simulation of the soil ph control system using fuzzy logic ...psrcentre.org/images/extraimages/4...

Abstract— Agriculture can be categorized as plants and food. Some basic factors that can affect both plant growth and quality of

the enlargement produced including temperature, relative humidity, intensity of light received by the plants and soil pH levels. Light intensity affect food manufacturing plant, stem length, leaf color and flower size. Light intensity too much can lead to amazing plants and increase the temperature in the room that enlargement can lead to increased respiration of plants in the rate of photosynthesis. The aim of this project is to design and develop control systems to provide and maintain agricultural soil pH value corresponding to a particular type

of plant. The suitable pH value will help the growth of plants perfectly. In order to provide efficient control of lighting intensity, fuzzy expert system is design with a graphical user interface (GUI) in Matlab. A fuzzy expert system developed to recognize changes in temperature, humidity and lighting in the plant area and determine the level of intensity of light. Graphical user interface (GUI) for this project is the design to show the real value of temperature, humidity and lighting in the room expansion and animation illustrates the

output to change soil pH Trend. Matlab software is used to perform simulation and fuzzy expert system prototype developed agricultural control room for face and shows the process of the system.

Keywords—fuzzy logic, Artificial Intelligence, Control System,

GUI

I. INTRODUCTION

O develop any expert system, the need to identify and

specify the problem as well as know the input, process

and the outcome. Plant growth is often limited by the

environment. If any environmental factor is less than ideal it

will become a limiting factor in plant growth. This paper

provide four main linguistic variables; temperature, humidity,

light intensity and soil ph. Linguistic variables are then used to

translate real values into linguistic value. The linguistic values

have to be properly identified to make sure the system will

work efficiently with all the condition, logic and measureable.

Growing roses in hot climates can be challenging especially with ongoing periods of high temperatures.. In higher

temperatures, while the roses grow faster, the quality of

production is lower. At lower temperatures growth will be

slower and flowering less profuse. Temperature during the

plant’s daytime can range between 55-75F or 13C-24C. It is

M.A. Abu is with the Universiti Kuala Lumpur British Malaysian

Institute, 53100 Kuala Lumpur (e-mail: [email protected]).

E.M.M Nasir is with the Universiti Kuala Lumpur British Malaysian

Institute, 53100 Kuala Lumpur.

C.R. Bala is with the Universiti Kuala Lumpur British Malaysian Institute,

53100 Kuala Lumpur.

very important to control the temperature for the newly

planted roses [10].

As for the humidity of growing roses, they normally need 50 –

60% of humidity for better growth. Roses generally enjoy a

pH of 6.0 through 6.9, with about 6.5 being ideal. In other

words, the soil should be just slightly acidic. For pH outside

this range, the availability of nutrients to the plant is greatly

affected

II. RELATED WORK

Fuzzy logic controller has been widely used in many

modern applications. However, the objectives and techniques

are slightly different according to demand and how to use. In

the area of agriculture, the same request was made using fuzzy

techniques in the past. One example was discussed and system

reference design is to control the water pump and the quantity

of nutrients given to plants [16]. Computer control system and the hardware used to monitor the physical condition of the

environment. This system is designed to control irrigation for

crops with three state variables of soil moisture, salinity and

pH of irrigation water. The sensor is placed in a system that

generates an electrical signal directly related to the parameters

to be measured. In this paper, a discrete sensor acts as a switch

that is useful to indicate the threshold value that is placed in

the plant area. A tensiometer switching is used to detect if soil

moisture is above the threshold of desire and PHE-45P pH

sensor is used to indicate if a certain pH level has been

reached. Fuzzy logic controller is then connected to the hardware by using serial communication to control irrigation

and nutrients given to plants. Consequently, this paper states

that the project is complex, expensive and sometimes unstable

to try to control all the variables caused by interface devices

that are used in the system. Therefore, the state of the system

at any time is defined by a set of variables measured

magnitude is important. This system provides a flexible design

to maintain the level of irrigation and quantity of nutrients to

the plants to be controlled naturally [6].

III. PROPOSED TECHNIQUE

The aim of this paper is to control the level of soil ph for

roses using fuzzy expert system by altering ph soil to an

adequate level to replace the adding of the fertilizer directly

and ensure a healthy growing of the plants. The input for this

system is temperature, light intensity and humidity. Figure 1

below shows the proposed system block diagram.

Simulation of the Soil pH Control System Using

Fuzzy Logic Method

M.A. Abu, E.M.M. Nasir, and C.R. Bala

T

International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2014) Dec. 15-16, 2014 Pattaya (Thailand)

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Fig.1 System Block diagram

IV. LINGUISTIC VARIABLES

Structure fuzzy system identifies the logical interference

flows from input variables to output variables. The

fuzzification of input interfaces translates analog input to the

obscure. Fuzzy inference rules applicable in blocks which

contain linguistic control rules. Output blocks is variable

linguistic rules. The defuzzification in output interface is

translated into analog variables.

TABLE I

LIGUISTIC VARIABLE

No. INPUT VARIABLE

1 Temperature

2 Light Intensity

3 Humidity

No OUTPUT VARIABLE

1 Soil PH

Fig.2 Structure of the fuzzy logic system

a) Input Variables

Table below shows the range for each input variable as well

as the type of fuzzy logic system methods used.

TABLE II

INPUT VARIABLE

b) Output Variables

Table below shows the range for each variable with the type of

fuzzy logic system method for output variable.

TABLE III

OUTPUT VARIABLE

V. LINGUISTIC VALUES

Linguistic variables were then used to translate real values

into linguistic values. The possible values of a linguistic variable are not numbers but so called Linguistic terms. The

fuzzy set for both inputs and output linguistic variable have

been determined for the system as shown in table below.

TABLE IV

INPUT LINGUISTIC VALUES

TABLE V

OUTPUT LINGUISTIC VALUES

VI. MEMBERSHIP FUNCTION

Depending of the variable level, each of these terms

describes the condition more or less well. Each term is defined

by a membership function (MBF). Each membership function defines for any value of the input variable the associated

degree of membership of the linguistic term. The membership

functions of all terms of one linguistic variable are normally

displayed in one graph. The following figure plots the

membership functions of the three terms for all the linguistic

value.

Fig.3 Membership function for temperature


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TABLE VI

TEMPERATURE DEFINITION POINT

TABLE VI

TEMPERATURE REFERENCE VALUE

Fig.4 Membership function for light intensity

TABLE VII

DEFINITION POINT FOR LIGHT INTENSITY

TABLE VIII

REFERENCE VALUE FOR LIGHT INTENSITY

Fig.5 Membership function for light humidity

TABLE VIIII

DEFINITION POINT FOR HUMIDITY

TABLE X

REFERENCE VALUE FOR HUMIDITY

Fig.6 Membership function for soil Ph

TABLE XI

DEFINITION POINT FOR SOIL PH

TABLE XII

REFERENCE VALUE FOR SOIL PH

VII. FUZZY RULES AND RESULTING GRAPH

In designing the fuzzy logic system, a fuzzy rule plays the

most important part in determining the possible outcomes of

the system. Fuzzy reasoning includes two distinct parts.

i. Evaluation the rule Antecedent (the IF part the rule)

ii. Implication or applying the result to the consequent

(the THEN part of the rule)

In fuzzy expect system, where the antecedent is fuzzy

statement. All rules fire to some extent or in other words they are partially. If the antecedent is true to some degree of

membership, the consequent is also true to that same degree.


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TABLE XIII

LEVEL FOR EACH INPUT VARIABLE

For Table XIII above, as a production rule, a fuzzy rule can

have multiple antecedents, for example:

IF temperature is low

AND light is Normal

AND Humidity is Normal

THEN Soil pH is Acid

All part of the antecedent are calculated simultaneously and

resolved in a signal number, using fuzzy set operation considered in the previous section.

A context is defined by the same input and output variable of

the rules “IF” part describes the situation, for which the rules

are designed. The “THEN” part describes the response of the

fuzzy system in this situation. The degree of support (DoS) is

used to weight each rule according to its importance.

TABLE XIIII

LEVEL FOR EACH INPUT VARIABLE

VIII. SIMULATION RESULTS

To simulate the fuzzy engine system, several testing data

need to be tested in the matlab working system. Testing data

starts from the fuzzification and defuzzification in matlab.

Several testing were done to tune the fuzzy engine as

following.

Fig.7 Result simulation

The 3D function graph plot and shown the relationship

between the input variables and output variable. The shape of

the graph will determined its importance for each variable in

the fuzzy system. The following Figure plots the relationship

of input and output variables. Since, the output id diversified

to two variables, the 3D graph will show the relationship

between the three input variables with a different outputs

variable which is the soil pH value.

3D Graph with Soil pH as an Output Variable

a. Input Variables (Temperature and Light Intensity) with

Output Variable (Soil pH)

Fig.8 3D Simulation for Input Variables temperature and light

intensity with output soil pH

b. Input Variables (Temperature and Humidity) with Output

Variable (Soil pH)

Fig.9 3D Simulation for Input Variables temperature and humidity

with output soil pH


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c. Input Variables (Light Intensity and Humidity) with

Output Variable (Soil pH)

Fig.10 3D Simulation for Input Variables light intensity and humidity

with output soil pH

Based on the results after the testing, the output (soil ph) is

slightly different from the original value. From the first testing

data, the output variable which is the soil pH shows that when

temperature is Hot, light intensity is Bright, humidity is Dry,

and the soil pH is Alkali. The fuzzy system was tuned several

times to make sure that the fuzzy logic work perfectly

according to the constructed fuzzy rules. There are 27 rules

that the fuzzy system need to follow before make any decision

on the output variable. The tuning was made at the fuzzy

levels of each variable. First, we try to set temperature at 0.5,

light intensity at 2.5 and humidity at 0.5. The original output should be 1.56. Then, the value is simulated with another

testing to make sure the system is wokring according to the

fuzzy rules. The testing results from the MatLab shows that

the output variable which is the soil pH is 2.16 when

temperature is 0.681, light intensity is 1.05 and humidity is

0.33. According to whole data, the soil pH will produce

suitable pH value dependent on the temperature, light intensity

and humidity.

IX. CONCLUSION

As a conclusion, this system by using a fuzzy logic method

with Matlab software is completely designed and simulated. A

fuzzy system is successfully design in order to control the soil

ph by altering it to adequate level. The identified linguyistic

variables and membership function as well as fuzzy rules are

well tabled and constructed. We believe when there is more

rules, the system will be stabilize and the soil ph will be more

accurate dependent on the temperature, light intensity and

humidity for a better and healthy growth of roses. By

constructing the fuzzy system, the conditions for roses to grow is collected and analysed. Roses live and grow better with

slight acidic soil ranged between 6.0 to 6.9. Finally, the fuzzy

system is successfully designed.

ACKNOWLEDGMENT

The authors would like to thank Prof. Dr. Badri Abu Bakar

for his help. This work was supported in part by the Short

Term Research Grant under grant no. STR11050.

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