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Neural Network Model for Distributed Temperature Control
3rd IFAC/CIGR Workshop on Control Applications in post-Harvest and Processing Technology, october 3-5, 2001, Tokyo, Japan
NEURAL NETWORK MODEL FOR DISTRIBUTED TEMPERATURE CONTROL
Haruhiko MURASE, Noriko TAKAHASHI, Katsusuke MURAKAMI Tateshi FUJIURA, Yoshifumi NISHIURA
Graduate School ofBiological and Agricultural Sciences, Osaka Prefecture University, 1-I,Gakuen, Sakai, Os aka, 599-8531,JAPAN
Abstract : As precision agriculture in Japan, Microprecision Agriculture program is defined as an ultimate optimized plant production system practiced for plant factories to be compatible environmental safeguards with yield. In fact at the plant factory, some environmental non-uniformity within the interior always
exists.
However the
environmental
non-uniformity
is
still
left
uncontrolled. In this paper, local temperature controls as a microprecision technology for plant factory will be discussed.
Keywords: microprecision agriculture, plant factory, distributed control, optimal management, Neural network Model,
generated. At the first, as the systematic research
I. INTRODUCTION
movement,
the
USA
(Low-Input
Sustainable
Ten years ago, since it was gening seriously to
Agriculture) program was started. This is one of the
waste the energy and disrupt the environment, people
operational definitions of sustainable agriculture.
were interested in agriculture of environmental
However, this project makes linle progress because
safeguards at USA. The current that aims for
USA always improves traditional agriculture at the
innovation of the food production technology was
expense of the yield by reducing or abolishing to
- 179-
input chemical materials. On the other hand, the
Microprecision technique for the plant factory
implementation of Precision Agriculture (PA) has
environmental control should be able to provide
been embraced since 1996.
objective
All
over
the
countries
are
doing
research,
controlled
distribution.
environment
including
In this paper, local temperature controls
development and spread PA as innovative agricultural
as a microprecision technology for plant factory will
technology of environmental safeguards that is based
be discussed.
on
utilizing
information,
sensor
and
software
technologies.
2.MATERIAL AND METHOD
In fact PA is the optimal management system corresponding to "uneven" at the large scale open
Figure I illustrates the experimental apparatus.
field in USA. This concept must be contributed to
Two sirocco fans create two major air streams.
environmental and energy issues or safety of food
Each sirocco fan brows air in a predetermined
that are difficult problems in Japan.
direction adjusted by fins.
The air temperature
It is need to construct Japanese PA because field
brown from each fan can be adjusted independently
condition in Japan is different from USA. Although
at a specific temperature that can be different from
the plant factory is also a large scale complex system,
the temperature of air blown by the other fan.
it is much less complex than the open field system.
flow rate of the air from each fan can be also varied
The fully controlled environment of a plant factory
independently.
can be considered as an ideal cultivation system in
the chamber appear depending up on the flow rates,
terms of alternative agriculture.
temperatures and directions of air brown by two fans.
Most of the
The
Various temperature distributions in
environmental factors in a fully controlled plant factory are observable and controllable; a plant factory can be optimized more easily than an open field.
Precision agriculture can be implemented in
the alternative cultivation system namely plant factories to realize profitable agriculture.
The
terminology Microprecision Agriculture is defined as a form of precision agriculture practiced for plant factories to be compatible environmental safeguards with yield. The conventional strategy of environmental control for the interior of plant factories is to bring atmospheric factors such as temperature and humidity as uniform as possible in the entire interior space. In the reality, some environmental non-uniformity within the interior always exists.
Figure I. Experimental Apparatus
Even some
greenhouse techniques often take advantage of such a
Temperature distribution can be measured at nine
distribution for adjusting harvesting time. However
locations by thermocouples in the chamber as shown
the
environmental
non-uniformity
IS
still
left
in Figure 2.
uncontrolled.
- 180-
3.NEURAL NETWORK MODEL Air stream
The temperature distribution can be assumed as a
Heat sink
function of air temperatures and directions of the air
Xo
blown by the two fans. Thus, these relationships can
Fan
be modeled by using a neural network as shown in Figure 3. The input units are two wind directions and
Hea source I
temperatures and output units are temperatures at the nine locations. The hidden layer consists of 9 units. To input the airflow of temperatures and directions, we can obtain temperature distributions of nine locations. Heat source 2
8, ,82
:
Wind direction.
~,V2
: Air flow rate
T;, ~ : Temperature of wind Figure 2.
Output
Input
(Xo,to) (Xht l )
~
Location of nine thermocouples for
(X 2,t 2)
temperature distribution measurement.
()2
In this paper, the airflow rates were fixed at 1.3m/s
(X 3,t3 ) (X 4 ,t4 )
1;
(Xs,t s)
and 1.15m/s. Two light bulbs (40W and 25W) were the heat
1;
(X 6 ,t6 )
sources. Air temperature changed according to the
(X 7,t7)
amount of available heat (from the light bulbs). The
(Xg,tg)
distribution of air temperature was calculated using eh e2 : Wind direction,
the direction and temperature of the airflows. Table I indicates an example of the measured
T 1,T2: Temperature of wind
Xn : Location, tn : Temperature at the location (n=0,1, .....8)
temperature result at nine locations.
Figure 3, Neural network model
Table I. Example of the measured temperature at nine locations
At first, two measured temperature data were took up as check data. Location Temperature Cc)
Xo 20.69
XI 2 I. I8
X2 21.02
X3 20.84
Neural network model was learned 7 training data of
measured
temperature.
The
learning
was
terminated when the error converged less than 1%. X4 20.97
Xs 20.74
X6 20.88
X7 20.91
Xg 20.88
Then, applying the synaptic weights, the temperature distribution was estimated by inputting the check data
- 181-
as shown in Table 2.1 and Table 2.2.
other environmental parameters in plant factory environment in terms of realization of microprecision
Table 2.1 Inspection data and estimated temperature
agriculture. In a traditional plant factory, crops at
by using a neural network model
different growth stages are grown under the same temperature condition in the same growing space. To
Inspection data Cc) Estimate Cc) Absolute error CC)
Xo
XI
X2
X3
use this neural network model and estimate the
24.57
24.28
24.21
24.43
temperature distribution, we can locate crops at the
24.27
24.45
24.11
24.15
optimum growing space corresponding to growth
0.3
0.17
0.1
0.28
stages. By providing optimum temperature for a group of crops at the same growth stage even in the same growth space, better outcome in terms of
X4
X5
X6
X7
Xg
24.26 24.03 0.23
23.92 23.92 0
24.16 23.97 0.19
23.70 24.1 0.4
23.18 23.89 0.71
quality, yield, harvesting labor and shipment can be expected. The local environmental control technology is applicable to not only plant production systems but also human living environment. For example, it can
Table 2.2 Inspection data and estimated temperature by using a neural network model
Inspection data Cc) Estimate Cc) Absolute error Cc)
make an amenity space that human and plants live together in a certain space. There has been more demand for introducing living plants into office
Xo
XI
X2
X3
32.13
32.37
32.28
32.29
32.54
32.55
32.51
32.46
optimum (or comfortable) temperature and humidity
0.41
0.18
0.23
0.17
for plant and human are different from each other.
workspace, ordinary living room, indoor garden, indoor sport facility, atrium, etc. It is obvious that
The local environmental control technology could
X4
X5
X6
X7
Xg
32.22 32.42 0.2
32.0 32.33 0.33
32.13 32.38 0.25
31.96 32.28 0.32
31.86 32.22 0.36
satisfy both plant and human with comfortable living conditions.
REFERENCES Both of the average errors were 0.27. By
using
these
synaptic
weights,
various
Hiroshi Ichikawa (1993). Hierarchy neural network (in Japanese) ,ppl-33. Tokyo: Kyoritsu Shuppan
temperature distributions that weren't measured in
Calculation & Algorithm research (1992). Neuro
this study can estimate.
Computing using C language (in Japanese), ppI4-79, 133-148. Tokyo: Rassel Books.
4. DISCUSSION AND CONCLUSION
Neuronet group and Shigeru Kiritani (1989). Neuro
Computer (in Japanese), pp 15, 28-40.Tokyo:
Experimental result has shown it is possible to
Gijyutsu-Hyoron.
estimate temperature distributions by manipulating wind directions and temperatures of the airflow.
Tomoharu Nagao (2000). Optimized Algorithm
Local environmental control technology is essential for providing optimal distributions of temperature or
- 182-
(in Japanese) ,pp. I04-115. Tokyo: Shokodo.
NEURAL NETWORK MODEL FOR DISTRIBUTED TEMPERATURE CONTROL
Haruhiko MURASE, Noriko TAKAHASHI, Katsusuke MURAKAMI Tateshi FUJIURA, Yoshifumi NISHIURA
Graduate School ofBiological and Agricultural Sciences, Osaka Prefecture University, 1-I,Gakuen, Sakai, Os aka, 599-8531,JAPAN
Abstract : As precision agriculture in Japan, Microprecision Agriculture program is defined as an ultimate optimized plant production system practiced for plant factories to be compatible environmental safeguards with yield. In fact at the plant factory, some environmental non-uniformity within the interior always
exists.
However the
environmental
non-uniformity
is
still
left
uncontrolled. In this paper, local temperature controls as a microprecision technology for plant factory will be discussed.
Keywords: microprecision agriculture, plant factory, distributed control, optimal management, Neural network Model,
generated. At the first, as the systematic research
I. INTRODUCTION
movement,
the
USA
(Low-Input
Sustainable
Ten years ago, since it was gening seriously to
Agriculture) program was started. This is one of the
waste the energy and disrupt the environment, people
operational definitions of sustainable agriculture.
were interested in agriculture of environmental
However, this project makes linle progress because
safeguards at USA. The current that aims for
USA always improves traditional agriculture at the
innovation of the food production technology was
expense of the yield by reducing or abolishing to
- 179-
input chemical materials. On the other hand, the
Microprecision technique for the plant factory
implementation of Precision Agriculture (PA) has
environmental control should be able to provide
been embraced since 1996.
objective
All
over
the
countries
are
doing
research,
controlled
distribution.
environment
including
In this paper, local temperature controls
development and spread PA as innovative agricultural
as a microprecision technology for plant factory will
technology of environmental safeguards that is based
be discussed.
on
utilizing
information,
sensor
and
software
technologies.
2.MATERIAL AND METHOD
In fact PA is the optimal management system corresponding to "uneven" at the large scale open
Figure I illustrates the experimental apparatus.
field in USA. This concept must be contributed to
Two sirocco fans create two major air streams.
environmental and energy issues or safety of food
Each sirocco fan brows air in a predetermined
that are difficult problems in Japan.
direction adjusted by fins.
The air temperature
It is need to construct Japanese PA because field
brown from each fan can be adjusted independently
condition in Japan is different from USA. Although
at a specific temperature that can be different from
the plant factory is also a large scale complex system,
the temperature of air blown by the other fan.
it is much less complex than the open field system.
flow rate of the air from each fan can be also varied
The fully controlled environment of a plant factory
independently.
can be considered as an ideal cultivation system in
the chamber appear depending up on the flow rates,
terms of alternative agriculture.
temperatures and directions of air brown by two fans.
Most of the
The
Various temperature distributions in
environmental factors in a fully controlled plant factory are observable and controllable; a plant factory can be optimized more easily than an open field.
Precision agriculture can be implemented in
the alternative cultivation system namely plant factories to realize profitable agriculture.
The
terminology Microprecision Agriculture is defined as a form of precision agriculture practiced for plant factories to be compatible environmental safeguards with yield. The conventional strategy of environmental control for the interior of plant factories is to bring atmospheric factors such as temperature and humidity as uniform as possible in the entire interior space. In the reality, some environmental non-uniformity within the interior always exists.
Figure I. Experimental Apparatus
Even some
greenhouse techniques often take advantage of such a
Temperature distribution can be measured at nine
distribution for adjusting harvesting time. However
locations by thermocouples in the chamber as shown
the
environmental
non-uniformity
IS
still
left
in Figure 2.
uncontrolled.
- 180-
3.NEURAL NETWORK MODEL Air stream
The temperature distribution can be assumed as a
Heat sink
function of air temperatures and directions of the air
Xo
blown by the two fans. Thus, these relationships can
Fan
be modeled by using a neural network as shown in Figure 3. The input units are two wind directions and
Hea source I
temperatures and output units are temperatures at the nine locations. The hidden layer consists of 9 units. To input the airflow of temperatures and directions, we can obtain temperature distributions of nine locations. Heat source 2
8, ,82
:
Wind direction.
~,V2
: Air flow rate
T;, ~ : Temperature of wind Figure 2.
Output
Input
(Xo,to) (Xht l )
~
Location of nine thermocouples for
(X 2,t 2)
temperature distribution measurement.
()2
In this paper, the airflow rates were fixed at 1.3m/s
(X 3,t3 ) (X 4 ,t4 )
1;
(Xs,t s)
and 1.15m/s. Two light bulbs (40W and 25W) were the heat
1;
(X 6 ,t6 )
sources. Air temperature changed according to the
(X 7,t7)
amount of available heat (from the light bulbs). The
(Xg,tg)
distribution of air temperature was calculated using eh e2 : Wind direction,
the direction and temperature of the airflows. Table I indicates an example of the measured
T 1,T2: Temperature of wind
Xn : Location, tn : Temperature at the location (n=0,1, .....8)
temperature result at nine locations.
Figure 3, Neural network model
Table I. Example of the measured temperature at nine locations
At first, two measured temperature data were took up as check data. Location Temperature Cc)
Xo 20.69
XI 2 I. I8
X2 21.02
X3 20.84
Neural network model was learned 7 training data of
measured
temperature.
The
learning
was
terminated when the error converged less than 1%. X4 20.97
Xs 20.74
X6 20.88
X7 20.91
Xg 20.88
Then, applying the synaptic weights, the temperature distribution was estimated by inputting the check data
- 181-
as shown in Table 2.1 and Table 2.2.
other environmental parameters in plant factory environment in terms of realization of microprecision
Table 2.1 Inspection data and estimated temperature
agriculture. In a traditional plant factory, crops at
by using a neural network model
different growth stages are grown under the same temperature condition in the same growing space. To
Inspection data Cc) Estimate Cc) Absolute error CC)
Xo
XI
X2
X3
use this neural network model and estimate the
24.57
24.28
24.21
24.43
temperature distribution, we can locate crops at the
24.27
24.45
24.11
24.15
optimum growing space corresponding to growth
0.3
0.17
0.1
0.28
stages. By providing optimum temperature for a group of crops at the same growth stage even in the same growth space, better outcome in terms of
X4
X5
X6
X7
Xg
24.26 24.03 0.23
23.92 23.92 0
24.16 23.97 0.19
23.70 24.1 0.4
23.18 23.89 0.71
quality, yield, harvesting labor and shipment can be expected. The local environmental control technology is applicable to not only plant production systems but also human living environment. For example, it can
Table 2.2 Inspection data and estimated temperature by using a neural network model
Inspection data Cc) Estimate Cc) Absolute error Cc)
make an amenity space that human and plants live together in a certain space. There has been more demand for introducing living plants into office
Xo
XI
X2
X3
32.13
32.37
32.28
32.29
32.54
32.55
32.51
32.46
optimum (or comfortable) temperature and humidity
0.41
0.18
0.23
0.17
for plant and human are different from each other.
workspace, ordinary living room, indoor garden, indoor sport facility, atrium, etc. It is obvious that
The local environmental control technology could
X4
X5
X6
X7
Xg
32.22 32.42 0.2
32.0 32.33 0.33
32.13 32.38 0.25
31.96 32.28 0.32
31.86 32.22 0.36
satisfy both plant and human with comfortable living conditions.
REFERENCES Both of the average errors were 0.27. By
using
these
synaptic
weights,
various
Hiroshi Ichikawa (1993). Hierarchy neural network (in Japanese) ,ppl-33. Tokyo: Kyoritsu Shuppan
temperature distributions that weren't measured in
Calculation & Algorithm research (1992). Neuro
this study can estimate.
Computing using C language (in Japanese), ppI4-79, 133-148. Tokyo: Rassel Books.
4. DISCUSSION AND CONCLUSION
Neuronet group and Shigeru Kiritani (1989). Neuro
Computer (in Japanese), pp 15, 28-40.Tokyo:
Experimental result has shown it is possible to
Gijyutsu-Hyoron.
estimate temperature distributions by manipulating wind directions and temperatures of the airflow.
Tomoharu Nagao (2000). Optimized Algorithm
Local environmental control technology is essential for providing optimal distributions of temperature or
- 182-
(in Japanese) ,pp. I04-115. Tokyo: Shokodo.