density was 88% compared with the nonnal population without missing plants), 88b (planting density was the same as that of 88a but with different positions for the missing plants), 75a (planting density was 75% compared with the nonnal population without missing plants) and 75b (planting density was the same as that of 75a but with different positions for the missing plants). The various positions for the missing plants are illustrated in Fig. 5.
•• •• •• •• ••• • • • • •• •• • ••• ••••• • ••••••••• •••••••• ••• •• •88a• •• • ••• ••• ••• •• •• •• •• • •• •• •••••••• •••• ••• •••le • •• • •• •
88a
•• • •• • • •• •• • • •• ••• •• •• •• •• •• •• •• •• •• •• •• •• •• •• ••
75a
•• ••
4.2 Evaluation Data Sets
The populations for evaluation data sets were grown from 12 August 1998 to 9 September 1998. Each planting pattern contained 8 populations. Only one evaluation data set was made from each of the 8 populations at each growth stage. The evaluation data sets also consisted of the soil coverage, the standard deviation of lightness, and the associated total leaf area. The soil coverage and the standard deviation of lightness were measured in the same manner as the training data sets. Five plug seedlings were sampled after a digital image of the population was captured. The total leaf area of each planting pattern was calculated using equation 4.
• •
Lt = pLs
88a
75b
Fig. 7 Original images of populations 14 days after seedlings emerged
•• ••88b•• •• • • •• • • •••• • •• • •• • • •• •• • •• ••• •••• •• •• •••• ••
75a 75b Fig. 5 Various position for missing plants
88b
Where: Lt
88b
(4)
= The
total leaf area of plug seedlings population p = The number of plug seedlings in each population (planting pattern) Ls = The average leaf area of the sampled plug seedlings
75a
There were 32 evaluation data sets. The adaptability of the non-destructive measurement system was evaluated by comparing the predicted results with the actual total leaf area of the evaluation data sets.
75b
Fig. 6 Original images of populations 4 days after seedlings emerged
154
5. RESULTS AND DISCUSSION The relationship between soil coverage and total leaf area is shown in Fig. 8. The soil coverage of every planting pattern increased proportionally to increases in total leaf area until the middle raising stage of the seedlings; after that stage, coverage tended to increase slightly.
plants. Fig. 10 shows the total leaf areas predicted by the developed neural network, using the evaluation data sets of planting patterns 88a, 88b, 75a, and 75b. The relationship between the predicted total leaf area and the actual total leaf area is linear. The coefficient of determination, R2, is 0.93, and the standard error is 173.5. 2500
~~.
~ /0
0.8
a~
0.6
~ 0
u
0.4
r= 0
'"
f1
0.2
E
~
] 1500 ~
.88.
] 1000 .101
-,::)
088b
.. 75a f:j. 75b
2:! Co 500
.. 75a
o
b. 75b
o
o o
SEP*=173.5 m.d**=15.9 R2=().93 I
",'-'" 2000
500 1000 1500 2000 2500 total leaf area (cm2)
500
1000 1500 2000 2500 actual varue( cm2)
*:standard error of prediction **mean of difference between actual and predicted vaiJe Fig. 10 Predicted and actual value of total leaf area
Fig. 8 Relationship between soil coverage and total leaf area of evaluation data sets The relationship between the total leaf area and the standard deviation of lightness is shown in Fig. 9. The standard deviation of lightness of every planting pattern tended to increase with increases in the total leaf area until the middle raising stage of the seedlings; after that stage, the standard deviation tended to decrease gradually.
The predicted total leaf areas for the plug seedlings populations with missing plants fitted well with the actual leaf areas and were impartial. It was possible to apply the developed non-destructive measurement system, that was trained by the normal plug seedlings population without missing plants, to cabbage plug seedlings populations with various planting patterns according to missing plants.
38
'"'" 36
5
j
'0
c .Q
.i -8 ~
"E
1S '"
tJo~t1~.
34 32 30 28 26 24 22 20 0
.88a
088b
.. 75a
b. 75b
REFERENCE Hack., G (1989) . The use of image proces sing under greenh ouse conditi on for growth and climate control . Acta Hort, 230, 215-22 0. Iwao, K., Kageyama, H., Tanimura, Y., Ezaki, K., Kawai, Y. and Nakamura, S (1991). Study on plant factory with f10resc ent lamps (2). Develo pment of monito ring techniq ue of vegetable growth by image processing, (in Japanese with English summary). Environ. Contro l in Bioi., 29 (2), 89-96. Muras e, H., Koyam a, S. and Ishida, R (1994) . Kalma n neuro compu ting by person al Computers. (in Japanese). Morikitashuppan, 175pp . Shibat a, T ., Iwao, K. and Takan o, T (1993) . Develo pment of autom atic plant growth measurement system by image processing. (in Japanese with English summary). Environ. Contro l in Bioi., 31 (1),29 -35.
500 1000 1500 2000 2500 total1eaf area (cm2)
Fig. 9 Relationship between standard deviation of lightness and total leaf area of evaluation data sets In this study, it was confirmed that the soil coverage and standard deviation of lightness of plug seedlings populations with missing plants varied with changes in the total leaf area similarly to the training plug seedlings populations without missing
155
Suzuki, T and Murase, H (1998). Non-destructive growth measurement of cabbage plug seedlings population by machine vision, Proceeding of the 3rd IFAClCIGR Workshop on AI in Agriculture, 104-107. Suzuki, T., Murase, H. and Honami, N (1999a). Non-destructive growth measurement of cabbage plug seedlings population by image information (1). Measurement of top fresh weight by soil coverage model. (in Japanese with English summary) J.JSAM,61 (1), 45-52. Suzuki, T., Murase, H. and Honami, N (l999b). Non-destructive growth measurement of cabbage plug seedlings population by image information (2) . Growth measurement by neural network model. (in Japanese with English summary) J.JSAM,61 (2), 65-72 . Suzuki, T. and Murase, H (2000). Objective and non-destructive growth measurement of cabbage plug seedlings population using machine vision. (in Japanese with English summary). J.SHTA,12 (1) 4-9.
156