International conference on Innovative Engineering Technologies (ICIET’2014) Dec. 28-29, 2014 Bangkok (Thailand)
ANN vs. FUZZY in Automated Irrigation
Er. Faruk Poyen, Adrish Pal, Biswanath Majumder, Dr. Apurba Ghosh, and Prof. Rajib Bandyopadhyay
The current irrigation devices need to determine actual
moisture content of the different region of the root crop zone
to determine which area/when need irrigation. The critical
period of crop, water requirement resulting in under
production of existing devices.
The advantage is cost of the device is very less.
Abstract---Irrigation happens to be the backbone of the civilized
society since time immemorial. With population increasing at an
exponential rate and land areas being curved short accommodating
this enormous population, several new and innovative practices are
coming up. We need to figure out new techniques such that we can
use our natural resources to the best of their abilities otherwise we
will be in peril. The subject of talk in this paper therefore relates to
the different techniques of using natural resources. Although many
innovative techniques have been employed towards Automated
Irrigation, they mostly indulge simple On - Off based controller.
FUZZY logic control can also give us also good amount of remedy
on resources, as it can operate based on IF-THEN logic. Then there
exists ANN control action which also comes good in utilizing
resources. Taking into consideration of the parameters which play
pivotal role in irrigation of a particular kind of crop or plantation, we
look forward to carrying out a comparative study to figure out which
controllers better while going for irrigational automation. The system
starts from taking signals from various sensors and ends up at giving
much better required output from the Final Control Elements.
II. CONTROL STRATEGIES
While discussing about control strategies, we do come
across two types of control action. One, the simple FUZZY
logic based Control and the other one rather the better and the
more demanding one, the ANN based control. What happens
in FUZZY Control is that it only works on some fixed Set
Point values. The On-Board sensors from the field feed the
controller with the data which gets compared in the Controller
section and depending upon the set point values, the output is
produced. The main drawback with this kind of control action
is that allows making definite decisions based on imprecise or
ambiguous data [1], [2]. Whereas ANN tries to incorporate
human thinking process to solve problems without
mathematically modeling them. The more preferable control
strategy is the ANN based Control action which has an
additional human like neural network controller to control the
FCEs likewise when the demand for the resource viz. water is
fulfilled. For proper and the most optimized irrigation
procedure and to yield the possible results, there are several
parameters to be considered, both static (fixed) and dynamic
(time dependent) parameters. Some of the fixed parameters at
any specific point of time are enumerated as follows:
Keywords----Artificial Neural Network, Fuzzy logic control,
Automated Irrigation, Evapotranspiration.
I. INTRODUCTION
F
OOD supply occupies the apex position in the
requirement pyramid of life and in order to meet this
quintessential demand for the mass, humans discovered the
art of agriculture and ameliorated it over the age. Studies
show that improper irrigation techniques lead to waste of
natural resources and also lead to inferior productivity of the
crops.
The existing alternative irrigation systems are in the stage of
selecting irrigation area manually via human operation to
achieve a selected area of irrigation, time consuming,
response speed is slow.
 Type of soil (texture)
 Status or stage of growth
 Salinity of the soil (determining the sweating of the
soil)
 Leaf coverage (transpiration and evaporation
determining factor)
 Water requirement
Based on the above set of parameter, few input parameters
to be considered are
 Soil humidity level
 Ambient temperature
 Breeze speed
 Radiation
In order to design control logic, all the above mentioned
parameters have to be considered and hence the output
parameters can be set which are
Er Faruk Bin Poyen is an Asst. Professor with University Institute of
Technology, Burdwan University, Burdwan, India (08697511868 ; e-mail:
[email protected] ).
Adrish Pal is a B.E student with University Institute of Technology,
Burdwan University, Burdwan, India (e-mail: [email protected]
).
Biswanath Majumder is a B.E student with University Institute of
Technology,
Burdwan
University,
Burdwan,
India
(e-mail:
[email protected]).
Dr. Apurba Ghosh is currently Associate Professor and Teacher In-Charge
with University Institute of Technology, Burdwan University, India
([email protected])
Prof. Rajib Bandhppadhyay2 is currently Professor in the dept. of
Instrumentation and Electronics with the Jadavpur University, India
([email protected])
http://dx.doi.org/10.15242/IIE.E1214050
 Opening / Closing of the valves and / or adjusting
their amounts in combination.
 Switching on / off the energy systems (airing, lighting
and heat exchanges)
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International conference on Innovative Engineering Technologies (ICIET’2014) Dec. 28-29, 2014 Bangkok (Thailand)
 Opening / Closing of the roof in case of Greenhouse
agriculture.
modeled or patterns are found. Artificial Neural Networks are
mathematical (not actual) models of the human neural
network. They are trained using large data sets and they learn
using those data. Once the learning process is complete, they
can be used on new data to find the desired results.
III. EXCOGITATION OF FUZZY CONTROLLED IRRIGATION
SYSTEM
Before we start off with this design techniques employing
FUZZY, we need to know what is FUZZY and why should
we involve such a technique in our work.
Fuzzy Logic belongs to the family of many-valued logic. It
focuses on fixed and approximate reasoning opposed to fixed
and exact reasoning. A variable in fuzzy logic can take a truth
value range between 0 and 1, as opposed to taking true or
false in traditional binary sets. Fuzzy logic provides a method
to make definite decisions based on imprecise and ambiguous
input data. Fuzzy logic is widely used for applications in
control systems, since it closely resembles how a human make
decision but in faster way. Fuzzy logic can be incorporated in
to control systems based on small handheld devices to large
PC workstations. Fuzzy logic is a rule based system that
uses IF-THEN rules.
Fig. 2 System block diagram using ANN controller
There are four stages connected together to fulfill the
requirement.
• Sensor Input: temperature, air humidity, soil moisture, wind
speed and radiation are collected.
• Evapotranspiration Model: This block converts four input
parameters into actual soil moisture.
There are five stages connected together to fulfill the
requirement.
• Required Soil Moisture:
• Sensor Input: temperature, air humidity, soil moisture, wind
speed and radiation are collected.
• ANN Controller: compares the required soil moisture with
actual soil moisture and decision is made.
• Evapotranspiration Model: This block converts four input
parameters into actual soil moisture.
What Are Their Advantages Over Conventional Techniques?
Depending on the nature of the application and the strength
of the internal data patterns you can generally expect a
network to train quite well. This applies to problems where
the relationships may be quite dynamic or non-linear. ANNs
provide an analytical alternative to conventional techniques
which are often limited by strict assumptions of normality,
linearity, variable independence etc. Because an ANN can
capture many kinds of relationships it allows the user to
quickly and relatively easily model phenomena which
otherwise may have been very difficult or impossible to
explain otherwise.
• Required Soil Moisture:
• FUZZY logic Controller: compares the required soil
moisture range with actual soil moisture and decision is
made.
• Sowing period
Fig. 1 System block diagram using FUZZY controller
This Figure exhibits the block diagram of Complete Irrigation
System embedded with ANN Controller.
IV. EXCOGITATION OF ANN CONTROLLED IRRIGATION
SYSTEM
A. Modeling of System Parameters:
Before we start off with any design techniques employing
ANN, we need to know what is ANN and why should we
involve such a technique in our work. In simple terms it may
be defined as “an artificial neuron network (ANN) is a
computational model based on the structure and functions of
biological neural networks. Information that flows through the
network affects the structure of the ANN because a neural
network changes - or learns, in a sense - based on that input
and
output.
It
processes
information
using
a connectionist approach to computation [7]. ANNs are
considered nonlinear statistical data modeling tools where the
complex relationships between inputs and outputs are
http://dx.doi.org/10.15242/IIE.E1214050
Irrigation system having several input parameters, but some
parameters are fixed in the process and some parameters
(physical parameters) are varied based on time and
production. The fixed parameters are type of soil, plants and
growth [4], [5], [6]. The physical parameters are temperature,
wind speed, humidity and radiation.
Inputs Parameters: There are four factors (Temperature, air
humidity,
wind
speed
and
radiation)
bywhich
evapotranspiration is influenced.
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International conference on Innovative Engineering Technologies (ICIET’2014) Dec. 28-29, 2014 Bangkok (Thailand)
A.1 Temperature:
(1)
• A sine wave with amplitude of 24 ºC;
• A frequency of 2pi/T=2pi/24. 24 hour time period.
(2)
• A constant bias (offset) of 30 ºC;
(3)
A.2 Air humidity: It is modeled as:
• A sine wave with amplitude of 94%;
(4)
• Bias of 100% (constant);
• A frequency of 2pi/T=2pi/24. 24 hour time period.
ET0 = Reference evapotranspiration [mm day-1],
A.3 Wind speed:
Rn= Net radiation at the crop surface [MJ m-2 day1],
• A sine wave with amplitude of 14 Km/h;
G = Soil heat flux density [MJ m-2 day-1],
• Bias of 35 Km/h (constant);
T = Mean daily air temperature at 2 m height [°C],
• A frequency of 2pi/T=2pi/24. 24 hour time period.
U2= Wind speed at 2 m height [m s 1],
A.4 Radiation: It is modeled as maximum possibleradiation at
earth’s surface (Rmax).
es= Saturation vapor pressure[kPa],
• A sine wave with amplitude of 4MJ/m2
es-ea= e0(T) =Saturation vapor pressure deficit [kPa],
• Bias of 12MJ/m;
D = Slope vapor pressure curve [kPa °C-1],
• A frequency of 2pi/T=2pi/24. 24 hour time period.
g = Psychometric constant [kPa °C 1].
ea= Actual vapor pressure [kPa],
B. Soil Moisture:
P = Atmospheric pressure [kPa],
It depends on plantation, type of growth, type of land and
type of soil. The required soil moisture is calculated according
tothe above mentioned factors. An assumed graph is shown in
figure3.
z = Elevation above sea level [m],
e0(T) = Saturation vapour pressure at the air temperature
T[kPa],
λ = Latent heat of vaporization, 2.45 [MJ kg-1],
Cp = Specific heat at constant pressure, 1.013 10-3 [MJkg-1
°C-1],
ε = Ratio molecular weight of water vapour/dry air =0.622
D. Control Unit:
The control unit is an Artificial Neural Network based
controller. Required moisture level and measured moisture
level are interfaced with this controller [12], [13]. The main
function of this stage is to keep the actual soil moisture close
to the required soil moisture. The output of this stage is
controlled input for valve to supervise what amount of water
to supply to optimize the situation. The Simulink diagram of
ANN based control system is given in figure 5.
The controller has two inputs i.e. required soil moisture and
calculated soil moisture from evapotranspiration model and
one output, control input for Valve position.
Fig. 3 Input Parameters: Graphical representation
Fig. 4 Required Soil Moisture-graphical representation
ANN Controller Architecture:
ANN Controller is implemented using the following:
•Topology: Distributed Time Delay Neural Network is
used;
•Training Function: Bayesian Regulation function is used
for training.
•Performance: Sum squared error is taken as performance
measure.
•Goal: The set goal is 0.0001.
•Learning Rate: The learning rate is set to0.05.
C. Evapotranspiration Model:
Penman-Monteith equation is an equation accepted as a
scientifically sound formulation for estimation of reference
evapotranspiration (Eto) [10], [11],[16]. It is a combined
function of radiation, temperature, humidity and wind speed.
Updated by FAO in May 1990, the Penman Monteith
equation is written as the following:
http://dx.doi.org/10.15242/IIE.E1214050
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International conference on Innovative Engineering Technologies (ICIET’2014) Dec. 28-29, 2014 Bangkok (Thailand)
B. ANN Controller
The block diagram of FUZZY controller is shown in figure 6.
Valve opens when required soil moisture exceeds the
measured soil moisture and otherwise.
The legends of figure 5 are:
• Violet signal-Required Soil moisture
• Yellow signal-Actual Soil moisture
• Blue -Valve output.
The actual soil moisture tracks the required soil moisture
without any oscillations.
IV. SIMULATION RESULTS
Once the neural network is trained, it can be used as direct
controller in cascade with the Evapotranspiration model. The
control target is to bring the actual soil moisture as close as
possible to required soil moisture and to optimize the
resources like water and energy.
Keeping the aforementioned requirement in mind behavior
of ANN controller is noted for reference (Required) Soil
moisture [8], [9], [10]. The Response of ANN controller is
compared with FUZZY controller implemented with the same
evapotranspiration model. This is shown in figure 11-12.The
important facts that can be extracted from the simulations are:
1. The error (difference between required and actual soil
moisture) is steady and reasonable (less than2%)
2. In ANN controller the ON/OFF of the valve and energy
system is very low and hence lot of energy and water can be
saved.
The main goal of designing the cost-effective and result
oriented Irrigation Control System has been achieved by using
ANN Controller.
A. FUZZY Controller
V. CONCLUSIONS AND FUTURE WORK
The legends of figure 6 are:
This paper holds a comparative study between FUZZY
control and its ANN control counterpart. The efficiency and
response of ANN are much better. The best part of it is that
the ANN system offers better efficiency than FUZZY system.
ANN system changes its decision and hence the output
according to ecological condition. By implying ANN we are
getting more accurate result while calculating required soil
moisture from the input parameter. Following results do show
why we have come to much a conclusion. The response does
vary on choice on other training tools and under all
circumstances Fuzzy comes second to ANN control.
• Violet signal – Required Soil moisture
• Blue Signal- Actual soil moisture.
• Yellow signal – valve output.
1. In FUZZY control based system, the actual soil moisture
tracks the required soil moisture but there are some range of
actual soil moisture, which differentiates from required soil
moisture.
2. There is no oscillation at the output; it shows that the
FUZZY control based system is stable.
In FUZZY controller the valve is opened and closed
continuously at a range (between max and min).Due to this,
lot of energy and water are consumed which is undesirable.
Fig. 5 ANN based Control System with Evapotranspiration model
http://dx.doi.org/10.15242/IIE.E1214050
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International conference on Innovative Engineering Technologies (ICIET’2014) Dec. 28-29, 2014 Bangkok (Thailand)
Fig. 6 FUZZY based Control System with Evapotranspiration model
Fig. 7 ETo output
Fig. 8 Comparator output in FUZZY logic
Fig. 9 Neural Network Training
Fig. 10 Neural network Trainer output
Fig. 11 Simulation Results of FUZZY control based System
http://dx.doi.org/10.15242/IIE.E1214050
Fig. 12 Simulation Results of ANN based control System
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International conference on Innovative Engineering Technologies (ICIET’2014) Dec. 28-29, 2014 Bangkok (Thailand)
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