modern irrigation systems using fuzzy logic technique

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    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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    B*C 0dward &. @artin, @ethods of Aetermining ;hen to rrigate &ooperative 0xtension,

    &ollege of "griculture H 'ife 1ciences, The niversity of "rizona .

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    ntegration 4f :igh !recision 1atellite Aata, "dvanced @odeling, !rocess &ontrol "nd

    =usiness nnovation .

    BJC :al ;erner, 0xtension irrigation engineer, @easuring 1oil @oisture for rrigation

    ;ater @anagement, &ooperative 0xtension 1ervice .

    BC http-DDwww.memsic.comDproductsDwireless%sensornetworks.html #0nvironmental

    @onitoring 1ystem manual$

    BLC 9umman " and 'ecler /',Q" continuous soil water potential measurement 1ystem for

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    B8C @. &ogan. Q;ater measurement in soil and vinesQ.

    http://www.memsic.com/products/wireless-sensornetworks.htmlhttp://www.memsic.com/products/wireless-sensornetworks.html