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Modern Irrigation System Towards Fuzzy


Published on Dec 12, 2015

Abstract

In 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 Control has been applied in traditional control systems, which yields promising results, It 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. In fact, fuzzy logic controller (FLC) 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.

NEED FOR MODERN IRRIGATION SYSTEM

Water and electricity should be optimally utilized in an agricultural like India. The development in the filed of science and technology should be appropriately used in the field of agriculture for better yields. Irrigation has traditionally resulted in excessive labour and nonuniformity in water application across the filed. Hence, an automatic irrigation system is required to reduce the labour cost and to give uniformity in water application across the field.

PHYSIOLOGICAL PROCESSING

In the irrigation system, plant take-varying quantities of water at different stages of plant growth. Unless 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(ET) from adjacent soil and from plant leaves at that specified time. The rate of ET 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, through out the month and through out the crop period. Values of daily consumptive use or monthly consumptive use are determined for a given crop and at a given place. It also varies from crop to crop. There are several elimatological factors, which will influence and decide the rate of evaporation. Some of the important factors of elimate influencing the evaporation are radiation, temperature, humidity and wind speed. In 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.

FUZZIFICATION UNIT

It 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.

KNOWLEDGE BASE

The Knowledge base consists of two components. A rule base, which describes the behaviour of control surfaces, which involves writing the rules that tie the input values to the output model properties. Rule formation can be framed by discussing with the experts. A database contains the definition of the fuzzy sets representing the linguistic terms used in the rules. The knowledge base is generally represented by a fuzzy associative memory.

INFERENCE UNIT

This unit is the core of the fuzzy controller. It generates fuzzy control actions applying the rules in the knowledge base to the current process state. It determines the degree to which each measured valued is a member of a given labeled group.

A given measurement can be classified simultaneously as belonging to several linguistic groups. The degree of fulfillment (DOF) of each rule is determined by applying the rules of Boolean algebra to each linguistic group that is part of the rule. This is done for all the rules in the system. Finally the net control action is determined by weighting action associated with each rule by degree of fulfillment.











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