- Email: [email protected]
Extraction of Non-Parametric Image Features for Plant Growth Control
optimal climate control in greenhouse cultivation. Proc. 1st IFACIISHS Workshop on Math., Cont. Applications in Agric. and Hort .. pp.13-18, Pergamon Press, Oxford. Chen, S., S.A. Billings and P.M. Grant (1990) Non-linear system identification using neural network. International Journal of Control, 51 (6), 1191-1214. De Baerdemaeker, J and Y. Hashimoto (1994). Speaking fruit approach to the intelligent control of the storage system. Proc. of 12th CIGR World Congress, Vol.!, pp. 190-197. Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley. Hashimoto, Y. (1991) Computer integrated plant growth factory for agriculture and horticulture. Proc. 1st IFACIISHS Workshop on Math., Cont. Applications in Agric. and Hart., pp. 105-110, Pergamon Press, Oxford. Hashimoto, Y. (1993). Computer integrated system for the cultivating process in agriculture and horticulture. In: Tbe Computerized Greenhouse (Hashimoto, Y. eds.). 175-196, Academic Press. Hunt KJ., D. Sbarbaro, R. Zbikowski and PJ. Gawthrop (1992) Neural networks for control systems - Survey. Automatica, 28(6), 1083-1112. Karr C.L. and EJ. Gentry (1993). Fuzzy control of pH using genetic algorithms. IEEE Transaction of Fuzzy Systems. 1(1), 46-53. Kitagawa, H. (1989). Storage of fruit. Refrigeration, 64(741),745-759. Lee, C.C. (1990). Fuzzy logic in control systems: fuzzy logic controller - part I. IEEE Trans. Sys., Man, Cyber.. 20(2), 404-418. Morimoto, T., T. Takeuchi and Y. Hashimoto (1993). Growth optimization of plant by means of the hybrid system of genetic algorithm and neural network. Proc. International Joint Conference on Neural Networks, VoU, pp.2979-2982. Morimoto, T., J. De Baerdemaeker and Y. Hashimoto (l995a). Optimization of storage system of fruits using neural networks and genetic algorithms. Proc. 4th IEEE International Conference on Fuzzy Systems, VoU, pp.289-294. Morimoto, T., T. Torii and Y. Hashirnoto (l995b). Optimal control of physiological processes of plants in a green plant factory. Control Engineering Practice, 3(4),505-511. Rumelhart, D.E., G.E. Hinton and R.J. Williams (1986). Learning representation by back-propagation error. Nature, 323(9),533-536. Seginer, I. and McClendon (1992). Methods for optimal contro1 of the greenhouse environment. Transaction of ASAE,35(4), 1299-1307. Taksgi, T., S. Nakanishi, K. Unehara and Y. Gotoh (1990). Construction of self-
optimal fuzzy control performance proposed here, and the dotted line denotes the control perfonoance by a simple onoff feedback control which means a feedback control based on the on-
REFERENCES Challa, H and G. van Straten (1991). Reflections about
883
Copyright © 1996 IFAC 13th Triennial World Congress. San Francisco. USA
4a-O 1 2
EXTRACTION OF NON-PARAMETRIC IMAGE FEATURES FOR PLANT GROWTH CONTROL H . Murase , Y. Nisbiura, T.Suzuki Lab. of Bi oi nstrumentation, Control andS yste ms Engineerin g Department of R egio na. Environmental S c ;~nc es Uni ve rsit y of OUR a Prefecture 1-1, Gakuen, Sakai, 593, JAPAN
Abstract:The interpretation of image information of plant s can be possibly done based on image features ex.tracted from the original pictorial image. This research work. focused its interest on techniCfJ es of n o n-parametri c image ex tracti o n . Two different t ypes of non-param e tric image features were discussed. First. the textural analysis which can be recognized as o ne of conventi onal non-parametri c i m age feat ure extractors was exam i ned. Seco ndly , The finite e lement method was introduced as an informati o n processing tool for image feature extracti on. The finite clement application i n image processing fealures a unique image data com(lre~sion which is apparently s imilar 10 th e function of huma n retina . Keywords :Computer vision , Textural features. Finite e lement S peaking P lan t Approach, Phyt o techn ol ogy. Image co mpress ion
I . INTRODUcnON In 1986, Phytotechnology was conccptualized by several Japanese founders who happe ned to be agricultural e ngineeri ng scie ntists . In the same year th e Japane..~ association for Phytotechnology was also established in collaboration with researchers in man y disciplines such as horticulture, soH science, agronomy as well as agricu1tural engineering. Phytotechnology has been in progress toward the
for
~cientific
development and te<.:hn ological advancement
the estab lishment of a new bioproduction system on the basis of environment, quality, safety and economy. One of major challenges or strategies of Phytotechnology is to implement the speaking plant approach (De Baerderrlreker and Hashim oto, 1994) in a practical sense by uti1i zing all interdisciplinary sc ientific and technological kn owJedges am skills available, Up to now a lot of useful information and
method.
technolog ies 8!'isocia ted with the progress of phytotechnology researches ha ve been accumulated (Phytotechnology Kenkyukai. 1994). In a protected plant production system such as a plant factory, the control applicati ons have been limited to its environmental controls. The feedback control technology for greenhouse environmental factors such as temperature, humidity, radiation inte nsity. ca rbon dioxide concentration and so forth has been developed and successfully implemented (Hashim oto and Nonami, 1992). The development of bio- response fecdhack control system has been a chall enging task for plant production engineers scientists. The plant growth can be optirnized or controlled by adjusting the environmental facto rs. Plan ts respond to the change of environmental parameters. For example, the stomata activity is sencitive to ambient humidity and CO2 concentration. The plant tissue rigidity is affected by the
am
884
monitored by measuring physical indices such as stem length, number of leaves, plant weight (dry or wet basis), leaf color and so forth. The leaf water potential which gives an essential factor indicating plant water status can be measured by attaching a thermocouple psychrometric transducer directly to the reverse side of a leaf where the stomata are located or extracting a small sample tissue from • leaf, which is placed in a chamber of a psychrometric transducer. The latter method is a destructive technique. Even a slight contact o f foreign material with the plant tissue disturbs physiological activity of the plant. The practice of non-destructive measurements against the plants is essential for the bio-response feedback andlor feed-forward control system. Few non-destructive methods for direct meas ureme nt of leaf water potential aer currently available.
One of alternatives is the use of indirect measurement techniques.
For
instance, those who
are skilled
,
/ - -- - - - . . ' -+--+-hresolu1ion cell
Fig. 1 Co-occurrence matrix.
In such a case that d= 1 and 9=0, some of the textural features are calculated as follows :
in
raising plants can sense whether their plants are under
(a)Homogeneity:
adequate water conditions or not from minor changes in the
~
appearance of their plants through their color and tone hefore the plants wilt. It may be possible to diagnose planl conditio ns fro m the appearance of plants. In order to achieve the aim of de veloping such a feedback control system bascd on the speaking plant approach, the primary concen} should be to develop image handling techniques for
monitoring the plant growing status. The success ful plant status analysis using image information depends on how much expressive image features can be extracted from the original image. This research work focused its interest on techniques of nonparamenic image extraction. Two different types of nonparametric image features were discussed . In this papar, non parametric image feature is defined in such a manner that non parametric image features are obtained based on not dimentional quan titi es such as length or area of drtaine part of a image but non dimenlional quantiti es such as brightness or contrast o r pattern. Firs t. Ihe textural analysis which can he recognized as unc of conventional nonparamctric image feature extractors was examined. Secondly. The finite element method was introduced as an infonnation processing tool for image feature extraction. The finite element application in image processing features a unique image data compression which is apparently similar to the function of human retina. 2. TEXTURAL FEATURES
The texturc-contex information is adequately specified by the matrix of relative frequencies P jj with which two neighboring resolution cells separated by distance d occur on the image , one with gray tone i and the other with gray lonc J as shown in Fig. I (Hardlick et al. , 1973). The joint pro bability density function is expressed by the notatio n P(d,a )(i ,j).
t
P (1,0) (i,j)
(I)
2
i=l j=l (b)Contrast:
n
n
E E (i-j) 2 P(l,O) (i,j) i=l
(2)
j=l
(c)Local Homogeneity:
n
n
E E (P(l,o/i,j) llr 1 + (i-j)2l
(3)
i=J j=J The perfonnance o r specification of the co-occurrence matrix can be sel by fixing d and values. The choice of d and e values detennines the sensitivity of the textural
a
features . 3. CHANGES IN TEXTURAL FEATURES DUE TO VARIATION OF LEAF WATER POTENTIAL
Figure 2 illustrates the experimental setup used for collecting the data indicating the relationships berween the leaf water potential and textural features. A video camera was set at a fixed position over the plant canopy (chrysanthemum). The video carnera was focused on the canopy. The pictorial data were collected under artificial lighling to provide a constant illumination condition. The destruclive technique was used to measure the leaf water potential that gives more accurate data than noodestructive one. At a time of the water potential
885
measurement, a few leaves were detached from the stem. From it, 3 disk-shape specimens were cut for the measurement. The plant had been grown under adequare water conditions before the measurement started. From the moment when the measurement started the water supply to the plant was suspended. Data were collected for five days until the plant reached its wilting point. The five sets of quantized gray tone s of the green component of the ROB data obtained from the domain consisting of 50 • 50 resolution ceUs were collected as digital images. The textural features were calculated from the gray-tone spatialdependence matrix for 16 gray levels constructed from the five sets of digital image data. Table I indicates the textural features calculated from the gray-tone spatia]dependence matrix. The variation of the leaf water potential is reflected on all of the textural parameters in order.
LAM:rI
....
4. FINITE ELEMENT FEATURES Figure 3 shows a schematic representation of the finite element image processing grid that converts pictorial image into non-parametric image features numerically. This non paramclric image featUI"C can he calculated based on the differences of brightness level between every input node of the finite element grid. Each of input nodes serves as a photosensitive rcccptor. In practice, for instance. signals transferred from sensing elements of CCD area array should be given to the input nodes. Nodal values of the output nodes become the finite element features. Other nodes are boundary nodes on which boundary conditions are specified.
Triangular Finite Element
VIDEO CAMERA Output Input N<>a"",~u
Node
Fig. 3. Finite element image processing grid. The algorithm to relate input and output signals of the
FlNDERVIEW
CHRYSANTHEMUM
mechanism of finite element image processing grid for generating image features is the conversion of incident light intensity distribution projected over the area comprising of
Fig. 2. Experimental set-up
TABLE I Change in textural features due to wata l.'!~ntj!!1
Average leaf leaf water potential (MP. )
-0.70 -0.95 -1.40 -2.15 -2.70
finite element image processing grid can be a linear mapping as described by a linear finite element equation, In this research work. 2-D Poisson's equation was utilized as a governing equation given by Eq.(4). The finite element equation used here is expressed as Eq.(5). The basic
variatiQn of ls;:aves.
finite element input nodes into a vector fonn of image features dislributed over the output nodes.
i'Jq,2
ISc - iJ2x
Thxtural features Contrast
Homogeneity
0.54 0.72 1.26 1.93 2.49
0.97 0.75 0.44 0.39 0.38
Local
Jq,2
+ K -Y
iJ2y
=Q
(4)
Homogeneity ~:information
0.52 0.41 0.30 0.30 0.27
conductivity in
)t
direction
'S-:information conductivity in y direction q, :potential Q :constant
886
A
2
[Kj-l
= C
Tomato Seedling
CCD
B
3 (5)
[Kl-l: inverse matrix of stiffness matrix lA) : input vector I I ) :oulpul veclor (image features) The performance or specification of the finite element infonnation processing grid can be set by fixing K values and the node arrangement The choice of K vaJues and the
node arrangements determines the sensitivity of the grid.
Fig.4 Experimental selup,
The K value is usually laken as the unity, The number of nodes is depending on allowable calculalion capacily, The arrangement of nodes is arbitrary. However some trial procedure is usually required to optirnize the grid performance.
30mio.
60mio,
90m;0.
5, CHANGES IN FINITE ELEMENT FEATURES
DUE TO VARIATION OF PLANT WATER CONTENT A tomato seedling (30 days after seedi ng) was used 10 obtain input data of a finite element im age processing grid. A single tomato seedling was placed in a growlh chamber 10 provide proper temperature, CO and light conditions as 2 illustrated by Fig, 4, Water supply la the seedling was stopped to observe change in appearance of wilting plant. The water loss from the plant was measured continuously by the electronic balance on which the plant with water pot was set. Movement of leaves and stems of the wilting plant due to the loss of turgidity of tissues was nx..-orded on a video taIX through a CCD color camera, An example of binary image of the wilting planl (30, 60 and 90 minutes after slopping water supply) is shown in Fig. 5. For extraction of finite element features, brighlness distribution in 256 gray scale over captured image frame of the wilting plant was fully used as input data. Figure 6 illustrates the procedure to prepare input data for the finite element image processing grid . The captured still image frame was divided into 12 x 12 segments. The image dimensions of each segment were 25 x 30 pixels. The average value of brightness (Y signal) of each segment was calculated. The calcu1ate average brightness values rmrle 144 input data which were fed inlo Ihe finite element image processing grid. Figure 7 shows the finite element image processing gird wilh 144 input nodes corresponding to the input data obtained from the· image frame. Six nodes were selected for OUlput nndes as shown in fig,7,
Fig,5, Change in appearance of wilting plant.
~'--
360 pixelsll2 sC!,'TTlents
' Averag(:d Brightness Fig, 6, Digital image frame consisting of 144
segments.
887
I...
~ r....
I •
. - NODENO.t
~
r-
t-
'"~
_ _ _ NOOENO..2
l-
.....-. NOOE NO.3
I'"
~"
ffi
_
NODENOA
~
NODENO.5
~
NODE N o .e
>
<
~OOOO 000029t_~ O.
1600
I·····~: !!'x::J~ :::!!.~ H' ~~~
~~4'~4·1i Q 0 .,
~£~~IID
.-.i,<. .
~
•8
0 ............
0 . . . . . . . . . . . .0 0 • • • • • • • • • • • •0
0000 0000000 0 0 0 Fig. 7. Each of segments of the image frame is coincident with the finite element input node in order. The variations of the finite element features with time after wilting stoned were plotted in Fig.S. The output nodes closer to the center of the output node array are the more sensiti ve to the change in plant appearance in the image frame. The relationship between water content of the plant and Ihe value of the finite element feature obtained at the node no. 6 is indicated in Fig.9 as an example. As shown in Fig.9. a strong non-linearity exists in the relationship between water content of the plant and Ihe value of lhe finite element feature. Figure 9 is not meant to interpret any obvious physical dependency of Iwo variables. Figure 9 indicates that the variation of pictorial image of plant reflecting the change in water content of the plant can be characterized by a pattern of a finite element feature . The manipulaled combi nation of all finite elemenl fealures oblained from an image frame of the plant should be able 10 identify the physiological status of plant.
6. COMPARISON BETWEEN TEXTURAL FEATURES AND FINITE ELEMENT FEATURES As indicated in Ihe previous sections, both methods gave fairl y good results in terms of describing the variation of digital image of plants. It is worth Irying 10 compare the two methods on the same input image.
H+H+H++-IH-i
1~0"~~at~~~~~ o
20
40 60 80 100 120 TIME - MINUTES
Fig. 8. Variation of the finite element features wilh time after wi ltin g started.
6. 1 Textural features Figure 10 s hows an image frame of a community of young seedlings of cabbage taken by a CCD colar camera. 11le 56 cabbage seedlings, of which rool parts had been cui at the lime when aboul 27 days of growth after seeding, were stuck into a stylofoam board at uprighl posilion in order 10 lel them dehydrate. The board with Ihe cabbage seedlings was placed on an electronic balance to monitor the weight change due to water loss from the p1ants. The change of appearance of the cabbage seedling community during the dehydration process was recorded in the form of digital image on a hard disk of image analyzer through a CCD color camera. Eleven image frames were sampled in the interval of about 35 minutes from the time when the foot parts were detouched. The eleven sets of quanti zed gray lanes (NTSC Y signal) of piclorial data obtained from .Ihe domain consisting of 100 x 100 resolution cells were collecled as digilal images. The lextural features were calculated from Ihe gray-lone spatial-dependence malrix for 16 gray levels constructed from each of .he eleven sets of digital image dala.
Figure 11 indicates three items of textural fea tures, i.e. I) Homogeneily, 2) Conlrasl and 3) Local Homogeneity varying wilh its level of dehydration of plants. The trends of contrast and homogeneity are either proponional or inverse proportional roughly to the change in plants weight.
6.2 Finite Element Features For this test, a 10 by 10 finite element image processing element was used. The image dimensions of each segment were IO x IO pixels. The average value of brightness (Y signal) of each segmenl was calculated. The calculate average brightness values made 100 input data which were fed into the finite element image processing grid. The variations of the finite element features wi th change in plants weighl afler Ihe dehydralion stoned were ploued in Fig.12.
888
optimal fuzzy control performance proposed here, and the dotted line denotes the control perfonoance by a simple onoff feedback control which means a feedback control based on the on-
REFERENCES Challa, H and G. van Straten (1991). Reflections about
883
Copyright © 1996 IFAC 13th Triennial World Congress. San Francisco. USA
4a-O 1 2
EXTRACTION OF NON-PARAMETRIC IMAGE FEATURES FOR PLANT GROWTH CONTROL H . Murase , Y. Nisbiura, T.Suzuki Lab. of Bi oi nstrumentation, Control andS yste ms Engineerin g Department of R egio na. Environmental S c ;~nc es Uni ve rsit y of OUR a Prefecture 1-1, Gakuen, Sakai, 593, JAPAN
Abstract:The interpretation of image information of plant s can be possibly done based on image features ex.tracted from the original pictorial image. This research work. focused its interest on techniCfJ es of n o n-parametri c image ex tracti o n . Two different t ypes of non-param e tric image features were discussed. First. the textural analysis which can be recognized as o ne of conventi onal non-parametri c i m age feat ure extractors was exam i ned. Seco ndly , The finite e lement method was introduced as an informati o n processing tool for image feature extracti on. The finite clement application i n image processing fealures a unique image data com(lre~sion which is apparently s imilar 10 th e function of huma n retina . Keywords :Computer vision , Textural features. Finite e lement S peaking P lan t Approach, Phyt o techn ol ogy. Image co mpress ion
I . INTRODUcnON In 1986, Phytotechnology was conccptualized by several Japanese founders who happe ned to be agricultural e ngineeri ng scie ntists . In the same year th e Japane..~ association for Phytotechnology was also established in collaboration with researchers in man y disciplines such as horticulture, soH science, agronomy as well as agricu1tural engineering. Phytotechnology has been in progress toward the
for
~cientific
development and te<.:hn ological advancement
the estab lishment of a new bioproduction system on the basis of environment, quality, safety and economy. One of major challenges or strategies of Phytotechnology is to implement the speaking plant approach (De Baerderrlreker and Hashim oto, 1994) in a practical sense by uti1i zing all interdisciplinary sc ientific and technological kn owJedges am skills available, Up to now a lot of useful information and
method.
technolog ies 8!'isocia ted with the progress of phytotechnology researches ha ve been accumulated (Phytotechnology Kenkyukai. 1994). In a protected plant production system such as a plant factory, the control applicati ons have been limited to its environmental controls. The feedback control technology for greenhouse environmental factors such as temperature, humidity, radiation inte nsity. ca rbon dioxide concentration and so forth has been developed and successfully implemented (Hashim oto and Nonami, 1992). The development of bio- response fecdhack control system has been a chall enging task for plant production engineers scientists. The plant growth can be optirnized or controlled by adjusting the environmental facto rs. Plan ts respond to the change of environmental parameters. For example, the stomata activity is sencitive to ambient humidity and CO2 concentration. The plant tissue rigidity is affected by the
am
884
monitored by measuring physical indices such as stem length, number of leaves, plant weight (dry or wet basis), leaf color and so forth. The leaf water potential which gives an essential factor indicating plant water status can be measured by attaching a thermocouple psychrometric transducer directly to the reverse side of a leaf where the stomata are located or extracting a small sample tissue from • leaf, which is placed in a chamber of a psychrometric transducer. The latter method is a destructive technique. Even a slight contact o f foreign material with the plant tissue disturbs physiological activity of the plant. The practice of non-destructive measurements against the plants is essential for the bio-response feedback andlor feed-forward control system. Few non-destructive methods for direct meas ureme nt of leaf water potential aer currently available.
One of alternatives is the use of indirect measurement techniques.
For
instance, those who
are skilled
,
/ - -- - - - . . ' -+--+-hresolu1ion cell
Fig. 1 Co-occurrence matrix.
In such a case that d= 1 and 9=0, some of the textural features are calculated as follows :
in
raising plants can sense whether their plants are under
(a)Homogeneity:
adequate water conditions or not from minor changes in the
~
appearance of their plants through their color and tone hefore the plants wilt. It may be possible to diagnose planl conditio ns fro m the appearance of plants. In order to achieve the aim of de veloping such a feedback control system bascd on the speaking plant approach, the primary concen} should be to develop image handling techniques for
monitoring the plant growing status. The success ful plant status analysis using image information depends on how much expressive image features can be extracted from the original image. This research work focused its interest on techniques of nonparamenic image extraction. Two different types of nonparametric image features were discussed . In this papar, non parametric image feature is defined in such a manner that non parametric image features are obtained based on not dimentional quan titi es such as length or area of drtaine part of a image but non dimenlional quantiti es such as brightness or contrast o r pattern. Firs t. Ihe textural analysis which can he recognized as unc of conventional nonparamctric image feature extractors was examined. Secondly. The finite element method was introduced as an infonnation processing tool for image feature extraction. The finite element application in image processing features a unique image data compression which is apparently similar to the function of human retina. 2. TEXTURAL FEATURES
The texturc-contex information is adequately specified by the matrix of relative frequencies P jj with which two neighboring resolution cells separated by distance d occur on the image , one with gray tone i and the other with gray lonc J as shown in Fig. I (Hardlick et al. , 1973). The joint pro bability density function is expressed by the notatio n P(d,a )(i ,j).
t
P (1,0) (i,j)
(I)
2
i=l j=l (b)Contrast:
n
n
E E (i-j) 2 P(l,O) (i,j) i=l
(2)
j=l
(c)Local Homogeneity:
n
n
E E (P(l,o/i,j) llr 1 + (i-j)2l
(3)
i=J j=J The perfonnance o r specification of the co-occurrence matrix can be sel by fixing d and values. The choice of d and e values detennines the sensitivity of the textural
a
features . 3. CHANGES IN TEXTURAL FEATURES DUE TO VARIATION OF LEAF WATER POTENTIAL
Figure 2 illustrates the experimental setup used for collecting the data indicating the relationships berween the leaf water potential and textural features. A video camera was set at a fixed position over the plant canopy (chrysanthemum). The video carnera was focused on the canopy. The pictorial data were collected under artificial lighling to provide a constant illumination condition. The destruclive technique was used to measure the leaf water potential that gives more accurate data than noodestructive one. At a time of the water potential
885
measurement, a few leaves were detached from the stem. From it, 3 disk-shape specimens were cut for the measurement. The plant had been grown under adequare water conditions before the measurement started. From the moment when the measurement started the water supply to the plant was suspended. Data were collected for five days until the plant reached its wilting point. The five sets of quantized gray tone s of the green component of the ROB data obtained from the domain consisting of 50 • 50 resolution ceUs were collected as digital images. The textural features were calculated from the gray-tone spatialdependence matrix for 16 gray levels constructed from the five sets of digital image data. Table I indicates the textural features calculated from the gray-tone spatia]dependence matrix. The variation of the leaf water potential is reflected on all of the textural parameters in order.
LAM:rI
....
4. FINITE ELEMENT FEATURES Figure 3 shows a schematic representation of the finite element image processing grid that converts pictorial image into non-parametric image features numerically. This non paramclric image featUI"C can he calculated based on the differences of brightness level between every input node of the finite element grid. Each of input nodes serves as a photosensitive rcccptor. In practice, for instance. signals transferred from sensing elements of CCD area array should be given to the input nodes. Nodal values of the output nodes become the finite element features. Other nodes are boundary nodes on which boundary conditions are specified.
Triangular Finite Element
VIDEO CAMERA Output Input N<>a"",~u
Node
Fig. 3. Finite element image processing grid. The algorithm to relate input and output signals of the
FlNDERVIEW
CHRYSANTHEMUM
mechanism of finite element image processing grid for generating image features is the conversion of incident light intensity distribution projected over the area comprising of
Fig. 2. Experimental set-up
TABLE I Change in textural features due to wata l.'!~ntj!!1
Average leaf leaf water potential (MP. )
-0.70 -0.95 -1.40 -2.15 -2.70
finite element image processing grid can be a linear mapping as described by a linear finite element equation, In this research work. 2-D Poisson's equation was utilized as a governing equation given by Eq.(4). The finite element equation used here is expressed as Eq.(5). The basic
variatiQn of ls;:aves.
finite element input nodes into a vector fonn of image features dislributed over the output nodes.
i'Jq,2
ISc - iJ2x
Thxtural features Contrast
Homogeneity
0.54 0.72 1.26 1.93 2.49
0.97 0.75 0.44 0.39 0.38
Local
Jq,2
+ K -Y
iJ2y
=Q
(4)
Homogeneity ~:information
0.52 0.41 0.30 0.30 0.27
conductivity in
)t
direction
'S-:information conductivity in y direction q, :potential Q :constant
886
A
2
[Kj-l
= C
Tomato Seedling
CCD
B
3 (5)
[Kl-l: inverse matrix of stiffness matrix lA) : input vector I I ) :oulpul veclor (image features) The performance or specification of the finite element infonnation processing grid can be set by fixing K values and the node arrangement The choice of K vaJues and the
node arrangements determines the sensitivity of the grid.
Fig.4 Experimental selup,
The K value is usually laken as the unity, The number of nodes is depending on allowable calculalion capacily, The arrangement of nodes is arbitrary. However some trial procedure is usually required to optirnize the grid performance.
30mio.
60mio,
90m;0.
5, CHANGES IN FINITE ELEMENT FEATURES
DUE TO VARIATION OF PLANT WATER CONTENT A tomato seedling (30 days after seedi ng) was used 10 obtain input data of a finite element im age processing grid. A single tomato seedling was placed in a growlh chamber 10 provide proper temperature, CO and light conditions as 2 illustrated by Fig, 4, Water supply la the seedling was stopped to observe change in appearance of wilting plant. The water loss from the plant was measured continuously by the electronic balance on which the plant with water pot was set. Movement of leaves and stems of the wilting plant due to the loss of turgidity of tissues was nx..-orded on a video taIX through a CCD color camera, An example of binary image of the wilting planl (30, 60 and 90 minutes after slopping water supply) is shown in Fig. 5. For extraction of finite element features, brighlness distribution in 256 gray scale over captured image frame of the wilting plant was fully used as input data. Figure 6 illustrates the procedure to prepare input data for the finite element image processing grid . The captured still image frame was divided into 12 x 12 segments. The image dimensions of each segment were 25 x 30 pixels. The average value of brightness (Y signal) of each segment was calculated. The calcu1ate average brightness values rmrle 144 input data which were fed inlo Ihe finite element image processing grid. Figure 7 shows the finite element image processing gird wilh 144 input nodes corresponding to the input data obtained from the· image frame. Six nodes were selected for OUlput nndes as shown in fig,7,
Fig,5, Change in appearance of wilting plant.
~'--
360 pixelsll2 sC!,'TTlents
' Averag(:d Brightness Fig, 6, Digital image frame consisting of 144
segments.
887
I...
~ r....
I •
. - NODENO.t
~
r-
t-
'"~
_ _ _ NOOENO..2
l-
.....-. NOOE NO.3
I'"
~"
ffi
_
NODENOA
~
NODENO.5
~
NODE N o .e
>
<
~OOOO 000029t_~ O.
1600
I·····~: !!'x::J~ :::!!.~ H' ~~~
~~4'~4·1i Q 0 .,
~£~~IID
.-.i,<. .
~
•8
0 ............
0 . . . . . . . . . . . .0 0 • • • • • • • • • • • •0
0000 0000000 0 0 0 Fig. 7. Each of segments of the image frame is coincident with the finite element input node in order. The variations of the finite element features with time after wilting stoned were plotted in Fig.S. The output nodes closer to the center of the output node array are the more sensiti ve to the change in plant appearance in the image frame. The relationship between water content of the plant and Ihe value of the finite element feature obtained at the node no. 6 is indicated in Fig.9 as an example. As shown in Fig.9. a strong non-linearity exists in the relationship between water content of the plant and Ihe value of lhe finite element feature. Figure 9 is not meant to interpret any obvious physical dependency of Iwo variables. Figure 9 indicates that the variation of pictorial image of plant reflecting the change in water content of the plant can be characterized by a pattern of a finite element feature . The manipulaled combi nation of all finite elemenl fealures oblained from an image frame of the plant should be able 10 identify the physiological status of plant.
6. COMPARISON BETWEEN TEXTURAL FEATURES AND FINITE ELEMENT FEATURES As indicated in Ihe previous sections, both methods gave fairl y good results in terms of describing the variation of digital image of plants. It is worth Irying 10 compare the two methods on the same input image.
H+H+H++-IH-i
1~0"~~at~~~~~ o
20
40 60 80 100 120 TIME - MINUTES
Fig. 8. Variation of the finite element features wilh time after wi ltin g started.
6. 1 Textural features Figure 10 s hows an image frame of a community of young seedlings of cabbage taken by a CCD colar camera. 11le 56 cabbage seedlings, of which rool parts had been cui at the lime when aboul 27 days of growth after seeding, were stuck into a stylofoam board at uprighl posilion in order 10 lel them dehydrate. The board with Ihe cabbage seedlings was placed on an electronic balance to monitor the weight change due to water loss from the p1ants. The change of appearance of the cabbage seedling community during the dehydration process was recorded in the form of digital image on a hard disk of image analyzer through a CCD color camera. Eleven image frames were sampled in the interval of about 35 minutes from the time when the foot parts were detouched. The eleven sets of quanti zed gray lanes (NTSC Y signal) of piclorial data obtained from .Ihe domain consisting of 100 x 100 resolution cells were collecled as digilal images. The lextural features were calculated from Ihe gray-lone spatial-dependence malrix for 16 gray levels constructed from each of .he eleven sets of digital image dala.
Figure 11 indicates three items of textural fea tures, i.e. I) Homogeneily, 2) Conlrasl and 3) Local Homogeneity varying wilh its level of dehydration of plants. The trends of contrast and homogeneity are either proponional or inverse proportional roughly to the change in plants weight.
6.2 Finite Element Features For this test, a 10 by 10 finite element image processing element was used. The image dimensions of each segment were IO x IO pixels. The average value of brightness (Y signal) of each segmenl was calculated. The calculate average brightness values made 100 input data which were fed into the finite element image processing grid. The variations of the finite element features wi th change in plants weighl afler Ihe dehydralion stoned were ploued in Fig.12.
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