implementation of particle swarm optimization pso algorithm on potato exp

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-

6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME

82

IMPLEMENTATION OF PARTICLE SWARM OPTIMIZATION (PSO)

ALGORITHM ON POTATO EXPERT SYSTEM

A.Sri Rama Chandra Murty1& M. Surendra Prasad Babu

2

1&2(Dept. of Computer Science & Systems Engineering, Andhra University, Vishakhapatnam, A.P)

ABSTRACT

The aim of this paper is to providing expert advice & information to the potato cultivators by

developing Potato Expert System. A Particle Swarm Optimization is evolutionary computational

technique which can be applied to solve optimization problems. The Potato Expert System is

implementation by using PSO algorithm in three phases: first, developing Potato Knowledge base.

Second, Machine learning algorithm is implemented for data collection. Third, Potato Expert System

shell developed using Rule Based Expert System with Backward Chaining. The Potato Expert

System interface tool is developed that provides experts’ advice to cultivators for disease

management.

Keywords: Particle Swarm Optimization, Potato Expert System, Machine Learning, Genetic

Algorithms, Backward Chaining, Rule Based Expert System.

I. INTRODUCTION

Expert system in general simulates both the knowledge and know-how of human experts i.e.,

the expert system solves problems that are normally solved by human experts. All expert systems

include at least three basic elements i.e., a knowledge base, which represents, what is known about a

given subject at present, an interface engine comprises the logistics to apply what is known to what is

yet unknown. Expert systems are most common in specific problem domain, and are traditional

application and/or subfield of artificial intelligence.

Based on the different representation schemes and interface techniques, the expert systems

are classified as rule based expert systems, frame-based expert systems case based expert system and

model based expert system. Several representations such as list of rules and facts, frames and slots,

semantic networks, OAV- triplets etc., are used in the above expert system. The interference engine

may infer conclusion from the knowledge base and the fact supplied by the user. Several expert

systems are widely used in various fields like medicines, geology, system configuration and

engineering design and structural analysis of chemical compounds.

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A rule-based, expert system maintains a separation between its Knowledge base and that part

of the system that executes rules, which was often referred to as the expert system shell. Rule-based

system represents knowledge as a bunch of rules and assertions. It involves a database that stores the

assertions and rules that can perform some action (production system) or deduce consequences

(deduction systems). The implementation of control over a finite set of assertions allow the systems

to dynamically generate new knowledge (forward chaining) and breakdown a complicated problems

in to many smaller ones (backward chaining). Unification (matching variables) allow flexibility of

the rules, with which the systems can deduce more specific facts (forward chaining) and solve more

specific problems (backward chaining) without having a giant set of smaller rules. Deduction

systems contain only IF-THEN rules.

Machine Learning refers to a system which is capable of autonomous acquisition and

integration of knowledge. The goal of machine learning is to program computers to use example data

or past experience to solve a given problem. Many successful applications of machine learning exist

already, including systems that analyse past sales data to predict customer behaviour, recognize faces

or spoken speech, optimize robot behaviour so that a task can be completed using minimum

resources, and extract knowledge from bio-informatics data.

Potato (Solanum tuberosum L.) popularly known as ‘The king of vegetables’, has emerged as

fourth most important food crop in India after rice, wheat and maize. Indian vegetable basket is

incomplete without Potato. Because, the dry matter, edible energy and edible protein content of

potato makes it nutritionally superior vegetables as well as staple food not only in our country but

also throughout the world. Hence, potato may prove to be a useful tool to achieve the nutritional

security of the nation.

Several varieties are grown in different parts of India. China and Rio-De- Janeiro are the two

imported varieties of potato. Other important varieties grown are kufri, Chipsona-1, Kufri Chipsona-

2, Kufri Jyothi, Kufri Luvkar, Kufri Chandramukhi, Kufri Badsah, Kufri Lavkar, Kufri Lalima, Kufri

Sindhuri, Irish Cobbler, Red Pontiac, Viking, Katahdin etc. This information is stored in Knowledge

base and retrieved through information system module. Several programmed interviews were

conducted with agricultural experts & progressive farmers and identified different diseases likes

Black Scurf, Potato Wart, Common Scab, Late Blight, Early Blight, Black leg and Soft Rot etc.,

II. POTATO EXPERT SYSTEM ARCHITECTURE

The architecture of the proposed Potato Expert System consists of 3 modules: (1) User

Interface (2) Interface Engine (3) Advices from Potato Expert System. The user interacts with the

system through a specially designed unified interface which assimilates the peculiarities of the

various components. A graphical user interface (GUI) provides a user friendly and comfortable

environment in which he/she works and communicates with Potato Expert System.

Fig.1. Proposed Potato Expert System Architecture



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The GUI presents interactive forms and command menus to retrieve and update system

parameters and steering variables, to enter user constraints and preferences and to prevent relevant

diagnosis, treatment information back to the user after simulations have run Potato Expert users can

query the system using an inference process that automatically matches facts against patterns and

determines which rules are applicable. When calling for the results of the diagnosis, the system will

explain the inference processes. After an examination of the facts collected from users, the system

will produce conclusions of the diagnosis and treatment methods. If inference process not matches

facts against the machine learning algorithm will be executed and produces the diagnosis and

treatment methods. The Knowledge base contains all the rules for the fish disease diagnosis. Each

rule has two sections – a symptom pattern section and an action section, in the form of ‘IF symptom

pattern E, THEN the disease H’. The advices produced by the expert system displays on the output

screen.

III. KNOWLEDGE BASE POTATO EXPERT SYSTEM

The rules used for the rule based system are extracted from Table 1.2 are given below.

Rule 1: IF the stage of the crop is seeding and the part of the crop affected is leaves and light

yellowing of the tips of lower leaves THEN the disease is Soft rot.

Rule 2: IF the stage of the crop is seedling and part of the crop affected is leaves and very small

round scattered spots in the youngest levels which increases with plant growth THEN the disease is

Leaf spot. Rule 3: IF the stage of the crop is seedling and the part of the crop affected is leaves and leaves of

infected plants tend to be narrower and more erect THEN the disease is Rhizome rot.

Rule 4: IF the stage of the crop is seedling and the part of the crop affected is leaves and small

powdery pustules present over both surfaces of the leaves THEN the disease is White grub.

Rule 5: IF the stage of the crop is seedling and the part of the crop affected is leaves and lesions

begin as small regular elongated necrotic spots and grow parallel to the veins THEN the disease is

Gray leaf spot.

Rule 6: IF the stage of the crop is seedling and the part of the crop affected is leaves and lesions with

oval narrow necrotic and parallel to the veins THEN the disease is Phyllosticta leaf spot.

Rule 7: IF the stage of the crop is seedling and the part of the crop affected is root white thin lesions

along leaf surface and green tissue in plants THEN the disease is Thread blight.

Rule 8: IF the stage of the crop is seedling and the part of the crop affected is root and the bushy

appearance due to proliferation of tillers which become chlorotic, reddish and lodging THEN the

disease is Fusarial wilt.

Rule 9: IF the stage of the crop is seedling and the part of the crop affected is root and irregular

section of epidermis and perforated leaves THEN the disease is Dry rot.

Rule 10: IF the stage of the crop is seedling and the part of the crop affected is stem and the affected

area just above the soil line is brown water-soaked soft and collapsed THEN the disease is Bacterial

wilt. Rule 11: IF the stage of the crop is seedling and the part of the crop affected is stem and affected

internodes become disintegrated and the presence of small pin-head like black sclerotic on the rind of

the stalks THEN the disease is Rhizome scale.

Rule 12: IF the stage of the crop is seedling and the part of the crop affected is stem and wilted

plants remain standing when dry and small dark-brown lesions develop in the lowest internodes

THEN the disease is Mosaic streak.

The following Database Records in Table 1 is used in the implementation of Rule Based Expert

System.



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TABLE.1. Rule Based Expert System Database Records

TABLE.2. Machine Learning Symptoms Database Table Description



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In machine learning system, the rules are stored in the form of 1’s and 0’s which are YES/NO

condition of the symptoms. These rules with corresponding diseases and their cure information are

represented below in Table 2, 3 & 4.

TABLE.3. Machine Learning Decision Database Table Description

TABLE.4. Machine Learning Database Table Description

IV. RULE-BASED EXPERT SYSTEM USING BACKWARD CHAINING

The first step in developing backward chaining system is to define the problem or learn about

the problem through reports, documents and books. The second step is to define the goals for the

system. Every backward chaining system needs at least one goal to get started. By defining the goals

this may help us to start from the right track and end on the expected track and this may avoid us



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from being misled from the real problem. The third step is designing the goal rules, such as IF

precondition1 AND precondition2 THEN portfilio1. With the goal portfolio1 is attained but undergo

first the two preconditions, the goal rules also have decision table to make and help the decision

making with this rules and testing for the goal rules.

The fourth step is to expand the knowledge of the system and these expansion techniques are

broadening the system knowledge that teaches the system about additional issues and deepening the

system knowledge teaches about the issues it already known. The fifth step is refining the system in

which there are several additional features that will enhance both its performance and maintenance.

The sixth step is designing the interface that will accommodate the needs of the user so that the user

can choose easily and efficiently. The final step is system evaluation and this is done by making

some questions to the expert and tests the system with sample input and sees if the system is really

running properly. The Backward chaining Mechanism for Rules based Potato Expert System is

shown in Fig.2.

Fig.2. Backward Channing Mechanism for Rule Based Potato Expert System

V. DESIGN OF PSO ALGORITHM FOR POTATO EXPERT SYSTEM

Particle Swarm Optimization (PSO) [1][2][3] is a population based stochastic optimization

technique, inspired by social behaviour of bird flocking or fish schooling. PSO shares many

similarities with evolutionary computation techniques such as Genetic Algorithms (GA) [4].

However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the

potential solutions, called particles, fly through the problem space by following the current optimum

particles. Each particle keeps track of its coordinates in the problem space which are associated with

the best solution (fitness) it has achieved so far. This value is called pbest [5]. Another best value that

is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the

neighbours of the particle. This location is called lbest [6].

When a particle takes all the population as its topological neighbours, the best value is a

global best and is called gbest. The particle swarm optimization concept consists of, at each time

step, changing the velocity of (accelerating) each particle towards its pbest and lbest locations.



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Acceleration is weighted by a random term, with separate random numbers being generated for

acceleration towards pbest and lbest locations.

Fig.3. Proposed PSO Architecture for Potato Expert System

PSO simulates the behaviours of bird flocking. Suppose the following scenario: a group of

birds are randomly searching food in the area. There is only one piece of food in the area being

searched. All the birds do not know where the food is. But they know how far the food in every

iteration. So what’s the best strategy to find the food? The effective one is to follow the bird which is

nearest to the food. PSO learned from the scenario and used it to solve the optimization problems. In

PSO, each single solution is a “bird” in the search space. We call it “particle”. All of particles have

velocities which direct the flying of the particles.

The particles fly through the problem space by following the current optimum particles. PSO

is initialized with a group of random particles (solutions) and then searches for optima by updating

generations. Every particle is updated by two best values in all iterations. The first best solution is

the fitness value called pbest. Another best value that is tracked by the particle swarm optimizer and

obtained so far by any particle in the population is the global best value called gbest. When the

particle takes part of the population as its topological neighbours the best value is the local best and

is called lbest.

After finding the two best values, the particle update its velocity and positions with following

equations

v[]=v[] +c1 * rand() * (pbest[] – present[])+c2 * rand() * (gbest[] – present[])… (a)

present[] = present[] + v[] …. (b)

v[] is the particle velocity, present[] is the current particle (solution). Pbest[] and gbest[] are defined

as stated before. rand() is a random number between (0,1). c1,c2 are learning factors, usually

c1=c2=2.

Procedure of Proposed PSO Algorithm Step 1: For each particle initialize.

Step 2: For each particle calculate fitness value.

Step 3: If the fitness value is better than the best fitness value (pbest) in history set current value as

the new pbest.

Step 4: Choose the particle with the best fitness value of all the particles as the gbest.

Step 5: For each particle calculate particle velocity ie., equation (a)

Step 6: Update the particle position ie., equation (b).

While maximum iterations or minimum error criteria is not attained, particles velocities on

each dimension are clamped to a maximum velocity Vmax. If the sum of accelerations causes the

velocity on that dimension to exceed Vmax, a parameter specified by the user, then the velocity on

that dimension is limited to Vmax. However, PSO does not have genetic operators like crossover and

mutation. Particles update themselves with the internal velocity. Compared with GAs the



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information sharing mechanism in PSO is significantly different. In GAs, chromosomes share

information with each other. So the whole population moves like a one group towards an optimal

area. In PSO, only gbest (or lbest) gives out the information to others. It is one-way information

sharing mechanism. The evolution only looks for the best solution. Compared with GA, all the

particles tend to converge to the best solution quickly even in the local version in most cases.

VI. CONCLUSIONS AND FUTURE WORK

The main emphasis of the paper is to have a well-designed interface for giving Potato plant

related advices and suggestions in the area to farmers by providing facilities like online interaction

between expert system and the user without the need of expert all the time. based on the proposed

architecture, a new expert system shell, capable of developing a Rule based Expert System with

Machine Learning capabilities, especially using PSO algorithm, will be designed. The expert systems

developed using this shell are expected to show better performance than the systems developed using

other algorithms like ABC algorithm and ACO algorithm.

The proposed interface tool can be extended further by creating more features and facilities to

the user and the subject expert. The shell can include all the system designs like static and dynamic

systems and other user friendly features so that the expert can design and make any changes online to

the system according to the future R&D in the crop production through proper administration

privileges.

The present system is developed for only disease management of potato crop, to provide a

complete advice to potato farmers; it is to be extended to all the aspects in farming such as Soil

management, Fertilizer management, Irrigation management, Marketing & Storage management,

Crop management etc. The system may be improved much by adding new features like language

translation facilities and adding the new updates in the crops and production techniques and

embedding audio, video clips and IVRS systems.

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BIOGRAPHIES

A.Sri Rama Chandra Murty, (Andhra Pradesh Civil Services – Executive

Branch) Special Grade Deputy Collector and presently working as Executive

Officer, Srikalahasteeswara Swamy Vari Devasthanam, Srikalahasti (A.P). He

obtained B.Com., M.Tech. (Computer Science & Technology – Artificial

Intelligence & Robotics), M.Sc. (Information Technology), MCA, PGDCPA,

B.L., L.L.M. (Corporate & Security laws), MBA (Finance & Marketing dual

specialization), MBA (Human Resources), M.Sc. (Psychology), M.A. (Public

Administration), PGDIRPM, MSPR (Master of Science in Public Relations). His areas of interests

are Expert systems and Artificial Intelligence.

M. Surendra Prasad Babu obtained his B. Sc, M.Sc. and M. Phil and

Ph.D. degrees from Andhra University in 1976, 1978, 1981and 1986

respectively. During his 27 years of experience in teaching and research, he

attended about 28 National and International Conferences/ Seminars in India and

contributed about 33 papers either in journals or in National and International

conferences/ seminars. He has guided 98 student dissertations of B.E., B.Tech.

M.Tech. & Ph.Ds. He is now Head of the Department of Computer Science &

Systems Engineering of Andhra University College of Engineering, Andhra

University, Visakhapatnam. He received the ISCA Young Scientist Award at the 73rd

Indian Science

Congress in 1986 from the hands of late Prime Minister Shri Rajiv Gandhi.

implementation of particle swarm optimization pso algorithm on potato exp

Technology

introduction expert

potato expert system

expert systems case

rulebased system

system configuration

potato knowledge base

expert advice information

potato cultivators