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7/24/2019 Modern Irrigation Systems Using Fuzzy logic Technique
1/23
1. ABSTRACT
This paper proposes a new irrigation system using fuzzy logic technique by
mapping the knowledge and experience of a traditional farmer. Fuzzy logic control,
which is similar to the human way of thinking, has emerged as the most active tool in
automatic control. The purpose of fuzzy logic controller is to automatically achieve and
maintain some desired state of a system and process by monitoring system variables as
well as taking appropriate control action.
The aim of this work is to develop an intelligent control using fuzzy logic
approach for irrigation of agricultural field, which simulates or emulates the human
beings intelligence. The status of any agricultural field, in terms of evapotranspiration
and error may be assumed as input parameters and the decision is made to determine the
amount of water required for the area to be irrigated, well in advance. This leads to use
effective utilization of various resources like water and electricity and hence becomes a
cost effective system for the expected yield.
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2. INTRODUCTION
From the past, agriculture has been playing an important role in human societies
to suffice the growing and dynamic demands. rrigation is an essential component of crop
production in many areas of the world. !recision "griculture#!"$ is an integrated system
designed to increase long%term, field%specific, and farm production efficiencies,
productivity, and profitability in the field of agriculture. The !" is very essential for the
countries like ndia whose agriculture completely depends upon the rains and climatic
conditions. !recision farming ensures quicker response times, better quality control for
the yield with a minimum labor effort. There is a requirement for use of sensing
technologies in the field of !" to monitor the crop parameters and control the utilization
of resources towards the societal benefits
n the past few years, there has been an increasing interest in the application of the
fuzzy set theory to many control problems. For many complex control systems, the
construction of an ordinary model is difficult due to nonlinear and time varying nature of
the system. Fuzzy &ontrol has been applied in traditional control systems, which yields
promising results, t is applied for the processes, which yields promising results, it is
applied for the processes, which are too complex to be analyzed by conventional
techniques or where the available information is uncertain. n fact, fuzzy logic controller#F'&$ is easier to prototype, simple to describe and verify, can be maintained and also
extended with grater accuracy in less time. These advantages make fuzzy logic
technology to be used for irrigation system also.
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3. LITERATURE REVIEW
The most important finding in the literature is the unanimity on the need to have a
site specific focus to irrigation schemes and to ensure that the community is brought into
process from the start, with their priorities, in order to equip them and their elected
committee to manage the scheme once the department, or agency or donor withdraws
form the process. n word there must be revitalization which (implies a move away from
pure infrastructure rehabilitation to a comprehensive programme to structure, train and
capacitate the smallholder farmers to run their scheme profitably and sustainably) #de
'ange, *++$. The literature supports rehabilitation in the strongest terms warning of
failure if capacity building of the community is left out- (The experience from the review
is explicitly clear that infrastructure development alone or as a dominant part of the
intervention is destined to failure. Farmers in smallholder schemes need support systems
that go far beyond ust the irrigation system if they are to improve their livelihoods
significantly. rrigation is a highly complex mix of social, agriculture, market and
technical parameters, which are in a state of on%going flux and interconnectedness.
rrigation planners and advisors must, as a critical priority, embrace the multiple sectoral
interests and dynamics in planning thinking. /arrow isolated, engineering and
infrastructure driven programs are destined to fail in their obectives.
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The growth processes of the crops are often effected by a lot of environmental
factors, including water deficit, temperature anomaly, disease insect damage and
disadvantageous soil condition etc..0ffect in water deficit among them was most serious,
and exceeded the sum of the other environment affects."t the same time, water resource
saving status have already been the important index appraising a country or regional
economies sustained development. 1tudy on water resource saving has been paid
attention to by the home and abroad scholars. t was shown in reference that agriculture
irrigation using water occupied 2+3 of the whole world fresh water, and or so +3 was
wasted owing to evaporation, deep sorption of soil etc..Therefore, precision irrigation
must be vigorously developed and promoted. !reliminary research results on fuzzy
control model of precision irrigation based on water stress monitoring for the corps were
designed in the paper. Five sensors were introduced and respectively monitored "0, the
temperature, humidity, illumination and the &4* density. 1elf%learning fuzzy model on
precision irrigation was layouted. !resent given volume on water was by five inputs. t
was shown that five inputs and signal output of double fuzzy control model on precision
irrigation system could effectively fulfill the tasks of normal irrigation and precision
irrigation, timely, suitably and scientifically irrigate under water required information for
the corps growth, so as to save water and expand productivity.
To a lesser extent, fuzzy logic applied to control is another discipline we explored.
First introduced by 5adeh in the early 678+s, this discipline has been widely used for
different applications. 4ur work extended the load%matching training procedure designed
for neural%network controllers to fuzzy%logic control. Therefore, the concept of
backpropgation is used here as well. 9ang produced an important contribution related to
self%adapting, fuzzy%logic control systems. :e developed the concept of adaptive
network%based fuzzy inference system, also know as "/F1. Fuzzy%logic system
identification was part of his approach. The fuzzy%logic defuzzification used by "/F1 is
based on a zero%order 1ugeno fuzzy model #or F1, Fuzzy nference 1ystem$ . "long with
"/F1, 9ang introduced the concept of universal approximator and using the 1tone%
;eierstrass Theorem he proved that when the number of rules is not restricted, a zero%
order 1ugeno model can match any arbitrary nonlinear function. :e also related the
1ugeno model with the Tsukamoto model. "n important issue that relates the neural%
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network world with fuzzy%logic models is the connection between F1s and
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used to start irrigations. Termination of the irrigation can be based on a pre%set time or
may be based on a specified volume of water passing through a flow meter. 4pen loop
control systems are typically low in cost and readily available from a variety of vendors.
They vary in design and complexity and often offer flexibility as to the number of zones
and how irrigations are scheduled. The drawback of open loop systems is their inability to
respond automatically to changing conditions in the environment. n addition, they may
require frequent resetting to achieve high levels of irrigation efficiency.
II. CLOSED LOOP SYSTEM
n closed loop systems, the operator develops a general control strategy. 4nce the
general strategy is defined, the control system takes over and makes detailed decisions of
when to apply water and how much water to apply. This type of system requires feedback
from one or more sensors. rrigation decisions are made and actions are carried out based
on data from sensors. n this type of system, the feedback and control of the system are
done continuously. &losed loop controllers require data acquisition of environmental
parameters #such as soil moisture, temperature, radiation, wind%speed, etc$ as well as
system parameters #pressure, flow, etc.$. The state of the system is compared against a
specific desired state, and a decision whether or not to initiate an action based on this
comparison. &losed loop controllers typically base their irrigation decisions on the
sensors that measure soil moisture, temperature, humidity and evaporation and other
climatic data to estimate water requirement of a crop .
4.2 IMPLEMENTATION OF SYSTEM HARDWARE
This section presents proposed Fuzzy based rrigation &ontrol 1ystem
architecture using ;1/ for monitoring and controlling the irrigation in an agriculture
which is as shown in Figure 6. t consists of four basic components namely B"C ;ireless
1ensor /etwork B=C >ateway /ode D 1ink /ode B&C Fuzzy based rrigation &ontroller
BAC rrigation !ipe /etwork. The first component consists of ;ireless 1ensor /etwork
which sense physical and environmental parameters and send data to the gateway node.
1econd component is application server which receives data from gateway and processes
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it. The last component is irrigation pipe network which is laid over the irrigated areas and
the electric control valves are installed on pipelines.
4.3 WIRELESS SENSOR NETWORK
The proposed system implemented using the @0@1& e?o !ro 1eries which is a
wireless agricultural and environmental sensing system for crop monitoring. The system
also provides an easy deployment of wireless monitoring system in an agricultural layout
for efficient collection of data about its needs from multiple locations.
The e?o /ode is a fully integrated, rugged outdoor sensor package that uses an
energy%efficient radio and sensors for extended battery%life and performance. The e?o
/ode integrates @0@1&s
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Figu!1. Fu""# B$%!& Iig$'i() C()'(**! S#%'!+ A,-i'!,'u!
4.4 NEED FOR MODERN IRRIGATION SYSTEM
;ater and electricity should be optimally utilized in an agricultural like ndia. The
development in the filed of science and technology should be appropriately used in the
field of agriculture for better yields. rrigation has traditionally resulted in excessive
labour and non%uniformity in water application across the filed. :ence, an automatic
irrigation system is required to reduce the labour cost and to give uniformity in water
application across the field.
4. PHYSIOLOGICAL PROCESSING
n the irrigation system, plant take%varying quantities of water at different stages
of plant growth. nless adequate and timely supply of water is assured, the physiological
activities taking place within the plant are bound to be adversely affected, thereby
resulting in reduced yield of crop. The amount of water to be irrigated in an irrigation
schedule depends upon the evapotranspiration#0T$ from adacent soil and from plant
leaves at that specified time. The rate of 0T of a given crop is influenced by its growth
stages, environmental conditions and crop management. The consumptive use or
evapotranspiration for a given crop at a given place may vary through out the day,
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through out the month and through out the crop period. Galues of daily consumptive use
or monthly consumptive use are determined for a given crop and at a given place. t also
varies from crop to crop. There are several climatological factors, which will influence
and decide the rate of evaporation. 1ome of the important factors of eliminate influencing
the evaporation are radiation, temperature, humidity and wind speed. n this work, the
input variables chosen for the system are evapotranspiration and rate of change of
evapotranspiration called as error and the output variable is water amount a shown in
fig.6
4./ IRRIGATION PARAMETERS FOR EFFICIENT SYSTEM OPERATION
To ensure proper design and operation of an irrigation system, the following
parameters should be considered.
i$ &
&4/T
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Fuzzy 'ogic &ontrol #F'&$ system is based on fuzzy set theory. This set theory is
advanced version of classical set theory called crisp theory. n crisp set theory, an element
either belongs to or does not belong to a set. =ut fuzzy set supports a flexible sense of
membership of elements to a set. @any degrees of membership, between + and 6, are
allowed. The membership function is associated with a fuzzy set in such a way that the
function maps every element of the universe of discourse or the reference set to the
interval B+, 6C. n crisp logic, the truth values acquired by propositions or predicates are
two%valued, namely T
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I. FUIFICATION UNIT
t converts a crisp process state into a fuzzy state so that it is compatible with the
fuzzy set representation of the process required by the inference unit.
II. KNOWLEDGE BASE
The ?nowledge base consists of two components. " rule base, which describes
the behaviour of control surfaces, which involves writing the rules that tie the input
values to the output model properties.
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IV. DEFUIFICATION UNIT
t converts the fuzzy control action generated by the inference unit into a crisp
value that can be used to drive the actuators. The defuzzification methods such as
centroid method, center of maxima method have been predominant on fuzzy control.
!erhaps the most frequently used defuzzification method is the centroid method.
. FUNCTIONAL AND TECHNICAL DETAILS
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DESIGN PROCEDURE FLC FOR IRRIGATION CONTROL
The heart of the F'& is to form the knowledge base that can obtained form human
experts is that field. n designing F'&, the following five steps are to be followed.
S'! 1 5 I&!)'i6i,$'i() $)& D!,*$$'i() (6 I)u'% $)& Ou'u'
This is the basic step in which the inputs and output are identified. n the
controller design for irrigation control, the inputs are evapotranspiration and error and the
output is water amount. The process of declaring the values of inputs and output called
universe of discourse is shown in table 6.
TABLE 1. U)i7!%! (6 &i%,(u%!
N$+! I)u'8Ou'u' Mi) 7$*u! 9 M$: 7$*u! 9
0vapotranspiration nput + 6++
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for water output. The input and output variables are represented by fuzzy membership
functions as shown in Fig Ja, Fig Jb and Fig Jc.
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S'! 35 B!-$7i(u (6 C()'(* Su6$,!%
Fuzzy rules are constructed in specify action for different conditions, that is the
control rules the associate the fuzzy output to fuzzy inputs are derived from general
knowledge of system behaviour. n this method, the rules are extracted form numerical
data and then combined with linguistic information collected for experts. The rule bas for
the said application is shown in Table *. The weightage take for rules involving zero error
is reduced to +.*L for facilitating over correcting problems.
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S'! 4 5 D!,i%i() M$;i)g L(gi, O6 I)6!!),! L(gi,
t infers a system of rules through the fuzzy operator. n inference mechanism
!
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good results in terms of accuracy and has a wide scope of being established in near
future.
=y applying the fuzzy logic system, the results which were already observed
#referred from 0T0 Technical
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#symbolics$. This has advantages over pure mathematical #numerical$ approaches or pure
symbolic approaches because very often system knowledge is available in such a
combination.
= !roblems for which an exact mathematically precise description is lacking or is only
available for very restricted conditions can often be tackled by fuzzy logic, provided a
fuzzy model is present.
= Fuzzy logic sometimes uses only approximate data, so simple sensors can be used.
= The algorithms can be described with little data, so little memory is required.
% The algorithms are often quite understandable.
% Fuzzy algorithms are often robust, in the sense that they are not very sensitive to
changing environments and erroneous or forgotten rules.
- The reasoning process is often simple, compared to computationally precise systems, so
computing power is saved This is a very interesting feature, especially in real time
systems.
= Fuzzy methods usually have a shorter development time than conventional methods.
"lthough the above named advantages are very promising, one must be aware that fuzzy
logic does not fit to every problem. The following remarks must be made-
= "s has been shown in section J, fuzzy logic amounts to function approximation in the
case of &risp%nputD&risp%4utput systems. This means that in many cases, using fuzzy
logic is ust a different way of performing interposition n the light of the fact that system
knowledge is often available as a combination of numerics #quantitative$ and linguistics
#quantitative or qualitative$ this approach may even be advantageous.
= n areas that have good mathematical descriptions and solutions, the use of fuzzy logic
most often may be sensible when computing power #i.e. time and memory$ restrictions
are too severe for a complete mathematical implementation.
= am convinced that results obtained in successful fuzzy application,- that are given in
literature can be reached with a conventional approach as well, possibly taking longer
development time and possibly with the use of different interpolation methods. &areful
analysis of comparison examples, OprovingO the superiority of fuzzy logic often shows that
they compare the fuzz$ approach with a very simple, non%optimized conventional
approach.
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. FUTURE SCOPE
&rop irrigation control is the most important concern in the domain of agriculture.
1hortage of water globally is also emphasizing the need of systems that not only control
the crop irrigation but also provide the intelligent way to provide water to only those
places where it is needed and in the required quantity. =y monitoring soil moisture, 'eaf
;etness, Temperature and
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>. CONCLUSION
=ased on crop growth by water stress, diseases such as forced, the characteristics
of environmental factors, a crop of conventional precision irrigation two%mode fuzzy
control model was designed in this paper. n order to overcome subectivity regulations
on control the influence on the quality of fuzzy control model, the self learning function
was introduced in the structural design, a suitable for crop growth self%learning fuzzy
control algorithm was put forward, and a crop precise irrigation self%learning fuzzy
control model was established, and makes fuzzy control system has the self%perfection
sex. 1o the system as the work of change amendment rule to adapt the practical situation.
1imulation results show that this control strategy for overcoming the crops of fuzzy
control precision irrigation system exists when the normal amount of irrigation water
waste and precisely when the irrigation low efficiency, give water too much, can in the
normal amount of irrigation take safety and energy saving, precise irrigation take the
safety and efficiency for crops, precision irrigation intelligent control provides a control
strategies and methods.
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1?. REFERENCES
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ntegration 4f :igh !recision 1atellite Aata, "dvanced @odeling, !rocess &ontrol "nd
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BJC :al ;erner, 0xtension irrigation engineer, @easuring 1oil @oisture for rrigation
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@onitoring 1ystem manual$
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http://www.memsic.com/products/wireless-sensornetworks.htmlhttp://www.memsic.com/products/wireless-sensornetworks.html