Penaeus Monodon :Prawn Diseases Management Expert System

Nestor D. Luna Marcelino R. Veloso National Polytechnic College,

Palompon Institute of Technology-Tabango Campus Tabango, Leyte

+63(053) 551 9014 mrvnpc_tabango@yahoo.com

nphall88@yahoo.com

Abstract

Expert system is a computer program that provides expert advice as if a real person had been consulted, which these systems capture and deliver knowledge that is not easily represented using traditional computing approaches. Expert systems could be used to gain access to expertise immediately, around the clock, by many people at the same time.

This study entitled “Penaeus Monodon : Prawn Diseases Management Expert System” was conducted to develop a system aims at providing comprehensive tool for prawn disease management. The system will provide vital information on the prawn diseases resulting to growth retardation, physical deformity, reduced fecundity, physiological malfunction and mortality, all integrated in a single ES. The ES will be dynamic as new facts are inserted into the knowledge base, allowing the system to adapt and learn new breakthroughs and studies in diseases management of a prawn. As a tool for end-user, starting with general questions, the system automatically supplies the most relevant questions depending on the user’s responses. As a tool for experts, it will provide an easy interface where they can transform their expertise into a digital knowledge base. Several Methodologies, models, tools and techniques were established in defining, organizing, and structuring the components of the final solution system that will serve as the blue print in the construction of the proposed system. The following methods and system implementation activities were designed and implemented. Knowledge Engineering Process and Expert System’s Architecture Design are the two methods used in developing the rule-based expert system. The Graph Search, Solution Matrix, Production Rules and Rating Mathematical Analysis were specifically sued in the engineering process. Systems Development Life Cycle was the approach used in the systems analysis; and a Rapid Application Development approach was utilized in the development of the proposed system. Specifically, the methodologies and tools used in the planning, analysis, design and development of the proposed ES include: Logical Framework Analysis, Gantt Chart and PERT/CPM; Software Development Framework; Project Management (Software, Hardware, People); Cost-Benefit Analysis; Input/output Requirements; Data Flow Diagram; Program Flowchart; Entity-Relationship Diagram and normalization; Network Model; Prototyping; and User’s Manual.

Keywords: Penaeus Monodon, Expert System, computer program, end-user, experts

1. Introduction

Cultured shrimp is a major export commodity of the Philippines. Because of its high yield, monoculture of shrimps in semi-intensive and intensive system has been the prepared production method. However, this approach led to the proliferation of diseases resulting growth retardation, physical deformity, reduced fecundity, physiological malfunction, and mortality. Even if they are outright rejected, diseased shrimps command a lower price in the market. Researchers and marine scientists continuously find ways to improve every aspect of the use of prawn which range from production, breeding, hatching , disease management and pathology, to new a few. These findings have become catalysts that enable us to discover even new breakthroughs about Prawn. These ever-improving breakthroughs continued to grow and one of theses is certainly the management of Prawn diseases where expertise in this field of work. With the advent of the information technology, the same can be found easily using only the tips of our hands. Despite, these rich resources, human experts in this area come very rarely from one place to another because of the varied geographical locations. Aqua farmers, students and filed technologists, come to these experts and do consultation to employ good management practices in terms of diseases management. There is categorically a missing link between access to a wide range of expert’s knowledge and people who need this expertise in actuality. Concurrent to this agricultural revolution, is the rapid advancement of computer technology. Computers have become effective tool in accessing a virtually unlimited pool of information. In the field of artificial intelligence, ES has become potential alternatives for the human experts. With computers as experts, students and field technologists anytime and anywhere as if a real expert is around to assists. ES is one of the important application oriented branches of Artificial intelligence. It is defined as “an intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solution” In the past decade, a great deal of ESs have been developed and applied to many fields such as office automation, science, and medicine including agriculture. In recent years, research and development of the ES fields of agriculture domain have been paid

much attention by many countries, not only by developed countries but also developing countries. The complexity of problems confronting Prawn farmers like yield loses, diminishing market prices from international competition, increasing chemical pesticides costs and pest resistance and economic barriers hindering adoption of Prawn farming strategies necessitates that they become expert managers of all aspects of their farming operations. While, one may contend that websites and research publications may provide the same information, ES allows itself to improve its knowledge base by being fed with new knowledge as time progresses. The system therefore is learning when new discoveries are found and thus the knowledge base widen just as the wisdom of a human expert. With computer ES, information across different disciplines of plant pathology, crop nutrition, entomology and pest and diseases management, can be integrated and converged into one single knowledge base providing researchers and farmers one stop access point where management of prawn diseases are readily available with maximum ease. This study aims to address scarce availability of ES that is able to adapt and learn new knowledge. It likewise addresses the lack of user-friendly ES dedicated to the management of diseases of Prawn. Prawn production has evolved into a complex business requiring the accumulation and integration of knowledge and information from any diverse sources. The modern farmer often relies on agricultural specialists and experts from the solution of their problem relating to prawn disease management. Unfortunately, expert’s assistance may not be always available when the farmer needs it. In order to alleviate this problem, this research is conducted for the purpose of providing a means of collating, hopefully all knowledge of experts on prawn health in order to build sustaining knowledge base aimed at providing a powerful tool in the management of prawn diseases. 2. Materials and Methods.

This study called for descriptive study and was utilizing a survey method, which involves the questionnaire to evaluate the system and conducted preliminary investigations and series of interviews to gather relevant information in line with the study.

The researcher adopted various data gathering tools to support and enrich the preparation of an expert system. The following instruments used in this study were: questionnaire, observation, and library techniques. The questionnaire used by the student and teacher respondents assessed the developed expert system in terms of content, manner of presentation, and usefulness of the material.

Observations were also made from the students as to their reactions and attitude to ward the expert system while trying it out. 3. Result and Interpretation After a thorough analysis of the data, the following findings are formulated that: 1. The extent of need as perceived by the respondents in the development of an expert system on prawn diseases management expert system was very often needed. 2. The difference in the extent of need as perceived by the respondents in the development of an expert system on prawn diseases management expert system among the groups of respondents was significant. Therefore, the status of hypothesis is rejected. 3. The important features that were ranked as very important should be included in the development of an expert system on prawn diseases management expert system . 4. System Development

System Architecture

The figure below illustrates the software development scheme. Due to time considerations, the researcher had decided to establish a prototyping and a rapid application development technique in the information-gathering and software development respectively, to supplement some of the traditional approaches in system development.

Since the researcher used prototyping model, the development process started with a simple working model following some good features inherent in existing agriculture-related ESs. From this point the development process follows the mock-up – revise –test cycle to come up with a more desirable result.

The design of the system architecture above here shows how the system interacts with the user and the expert. All inputs coming from the user or expert are captured from the graphical user interface designed using the visual basic integrated development environment. When the user attempts to

consult the ES,initial facts are already asserted at the start of the consultation. These are the general questions and a list of possible choices for the question which are pushed to the clips router object. Through the router, the external front–end application is able to process and capture the active agenda fired by the C-Language Integrated Production System (CLIPS) engine. Such data are essentially an array of string information that act like 'result sets' in response to the data asserted back to CLIPS engine which is the user’s selection from a pool of choices presented for every question asked. The values from the router are then encoded as a query object for further user response. This object represents the node being visited in semantic tree which will eventually result to a case diagnosis. This cycle continues until a path leading to a conclusion is found. In the event, that the set of user’s responses do not deduce to conclusive results, this set responses are processed again by the CLIPS inference engine and use its rules to decide which prognosis has a close description to the case presented. This will allow the user to browse the set of matching diagnoses and read the details of each anomaly for more information.

Figure 1 Expert System Block Diagram

As shown in the previous diagram, the software development stages started with the requirements planning phase which includes two main activities, these are the identification of the objectives of the proposed system and identification of information requirements. Specific activities include: formulation of Logical Framework Analysis; preparation of Gantt Chart and PERT/CPM diagram; performing project management that involves recommendation of software, hardware and the people; conducting Cost-Benefit Analysis; knowledge engineering and information gathering through data sources; data flow diagramming; program flowchart formulation; formulation of production rules; analysis and interpreting of current forms and reports. Furthermore, responses from the survey were utilized in the development of the proposed system, specifically, on the features to be included in the development of the proposed system.

After the identification of system requirements, the rapid application development had been established. Active involvement of the prospect users (fishpond technicians and the students) were intensely established in this phase. Specific activities include: designing of Entity Relationship Diagram; normalizing data; listing of data dictionary; designing of screen layout; designing the architecture of the expert system; designing of network specifications; documenting and coding of specifications. Immediate refinement was done based from the suggestions and recommendations from the identified users.

The final phase of the development scheme is the implementation phase, specifically, introducing the partial proposed system. Specific activities include: testing and simulation; evaluation; debugging and revisions; user’s manual formulation; delivery; and installation. Front-end Processing

The front-end part of the system is basically the interface to the outside world. It is where users and the system itself interact with each other. The expert are the real people expert in the field of prawn diseases. They are the fisheries, aquatic culturists and marine biologist. The users are those that consult the expertise of the system. They are the fishpond operators, fishpond workers , technicians, students and researchers.

The Knowledge Acquisition Facility is the graphical user interface designed to

capture the knowledge of a human expert into a digital knowledge.

The knowledge base is stored in the plain text file. In order to allow smooth interfacing with the graphical user interface, a database is used to temporarily refine system outputs and user inputs as well. A database is a collection of data that is organized so that its contents can easily be accessed, managed and updated. Functional keywords entered by the human experts are stored into the database purely for the purpose of interfacing to the graphical user interface design. These information are transformed programmatically by the system into an external text file ready to be loaded into the working memory. It is important to note that the solution or diagnosis of a disease problem is not controlled by the database system. Once a new knowledge in entered a text file is generated which represents as the knowledge base of the system. It is this file that is used to derive facts when users attempt to consult the expertise of the system.

During a case consultation, users respond to questions raised by the system. These are case assertions or facts entered in to system that determines which next question is asked. Initially, the system raises general questions and as the user responds, the next questions deduces into a more detailed query. This cycle continues until the most detailed facts in the agenda matches the prognosis of the case. Back-end processing

The bulk of the operations that occur in back-end processing are handled by the CLIPS engine and the CLIPS interface control. CLIPS is a productive development and delivery ES tool which provides a complete environment for the construction of rule and or object-based ESs. The CLIPS interface control is a windows platform ActiveX control which allows programmers to easily imbed the artificial intelligence engine of CLIPS into C++ and Visual Basic Programs.

Human expertise are Knowledge base rules. This is the knowledge of the experts transformed into rules. In every particular case, a rule might be used to infer a particular fact. The inference Engine is like the brain of the ES. It is the inference engine that controls which solution is appropriate for the specific case scenario.

The case facts are the facts loaded into the main memory. This is the initial data stored in the working memory where all facts related to the case are monitored by the

inference engine. The latter then decides which rules should be fired.

Figure 2. Expert System front-end and back-

end processing CLIPS Router

The CLIPS Router is also known as the logical input/output route of the CLIPS engine. This is the part of CLIPS that handles all input and output operation that occurs in the ES shell. Routers need not be predefined. If you attach to router 'foo', it will be created for you, if it already exists then you are attached to it. The CLIPS router object handles all buffering of logical input/output routes fired by the CLIPS engine. It is thru this object that the front end gains access to the facts active in the agenda of the ES shell. Router objects contain data local to themselves, thus if you have three router objects attached to a single route, deleting one of them does not affect the contents of another. (CLIPS Advanced Programming Guide). The pre-defined system routes (or logicals) will always exist and cannot be deleted. These are: stdin, stdout, werror, wclips, wdisplay, wdialog, wtrace, wagenda, wwarning. Knowledge Representation

The knowledge base of the system is represented as text file of facts written in CLIPS. Since the system is capable of “learning” new knowledge, editing and updating an existing text file may proved to be a laborious job. By using Database Management Software, raw text data entered by the expert can be controlled and refined

before transforming the data into a fact statement in a text file. Using the updated information on the database, new facts set can be generated replacing the old text. This will presents the knowledge base of the system which is accessed by the CLIPS engine during a case consultation. Figure 3 shows the semantic tree diagram of the knowledge representation.

NODES: N1: Where is the prawn pond located? N2: What indication of the problem is observable? N3: What is the general appearance of the affected prawn? N4: Which part of the affected part showed some abnormality? N5: Which prawn have the symptoms? N6: What is the appearance of the affected prawn? N7: What is the color of the prawn ? N8: Describe the general appearance of the prawn N9: What Indications can be found on the body visible on the prawn N10: Describe the deformation of the prawn RESPONSE: R1: Asia or Africa R2: Presence of Obvious symptoms R3: Damage R4: Mysis, postlarvae R5: Pond rear R6: Discolored R7: Green to yellow green R8: White cuticular spots R9: Dark Patch Visible R10: Exudation of dark red fluid R11: Black colored bark

Viral Disease

s

Bacterial Diseases

Fungal Disease

s

Viral Disease

s

Bacterial Diseases

Fungal Disease

s

R12: Deformed R13: Spiraled R14: Retarded growth R15: Show erraric swimming behavior

Figure 3. Knowledge Representation using semantic net

Fact and Fact List

Facts are one of the basic high-level forms for representing information in a CLIPS system. Each fact represents a piece of information which has been placed in the current list of facts, called the fact-list. Facts are the fundamental unit of data used by rules. Facts may be added to the fact-list using the assert command; removed from the fact-list using the retract command; modified using the modify command through explicit user interaction or as a CLIPS program executes. The number of facts in the fact-list and the amount of information that can be stored in a fact is limited only by the amount of memory in the computer. If a fact is asserted into the fact-list that exactly matches an already existing fact, the new assertion will be ignored.

The text file below is a sample CLIPS file that represent the knowledge base of the system. Assert and Retract instructions will be used depending on which facts is applicable (query (node 1)(question "Where is the prawn pond palm ")(ans-list Asia Africa Oceania Europe North_America Carribean South_America Central_America)) (query (node 2)(question "What indication of the problem is observable ?")(ans-list low_or_declining_yields_w/o_obvious_symptom presence_of_obvious_symptoms presence_of_pale –bluish gray _dark blue-dark coloration)) (query (node 3)(question "What is the general appearance of the affected prawn? ")(ans-list damaged abnormal-growth dying)) (query (node 4)(question "What part of the affected part showed some abnormality? ")(ans-list Mysis postlarvae juveniles adults)) (query (node 5)(question "Which prawn have the symptoms")(ans-list young_adult pond rear )) (query (node 6)(question "Describe the general appearance of the affected prawn?

")(ans-list discolored drying shrivelling spots-found)) (query (node 7)(question "What is the color of the prawn showing the abnormality")(ans-list yellow yellow_green)) (path (node 1)(answer Asia)(next 2)) (path (node 1)(answer Africa)(next 3)) (path(node2)(answer presence_of_obvious_symptoms) (next3)) (path (node 3)(answer damaged)(next 4)) (path (node 3)(answer no)(next results indeterminate)) (path (node 4)(answer mysis)(next 5)) (path (node 4)(answer tpostlarvae)(next 10)) (path (node 5)(answer all_colors s)(next 6)) (path (node 5)(answer young)(next 9)) (path (node 6)(answer discolored )(next 7)) (path (node 7)(answer yellow )(next 8)) (path (node 10)(answer dark_patches)(next 11)) (path (node 10)(answer deformed)(next 12)) (path (node 11)(answer exudation_of_dark_red_fluid) (next13)) (path (node 11)(answer black_colored_bark)(next 13)) (path (node 12)(answer spiralled)(next 14)) Agenda

The agenda is the list of all rules which have their conditions satisfied (and have not yet been executed). Each module has its own agenda. CLIPS module allow a set of constructs to be grouped together such that explicit control can be maintained over restricting the access of the constructs by other modules. This means that a set of rules can be partitioned in several modules allowing explicit control flow of execution depending on which module is focused.

The agenda acts similar to a stack (the top rule on the agenda is the first one to be executed). When a rule is newly activated, its placement on the agenda is based (in order) on the following factors: (a) Newly activated rules are placed above all rules of lower salience and below all rules of higher salience. (b) Among rules of equal salience, the current conflict resolution strategy is used to determine the placement among the other rules of equal salience. (c) If a rule is activated (along with several other rules) by the same assertion or retraction of a fact, and steps a and b are unable to specify an ordering, then the rule is arbitrarily

Viral Diseases

Monodon Baculovirus (MBV) Diseases

Figure 5a. Expert System’s forward-chaining method of inference.

(not randomly) ordered in relation to the other rules with which it was activated . Inference Engine

Generally, the inference engine acts like the brain of the ES. “It makes inferences by deciding which rules are satisfied by facts or objects, prioritizes the satisfied rules, and executes the rule with the highest priority”. Figure 4 shows how rules are fired in the inference engine.

Initially, the working area will be loaded with facts necessary to start the consultation process. These are essentially the general questions that are presented when activated rules in the agenda are executed. From this point, the client user will begin to respond to these questions thru the Front-end GUI. In any point, it is possible that the user selects one or more matching choices from the given list. This selection list will then be sent back to the CLIPS that will become part of the facts in the fact-list. CLIPS will then use them for pattern matching to find rules to activate. Rules found to be matched with given facts will cause new set of facts to be asserted in the working memory. This is basically one cycle of pattern matching which is made possible through the CLIPS control run method invocation. The cycle repeats until no more rules match the selection lists chosen by the client user. At this point, either the anomaly is found or the set of choices are inconclusive.

Figure 4. Inference Process Flow Diagram.

Penaus Monodon DMES, uses forward-chaining method of inference. It is characterized by searching or traversing a chain from a problem to its solution. Forward chaining is actually reasoning from existing facts to the conclusions that follow from the facts . Figures 5a and 5b show how the Expert

system draws conclusions from existing and asserted facts. For example, in diagnosing viral disease, the three facts (F1, F2, F3) supplied by the user will activate rule R1 upon which the user indicates that damages are manifested on the Mysis, postlarvae ,juveniles, adults (F4) which is immediately asserted, and thus becomes additional fact that will support further inference. Eventually, upon presentation of additional questions, the user provides additional facts (F5, F6, F7). These facts and F4 will together activate rule R3 that effectively indicate the presence of the viral disease.

Parvovirus Diseases

Segment of Prawn Diagx Inference Engine (Forward Chaining) Rules: R1,R2,R3…..

Facts F1 – Asia or Africa F2 – Presence of obvious symptoms F3 - Damage F4 - Mysis, postlarvae F5 - Pond rear F6 – Discolored F7 - Green to yellow green F8 - White cuticular spots F9 - Dark Patch Visible F10 - Exudation of dark red fluid F11 - Black colored bark F12 - Deformed F13 – Spiraled F14 - Retarded growth F15 - Show erraric swimming behavior Figure 5b. Expert System’s forward-chaining

method of inference.

Use MBV-free post larvae

Eggs washed with ozone-disinfected seawater yielded 68% survival of PL 7 compared with untreated ones yielding 31% survival

Reduce stress by good husbandry practices and good nutrition.

Destroy infected shrimp juveniles by burning or burying in pits lined with lime

Disinfect rearing facilities

Knowledge Acquisition

Figure 6 shows a snapshot of the knowledge acquisition screen. The knowledge acquisition process of the system begins with the expert creating a new pest or disease anomaly. All necessary information related to the anomaly is encoded into the graphical user interface such as management/recommendation, expert’s explanation of the symptoms appearing, causal organism and its life cycle and related

photos. These data can be edited, updated and save into the database. The next phase would be to identify the symptoms of the anomaly. At this point, the expert will be looking at the possible set of question and answer set that would satisfy the presence of such anomaly or disease. Looking at the existing questions/answers set in the database, the expert may add or use existing question and choice answer depending on the nature of the symptom of the disease identified. It is important to note that the expert should bear in mind that the progression of the consultation would be from general questions going to specific. Likewise, when there are least numbers of selections available to choose from, the user will have better judgment on which choice is appropriate for the specific case.

The knowledge acquisition module uses object oriented-approach in packaging all information before and after they are save or retrieve into or from the database. Once all identification of symptoms is complete, an external text is finally generated which is a CLIPS fact statement. The old existing knowledge file is therefore overwritten. Figure 6. Snapshot of Knowledge Acquisition

Screen.

Figure 7. Snapshot Screen for identify the symptoms of a particular anomal

What part of the affected shrimp showed abnormality? Which abdominal external part have the symptoms? What is the color of the shrimp? What is the general appearance of the shrimp? Does the color exhibit pale-bluish gray to dark blue-black coloration? Describe the viscosity of the affected area?

Which part were affected?

Project Management Project preparations and requirements

management are the activities by which the researcher-analyst had planned, delegated, directed, and controlled the progress in the development of the proposed system. This includes the software, hardware and people recommendation, which are shown below

Conclusion

The development of Prawn Diagx, Penaeous Monodon Diseases Management ES as an extensive and comprehensive tool has been hindered and limited both by time

constraints, the cost of proprietary software license, and the scarce availability of real experts in prawns health . Since expert themselves are the ones who will transform their own knowledge into a digital knowledge, they are consequently given the task to understand how the system adapt and learn the new knowledge they entered. The system, in order to behave closely like a true human expert, requires the latter to simulate its own “actions” in a real case consultation. These actions are the correct questions thrown to the client by which answers or responses are evaluated and assessed in return by the expert so as to come up with an accurate diagnosis. The simulated actions must then be “learned” by the system such that it can more or less behave like a real expert. Summarily, the experts will have to tediously and carefully compose the right questions and the choices available for selection, rather than plainly encode all the necessary information about a particular knowledge. Hence, the quality of the system response in a consultation case scenario would entirely depend on how precise the human expert “trained” the computer ES. Nevertheless the integration of very common diseases knowledge and the necessary simulated actions for these case anomalies, by the knowledge engineer would greatly help out the prawns health experts on how to deal with the “learning”behavior of the system. Recommendation

The Prawn Diagx, Penaeous Monodon Diseases Management ES project is meant to consolidate existing knowledge in Prawns health among various stack knowledge sources. The web-based implementation of this software could have been more beneficial to a large number of potential users. However, since the developer was hindered by the considerably large cost in acquiring the license for web-enabled version of CLIPS ActiveX control, the student edition was used instead, which is limited only for stand-alone and clientserver environment. Despite this limitation, the knowledge base that the system can accumulate can easily be adapted and take advantage of the objectoriented design features as well for a possible webbased implementation in the future.

Software Requirements of the Expert System

Development Tools Purpose

• On Software Development Tools � MS Visual Basic 6.0 � MS Access � Adobe Photoshop

MS Visual Basic is used to develop the Expert System, MS Access for the implementation of its databases, and For the graphics design of the interface of the proposed system, the Adobe Photoshop was used.

• On OS Platform: � Windows XP

This system software is used as an operating system in the development, implementation and maintenance of the Prawn Diagx:PMDMES.

• On Documentation and Presentation � MS Office 2003 � MS Visio

MS Office 2003 application software package is used for the preparation of the Prawn Diagx:PMDMES documentations such as the research paper, user manual, written communications, tables and slide presentation, etc. The MS Visio is used for the analysis and design of the system particularly on the DFD, system flowchart and some other diagramming tools.

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