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CHAPTER ONE

1.0 BACKGROUND TO THE STUDY

Knowledge and the way it is managed, according to Jashapara (2011), has been with

humankind since the beginning of time. Knowledge is an asset which does not deplete

after its use rather it grows through transfer or exchange. However, knowledge, if not

closely watched or kept may go extinct. Whether indigenous or modern, knowledge

has become the key asset to drive organizational survival and success and as such is

an asset which should not be allowed to suffer death due to ineffective management.

Knowledge is constituted by the ways in which people categorize, code, process, and

impute meaning to their experiences (Studley, 1998).

It should not be forgotten that indigenous knowledge formed part of humanity‟s

common heritage. Indigenous, Local and Traditional are terms that have been used

interchangeably to describe the peculiarity of arts, beliefs, language, practice or

knowledge (the list being in-exhaustive) to communities. Indigenous Knowledge

(IK) has been defined by a number of authors, though different yet similar in their

ideas of what indigenous knowledge is. The recurring terms in the various definitions

of indigenous knowledge are: natural resources, local, communities, experience and

innovation. Kolawole (2001) used the term local or indigenous knowledge (IK) to

distinguish the knowledge developed by a given community from international

knowledge systems or scientific knowledge.

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The United Nations Environment Programme (UNEP) defined IK as the knowledge

that an indigenous local community accumulates over generations of living in a

particular environment. UNEP also identified a number of terms that are often used

interchangeably to refer to the concept of indigenous knowledge. These terms

include Traditional Knowledge (TK), Indigenous Technical Knowledge (ITK), Local

Knowledge (LK), and Indigenous Knowledge Systems (IKS). Indigenous knowledge

(IK) is unique to every culture and society and it is embedded in community

practices, institutions, relationships and rituals. It is considered a part of the local

knowledge in that it has roots in particular communities and is situated within broader

cultural traditions.

Agricultural indigenous knowledge (AIK) refers to the knowledge through which

local communities go about their agricultural practice to ensure survival. Indigenous

knowledge (IK), and AIK for that matter, is knowledge that has been in existence

since the existence of man. It is knowledge that evolved as man perceived the only

means for survival was to adapt to his environment, and by adapting there was need

to identify which plants and animal were edible, how to cultivate the land around

them so as to reproduce these plants, how to protect the plants and animals from

diseases and so on. IK is not static. It evolved in response to changing ecological,

economic and social circumstances based on how creative and innovative members of

the community are.

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AIK has been observed to be a significant asset to communities in the area of decision

making towards sustainability. Adedipe et.al (2004) testified to the undeniable

importance of IK when they stated that this kind of knowledge , i.e. IK, are evidently

related to global science traits of Conservation; Biodiversity maintenance; Plant

physiological; Plant psychological; and Entomological principles of crop protection

and Pest management. START (Global Change System for Analysis Research and

Training) in its flood risk analysis in the coastal communities in Nigeria noted that

some communities in the Niger Delta have used indigenous knowledge to forecast

floods with some degree of accuracy.

Africa is a continent rich in indigenous knowledge and Nigeria, by all indication, is a

major contributor to this richness. Nigeria‟s richness in indigenous knowledge (IK)

can be attributed to the large number of (divers) ethnic groups in the country.

Relevantly is AIK. This varies from indigenous yam production and control of mite

in Poultry farming in the South to control method for pest and disease of cattle in the

North, to mention a few. A lot of research has been carried out with the aim of

identifying some of the indigenous agricultural practices in selected places in Nigeria

but this knowledge does not exist in any structured form. Based on the researchers

search so far there is no such collection or large documentation of indigenous

knowledge in Nigeria.

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It has been shown that organizations that are able to harness knowledge grow stronger

and are more competitive. This validates the more a saying about knowledge being

power. In the economy today, corporate success can be achieved through an

organizations ability to acquire, codify, and transfer knowledge more effectively and

with greater speed than the competition. Jashapara (2011) considers knowledge as

„actionable information‟. Unlike data and information, which are letters and numbers

without and with context, respectively, knowledge equips one with a greater ability to

predict future outcomes.

In a more definitive form, knowledge is information plus the rules for its application.

Knowledge is information associated with rules which allow inferences to be drawn

automatically so that the information can be employed for useful purposes.

Knowledge can be classified into implicit knowledge and explicit knowledge.

Explicit knowledge is the knowledge that is documented while tacit knowledge is

knowledge in the human brain; it is personal knowledge.

Agricultural indigenous knowledge (AIK) can be classified as tacit knowledge. The

core feature of AIK which qualifies it as tacit knowledge is that it is embedded in the

farmer‟s brain. Tacit knowledge is accumulated through study and experience. It is a

kind of knowledge that grows through the practice of trial and error and series of

success and failure experience. These features are also peculiar with traditional

agricultural practices.

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Knowledge-based systems otherwise known as Expert systems are computer

programs that use knowledge of the application domain to solve problems in that

domain, obtaining essentially the same solutions that a person with experience in the

same domain would obtain. It is a system that tries to solve problems that will

normally require human experts.

An expert system is designed in a manner in which it imitates human experts‟

thinking process to proffer solution to problems. In order to get the most of an expert

system it is important to engineer knowledge appropriately otherwise it would be a

case of “garbage in, garbage out”. The same can be said of a medical doctor who has

not immersed himself well enough in practice to diagnose a patient with malaria. He

must be equipped with knowledge acquired through studies and experiences which

will enable him deliver the right medical services for the right ailments. Thus,

designing an expert system requires well pruned processes of Knowledge acquisition,

Knowledge representation and Knowledge validation.

Expert systems have been noted to assist in a number of fields ranging from medicals

(MYCIN), automobile (ALTREX), building and construction (PREDICTE), to

mineral resources (PROSPECTOR), to mention a few. Expert systems can be applied

to perform functions such as interpreting and identifying, predicting, diagnosing,

designing, planning, monitoring, debugging and testing, instruction and training, and

controlling.

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1.2 Statement of Problem

In Africa there is limited documented literature in IK. This owes to the fact that IK is

transmitted among generations orally or through observation. It is passed unto

generations through traditional socialization processes by elders of indigenous

communities. These modes of learning are insufficient and unreliable in protecting

IK from going into extinction. According to Msuya (2007), lack of written memory

on IK has also led to its marginalization. He also pointed out that the new generation

folks spend most of the time nowadays in formal education and as such are exposed

the more to western education and less to IK.

Western education, which brings with it global knowledge, no doubt has advantages

but global knowledge without local knowledge is inefficient. Every knowledge

system has its origin and functions for which it came into existence. Rather than use

a knowledge system as a benchmark for other knowledge systems, each knowledge

system should be recognized as distinct and unique. Shiva, (2000) as cited by Gall

(2009), opined that the various knowledge systems should not be reduced to the

language and logic of Western knowledge systems as each of them has its own logic

and epistemological foundations.

Banuri; Apffel-Marglin et al (1993) explained the differences between indigenous

knowledge and western knowledge. One of the points they noted as the difference

between the stated types of knowledge is based on a contextual ground. That is,

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indigenous knowledge differs from western knowledge because indigenous

knowledge is more deeply rooted in its environment. It is people‟s knowledge.

Brokensha et al., 1980, as cited by Agrawal (2004), therefore emphasized that to

ignore people's knowledge is almost to ensure failure in development.

The agriculture profession is one that has been facing intensive marginalization since

the discovery of oil in Nigeria. There is an increasing demand for white collar jobs

while the farm work is left for the poor rural farmers. Agriculture is not an area of

interest to an average Nigerian graduate; even the so called graduates of agricultural

sciences abandon their farming tools for pens.

Abebe et al as cited by Kolawole (2001) reviewed that farmers have quite a

sophisticated knowledge of agriculture based on insights from several generation and

he stressed the need to document and preserve the knowledge in situ and ex situ. The

emphasis, of this present project however, is on agricultural indigenous knowledge

(AIK) and how its use can be aided by an expert system. In designing an expert

system for AIK, there are accompanying advantages of protection, preservation, and

improvement (in its use) of the knowledge.

1.3 Overall Objective

The overall objective of the study is to develop a knowledge-based system which will

manage indigenous knowledge for crop protection.

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1.3.1 Specific objectives

The objectives of this study are to:

To elicit domain knowledge on local crop pests and disease;

To elicit domain knowledge on local pest and disease control ;

To elicit domain knowledge on local storage methods;

To develop a knowledge-based system that can reason based on the indigenous

knowledge provided and proffer solutions to problems in the domain.

1.4 Justification of the study

In the history of humans, people have sustained themselves by using the natural

resources around them in a largely suitable manner (Akegbejo-Samsons, 2009). Many

of these survival practices particularly those that are unique to indigenous people

around the world are disappearing. This therefore heavily threatens the existence of

indigenous knowledge.

Indigenous knowledge in agriculture is only one out of the numerous categories of

indigenous knowledge that suffer the threat of extinction. As the greater part of

agricultural produce in Nigeria comes from rural farmers there is a need to pay

attention to the farmers‟ local knowledge system. Hansen et al (1987) as cited by

Bamigboye and Kuponiyi (2010) stated that researchers have observed that these

indigenous agricultural practices are cost-effective and it poses less production risks

such as environmental degradation. An understanding of indigenous knowledge

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systems will enable agriculturalists take advantage of the benefits offered by the age

old practices.

Warren and Rajasekaran (1993) noted that policy makers and agricultural

development planners are beginning to give attention to existing indigenous

knowledge systems and decision-making processes. Indigenous knowledge if built

upon will enhance local development, enhance sustainability and capacity building

such as this study provides. This is based on the fact that a clear understanding of a

community‟s indigenous knowledge will provide the basis for basic communication

with the farmers. Indigenous knowledge should form the foundations for agricultural

and food policy initiatives and technological interventions.

Every phase of this present project is vital but a more significant phase without which

this project would not be relevant is the knowledge acquisition phase. Knowledge

acquisition refers to the processes by which knowledge is acquired, either from

primary or secondary sources. Primary and secondary sources were considered for the

supply of the knowledge required for this project but while some of them have

yielded the results many of these sources have not proven to provide sufficient

knowledge for the purpose due to some constraints.

The Faculty of Agriculture and Forestry at the University of Ibadan was selected as a

source for data needed for this present project. On visitation to some of the

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departments of the faculty (Agricultural Extension and Rural Development

department, Crop Protection and Environmental Biology department, and Agronomy

department) the senior researchers whom the researcher interacted could not provide

such data. The senior researchers stated clearly that there is no such documentation

(of indigenous knowledge used for pest control and disease management in crops).

Some of the senior researchers offered textbooks which they thought could provide

some information. In their opinion such data can only be elicited from farmers, thus,

they suggested that the researcher visits various farming communities in order to

acquire such information in details.

Based on the recommendation of the senior researchers, the researcher interviewed

farmers in Ijero Ekiti. It was a process which consumed time and financial resources.

Some of the farmers were able to provide some information based on the crops they

specialize in. It was observed that the farmers, being the elderly ones, were gradually

forgetting the indigenous methods. It took some of the farmers significant time to

remember the names of pests, the names of leaves or other ingredients used to prepare

solutions for treating infested crops. This owes to the fact that they have been

introduced to the use of modern pesticides and herbicides which has reduced the used

of local pesticides.

The researcher proceeded to some research institutes such as International Institute

for Tropical Agriculture (IITA), National Stored Products Research Institute, and

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Nigerian Institute of Social and Economic Research (NISER). The researchers spoken

with said they do not have documented indigenous knowledge. As a matter of fact

they strongly believe that such information should be available at the Faculty of the

Agriculture and Forestry at University of Ibadan.

The researcher also visited the indigenous knowledge library at Nigerian Institute of

Social and Economic Research (NISER). The books, periodicals and journals which

were consulted did not spell out the indigenous knowledge used for pest control and

disease management rather they emphasized the importance of indigenous knowledge

for development. A source at the National Centre for Genetic Resources and

Biotechnology (NAGRAB) whom the researcher spoke with said based on his

interaction with farmers during his duties as an extension officer he has no doubt that

agricultural indigenous knowledge is invaluable but to his knowledge there is no

collection whether in prints or in an electronic database to preserve these elements of

knowledge.

This demonstrates the urgency of harvesting and documenting of all available

indigenous knowledge and the necessity of a much bigger project which could be

well organized and funded by national or international research institutes.

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Due to time, financial and logistic constraints the project study cannot assume the

responsibility of the proposed bigger project. However, it presents a template and a

knowledge-based platform upon which the proposed project can be based.

A knowledge-based system in AIK will reveal the step by step processes that rural

farmers apply in their farming processes. It is, thus, capable of providing this

information to agricultural researchers and other practitioners in a format easily

accessible for use and modification where, and if, necessary.

A knowledge-based system serves beyond documentation but also provides solutions

to problems. Some of the benefits a knowledge-based system for indigenous

agricultural practices will offer are highlighted below.

Preserve and protect agricultural indigenous practices ;

Provide researchers and scientists with a problem-solving platform which will assist

in research;

Provide a platform for diffusing and integrating agricultural indigenous knowledge

with scientific knowledge to improve agricultural production;

Serve students in their academic work;

Serve as a tool for extension workers in their field work.

It could also be useful to young farmers who cannot afford expensive pesticides.

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1.5 Scope of the Study

The scope of the study is to build an expert system shell which can describe

indigenous methods of crop storage and also be used to identify pests and diseases in

selected crops and proffer indigenous methods of controlling the pests and diseases.

The knowledge in the knowledgebase is limited to indigenous/traditional knowledge

used in agriculture. The expert system provides an interactive user interface through

which users can interrogate the system.

1.6 DEFINITION OF TERMS

Artificial intelligence: it is the study of ways in which computers can perform tasks

which people are better at.

Disease: an abnormal condition of an organism which interrupts the normal growth

and function of the organism.

Expert system: an expert system is a computer system designed to solve problems in

specific narrow domain in the same way human experts will do.

Indigenous knowledge: indigenous knowledge is local or traditional knowledge

acquired by communities over the years through interactions with their environment

in a bid to survive.

Pest: a destructive insect or other animal that attacks plants, crops or animals.

Prolog: Programming in Logic

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CHAPTER TWO

Literature Review

2.0 Artificial Intelligence

The name „artificial intelligence‟ dates back to 1955 when McCarthy, Minsky,

Rochester, and Shannon at the Dartmouth conference made a proposal to study

artificial intelligence. The study, they said,” was to proceed on the basis of the

conjecture that every aspect of learning or any other feature of intelligence can in

principle be so precisely described that a machine can be made to simulate “ (Rich,

2003).

Alan Turing, however, had in 1950 implied the name artificial intelligence in his

paper „Computing Machinery and Intelligence‟ when he asked the question “Can

machines think?” Turing in an attempt to prove the said intelligent behavior of a

machine against that of a human being, proposed a test which he called the imitation

game. In the imitation game, he placed the machine and a human in a room and a

second human in another room. The second human is the interrogator in the game.

The interrogator then communicates with the human counterpart and the machine in

the other room via a textual device. The interrogator through a question and answer

session is expected to distinguish the computer from the human based on the

responses he gets for the questions he poses. If the interrogator is unable to tell the

difference, Turing argues, the computer can be assumed to be intelligent.

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Artifice outlined three important features of Turing‟s test. The features are:

1. The test attempts to give an objective notion of intelligence, i.e., the behavior of a

known intelligent being in response to a particular set of questions.

2. It prevents us from being sidetracked by such confusing and currently unanswerable

questions as whether or not the computer uses the appropriate internal processes or

whether or not the machine is actually conscious of its actions.

3. It eliminates any bias in favor of living organisms by forcing the interrogator to focus

solely on the content of the answers to questions. (UVETEJO, 2007)

Russel and Norvig noted, however, that a computer must possess some capabilities to

enable it pass the test. The computer must be able:

To communicate in natural/human language (natural language processing);

Store what it knows or hears (knowledge representation);

Use the information it stores to provide answers, make inferences and also to draw

conclusions(automated reasoning);

To adapt to new circumstances and to detect patterns and to further extent the

application of such patterns (machine learning).

The question which evolves at this point is, what is intelligence? There has been a

long history of debate as to what intelligence is, and despite the decades of research

there is still no single acceptable or standard definition of intelligence. Several

definitions of intelligence have been recorded. Legg and Hutter (2006) noted that

there are obvious strong similarities between the numerous proposed definitions of

intelligence. Some definitions of intelligence given are as follows:

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“A person possesses intelligence insofar as he has learned, or can learn to adjust

himself to his environment”. S. S. Colvin

“…. the ability to plan and structure one‟s behavior with an end in view” J. P. Das

“…in its lowest terms intelligence is present where the individual animal, or human

being, is aware, however dimly, of the relevance of his behavior to an objective.

Many definitions of what is indefinable have been attempted by psychologists, of

which the least unsatisfactory are:

1. The capacity to meet novel situations, or to learn to do so, by new adaptive

responses and,

2. The ability to perform tests or tasks involving the grasping of relationships, the

degree of intelligence being proportional to the complexity, or the abstractness, or

both of the relationship” J. Drever

“…adjustment or adaption of the individual to his total environment, or limited

aspects thereof …the capacity to reorganize one‟s behavior patterns so as to act more

effectively and more appropriately in novel situations …the ability to learn …the

extent to which a person is educable …the ability to carry out on abstract thinking

…the effective use of concepts and symbols in dealing with a problem to be

solved…” W. Freeman

Amtar (1976), remarked that the major problem with the several viewpoint is that

intelligence is generally regarded as a uniquely human quality. He stated further that

we humans are yet to understand ourselves, our capabilities, or our origins of thought.

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Minsky (1991), on the contrary, points out a problem of attempts to unify theories of

intelligence. He assigns blame to lack of clarity in distinguishing between some broad

aspects of what constitutes intelligence. Minsky offered the definition of intelligence

as “…the ability to solve hard problems”. But there arise a question such as “at what

point is a problem regarded as hard?” and “who decides which problem is hard?” A

problem remains hard as long as one does not know how to go about solving it and

the moment it is solved it becomes easy.

Schwartz (2006) therefore regards intelligent any organism or system that is able to

make decisions. Decisions are vital ingredients of survival and as long as there are

goals to be achieved decisions must be made in order to achieve them. In his opinion,

any proposed definition of intelligence should not rely on comparisons to individual

organism. According to Carne (1965), as cited by Schwartz (2006), the basic attribute

of an intelligent organism is its capability to learn to perform various functions within

a changing environment so as to survive and to prosper.

Several definitions have also been offered for artificial intelligence. Artificial

intelligence (AI) is the study of how to make computers do things which, at the

moment, people are better (Rich, 1983). Artificial intelligence can be referred to as an

information-processing program, the information-processing element which can be

likened to human thinking. Simon (1966), according Frantz (2003), identified three

operations that are peculiar to human thinking and information-processing programs.

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He noted that human thinking and information-processing programs scan data for

patterns, store the patterns in memory, and then apply the patterns to make inferences

or extrapolations.

After a thorough examination of some definitions of artificial intelligence Russell and

Norvig (2003) observed a pattern along the definitions. The definitions he examined

described artificial intelligence along four main dimensions: thinking rationally,

acting rationally, thinking humanly, and acting humanly

Systems that think like humans

“The exciting new effort makes

computers think…machines with

minds, in the full and literal sense.”

(Haugeland, 1985)

“[the automation of] activities that we

associate with human thinking,

activities such as decision-making,

problem solving, learning..”

(Bellman, 1978)

Systems that think rationally

“The study of mental faculties

through the use of computational

models.” (Chamiak and McDermott,

1985)

“The study of the computation that

make it possible to perceive, reason,

and act.” (Winston, 1992)

Systems that act like humans

“The art of creating machines that

perform functions that require

intelligence when performed by

people.” (Kurzweil, 1990)

“The study of how to make

computers do things at which, at the

moment, people are better.” (Rich and

Knight, 1991)

Systems that act rationally

“Computational Intelligent is the

study of the design of intelligent

agents.” (Poole et al., 1998)

“AI …is concerned with intelligent

behavior in artifacts.” (Nilsson, 1998)

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Artificial intelligence has roots in a number of disciplines. These disciplines include

Philosophy, Logic/Mathematics, Computation, Psychology/Cognitive Science,

Biology/Neuroscience, and Evolution.

2.2 Artificial General Intelligence and Narrow Intelligence

The original notion behind artificial intelligence was to create machines that simulate

human reasoning in solving problems, that is, a machine that thinks. This attracted the

use of the terms “Artificial Intelligence” and “Artificial General Intelligence (AGI)”

interchangeably. Attempts were made to develop machines that could solve variety of

complex problems in different domains. Some of the AGI systems that were

developed are:

General Problem Solver

Fifth Generation Computer Systems

DARPA‟s Strategic Computing

Wang (2007) recorded that despite the ambitiousness of the AGI projects, they all

failed. Due to these failures, artificial intelligence aim was redirected to solving

domain-specific problems and providing special purpose solutions. Thus, “Narrow

Intelligence”.

Narrow artificial intelligent systems are systems that demonstrate intelligence in

specialized domains. Artificial intelligence has been applied in the following areas:

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Game: Game Playing is one of the oldest and well-studied domains of artificial

intelligence. a basic feature of game in artificial intelligence is its mixture of different

approaches of representing in intelligence (Wexler, 2002).

Natural Language Processing: This area of artificial intelligence tries to take on

one of the inherent capabilities of human beings – Understanding language. In natural

language processing machines are made in such a way that they can understand

natural language. A machine that understands natural language carries out the

following steps consecutively: speech recognition, syntactic analysis, semantic

analysis and pragmatic analysis.

Computer Vision: This is an area of artificial intelligence that deals with the

perception of objects through the artificial eyes of an agent, such as a camera

Machine Learning: Machine Learning, as the name implies, involves teaching

machine to complete tasks. It emphasizes automatic methods, that is, the goal of

machine learning is to device learning algorithms that do the learning automatically

without human intervention or assistance. It is an area of artificial intelligence which

intersects broadly with other fields such as statistics, mathematics, physics, and so on.

Examples of machine learning problems are Face detection, Spam filtering, and Topic

spotting.

Neural Networks: a neural network is a massively parallel distributed processor that

has a natural propensity for storing experiential knowledge and making it available

for use. It is a machine that is designed to model the way in which the brain performs

a particular task or function of interest; the neural network is usually implemented

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using electronic components or simulated in software on a digital computer (Hajek,

2005).

Expert Systems: Expert systems are computer programs that are designed to

replicate knowledge and skills of human experts in specific narrow domains.

2.3 Expert Systems

Expert systems are computer software which are developed to provide solutions to

problems in narrow domains. The solutions provided by the expert system should be

the same, if not better, as would be provided by the domain-expert if he was to solve

such problem. Expert system, though takes roots in cognitive science, has been a

significant aspect of artificial intelligence research and quite a number of systems

have been developed. Expert systems, according to Anjaneyulu (1998), encode

human expertise in limited domains.

Armstrong (2002) defines expert system as a program that emulates the interaction a

user might have with a human expert to solve a problem. Expert systems do not make

significant use of algorithms rather they use rules of thumb (heuristics), as an expert

normally will do.

Expert systems are beneficial in a number of ways.

Expert systems, unlike human experts, are readily available when needed.

Human experts may get tired or forget things but experts systems do not exhibit such

frailties

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Expert systems can be used to train experts and pass knowledge to non-experts

Due to the various distractions in the environment human experts may be inconsistent

in carry out their task. An expert system is consistent.

Expert systems are usually the result of the pooling of resources of various experts.

Expert systems produce results faster than humans

Expert systems, in the long run, are cheap.

The process of designing expert systems is called Knowledge Engineering. The

knowledge engineering process consists of sub-processes which are knowledge

acquisition and knowledge representation.

2.4 Knowledge Acquisition and Representation

Knowledge acquisition is a process which involves gathering of knowledge form

books, journals, databases and most importantly experts in a domain of expertise.

The knowledge engineer irrespective of whether he has a deep knowledge of the

domain or not is charged with the responsibility of gathering the knowledge required

to build a knowledge system. This process is one which needs the elicitors‟ keen

attention so as to ensure that knowledge is captured in the sense that the expert means

it to be. Collecting knowledge from secondary sources may not be as challenging as

collecting from primary sources, i.e. the knowledge experts. The major challenges a

knowledge engineer might encounter in this process is either the unwillingness of the

experts to share the knowledge or the lack of awareness. Knowledge engineers

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should beforehand equip themselves with the knowledge eliciting skills and general

domain awareness before engaging with the experts, thus, Knowledge Elicitation.

Knowledge elicitation, according to Regoczie and Hirst (1992) as cited by Cooke

(1999), is a sub process of knowledge acquisition which is further a sub-process of

knowledge engineering. Shadbolt and Murton (1995) refer to knowledge elicitation as

a subtask of gathering information from experts. Knowledge elicitation asks the

question, how do we get experts to say exactly what they do and why?

Shadbolt and Burton (1995) expatiated on the different methods of eliciting

knowledge from experts. Some of the methods of knowledge elicitation are:

Structured interview: This is an organized and planned discussion format for

knowledge elicitation. The knowledge engineer must have planned the whole session.

The advantage of using structured interview is that it provides structured transcripts

that are easier to analyse. Shadbolt and Borton (1989)

Protocol Analysis (PA): In PA the knowledge engineer makes video or audio records

of the expert. Protocols are made from the records and the knowledge engineer

further extracts meaningful rules from the protocols. Shadbolt and Borton (1989) The

knowledge engineer could record the expert while he (expert) solves a problem; the

experts in the process will give commentary concurrently describing what he is doing

as he solves the problem. This is called On-line PA. When the expert comments

retrospectively on the problem solving session the process is called Off-line PA.

Shadbolt and Borton (2006)

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Concept sorting: The concept sorting method is used to reveal how an expert relates

different concepts in his domain of expertise. The expert is presented with cards on

which is written different concepts. The cards are shuffled and the expert is told sort

the cards into piles he finds appropriate.

Laddered grids: This process requires that the expert and knowledge engineer

construct a graphical representation of the domain terms of the relations between

domain elements.

The choice of which method to use depends on the expert from whom the knowledge

will be elicited and the type of knowledge to be elicited. The knowledge engineer is

allowed to use more than one method in the knowledge eliciting process.

As earlier stated knowledge representation is one of the processes that a knowledge

engineer must pay keen attention to in designing an expert system. The time and

effort that a knowledge engineer put into eliciting knowledge from experts will not be

fully credited if the knowledge engineer does not represent the knowledge acquired in

such a way that it enables effective automated reasoning. In an attempt to proffer

solution to real life problems, an expert first observes the problem and then

internalizes it in a language that will assist his reasoning about the problem.

Reasoning is a thought process based on what the expert has been able to internalize

and from which he/she draws inferences or makes conclusion. The computer

program is also expected to work in this same way but is deficient in the area of

observing and representing the real life problem in its own language. The knowledge

engineer is thus faced with the responsibility of representing knowledge in the

language the computer is designed to understand.

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The knowledge acquisition phase is succeeded by the knowledge representation

phase. Knowledge representation is the way knowledge is encoded. Copping (2004),

identifies knowledge representation as a very core of Artificial Intelligence (AI).

Symbols, whether character strings or numbers, are ways AI Programmers represent

and manipulate knowledge on computers in order to generate information.

Information described in this contexts refers to the advice generated by an expert

system based on the knowledge which has been well represented and intelligently

manipulated. This differs from information generated from data as in the case of

statistical information. Data is raw information which ordinarily might not make

much sense until it is processed into information. Data is also represented by symbols

but it should not be confused with knowledge. Data is the lowest stage or state of

describing or representing reality; at that stage or state a person cannot make meaning

of the representation because it is without a context. Knowledge on the other hand has

an understanding pattern.

Knowledge, if represented appropriately, should enable fast and accurate access to

knowledge and an understanding of the content. A good knowledge representation

model should have the following capabilities:

Representational adequacy: this is the ability of the system to represent the

knowledge in the domain it is being used.

Inferential efficiency: is the system‟s ability to manipulate the structures that have

been represented within it in order to produce new knowledge inferred from the old

ones. It is the system‟s ability to reason with the knowledge provided to produce

new knowledge.

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Inferential adequacy: is the system‟s ability to incorporate additional knowledge

structure that can be can be used to direct the focus of the inference mechanisms in

the most promising direction.

Acquisitional efficiency: is the ability of the system to acquire knowledge using

automatic methods wherever possible rather than rely on human intervention.

Literature however revealed that, so far, no single representational formalism

optimizes all the capacities.

Knowledge can be represented through different mechanisms/models namely: Rules,

Frames, O-A-V triplet (Objects, Attributes, and Values), Semantic net, and Logic.

Each of these models is briefly explained below.

Rules: this model of representation usually takes the “IF, THEN” form. Knowledge is

represented in condition-action pair, (Haq). In the rule-based system, according to

Giarratano (2004), the inference engine determines which rule antecedents are

satisfied by the facts. The rules are there to assist the system draw conclusions based

on the facts provided.

Example:

IF X THEN Y; X being the antecedent and Y the consequence. Say, IF infected

joints THEN arthritis.

Frames: this model consists of a set of nodes, each representing objects, connected

by relations. The knowledge in the frame is divided into slots to which values are

assigned.

27

Example

An advantage of using the frame model is that information about an object is stored in

one place, however when the object to be described has a lot of properties and many

relationship need be reflected, it becomes complex.

O-A-V triplet: the Object, Attribute, and Values method simply represents

knowledge showing their characteristics and the measure of the attribute. Objects here

could either be physical or conceptual.

Example

Bird Frame

Families: Robin

Government Protected Frame

Endangered species: robins, eagles

Robin Frame

Is a: Bird

Is an: Endangered species

Fly: Yes, Wings: yes

Mini: instance frame

Is a: robin

Lives in: nest

Facet

Location: pine tree

Facet:

Location: Wang’s yard

Instance of

Dog Weight, Colour,

Breed 15kg, White, Poodle

28

Semantic Net: this system represents knowledge using graphs. The graphs are made

up of nodes (which represents objects), and edges/links (which represents the

relationship between the objects).

Coppin (2004), noted that as much as semantic nets provide a very intuitive way to

represent knowledge about objects and existing relationships. Semantic nets being

graphical representation can get cumbersome when the graphs are too many. It also

cannot represent relationship between three or more objects.

Logic: this is concerned with reasoning and validity of arguments, Cooping (2004). It

is concerned about the validity of a statement rather than its truthfulness. Take for

instance the following statements:

Fishes live on land

Jerry is a Fish

Therefore, Jerry lives on land.

The concluding statement is logical based on the previous statements. The reasoning

process determines the conclusion based on the premises; thus, the validity of a piece

Fish

Jerry

Phil

Blue

Wate

r Aquarium

colour

Is a

owns

in

lives

29

of reasoning is based on if it leads to a true conclusion in every situation where the

premises are true.

The types of logic representation are Propositional logic, Predicate logic, First order

logic, Temporal logic, and Fuzzy logic.

Irrespective of the knowledge representation model an engineer selects for a project

he/she should bear in mind the stages that must be followed, so as to enhance the

desired outcome. Poole (1999) developed a framework for representing knowledge.

2.5 Agriculture and Indigenous Knowledge

The agricultural sector has the potential to provide a jumping-off point for a nation‟s

industrial and economic development. This is owed to the multiplier effect which

springs from the sector‟s activities. A vibrant agricultural sector, according to Ogen

(2007), would enable a country to feed its growing population, generate employment,

earn foreign exchange and provide raw materials for industries. He further

Problem

Representation

Solution

Output

represent

compute

interpret informal

formal

solve

30

emphasizes that the agricultural sector is the engine of growth in virtually all

developed economies.

Of the79 million hectares of arable land which Nigeria has 32 million hectares are

cultivated. Eighty percent of all farm produce in the country is produced mostly by

subsistence farmers, thus, leaving crop and livestock production below potentials.

(Nwajiuba, 2012)

Indigenous knowledge (IK) is accumulated store of cultural knowledge that is

generated and transmitted by communities from one generation to another. This

knowledge encompasses how to adapt to, make use of, and act upon physical

environments and the material resources in order to satisfy human wants and needs

(Gbenda, 2010). Indigenous knowledge, according to Workineh et. al (2010), stands

out. This is because it is an integral part of culture and unique to every given society,

and it was developed outside the formal educational system. Due to inter-cultural

relationships indigenous knowledge in some communities has been modified.

Quite a number of terminologies have been used to refer to indigenous knowledge.

Atte (1986), as cited by Williams and Muchena (2000), listed some terms which are

synonymous to indigenous knowledge. These terms include indigenous knowledge

systems, indigenous technical knowledge, ethno-science, local science, traditional

science, people‟s science, and village science. Irrespective of its size every

community has its own local knowledge, as the local knowledge is the keystone for

decision making to ensure harmonious survival with nature.

31

There are increasing numbers of literatures on indigenous knowledge in recent times.

This is not to say that indigenous knowledge is a new area of research.

Anthropologists have been in the “business” of studying and documenting people‟s

culture, practices, beliefs, and customs for years (Schneider 2000). They have

traditionally been academic loners, spending long periods, ranging from months to

several years, for field work and data analysis. Schneider highlighted three new areas

of interest indigenous knowledge as:

The interest in indigenous technologies

The involvement of non-anthropologists and development professionals in recording

indigenous knowledge

The speed with which it is now being accomplished.

This shows that indigenous knowledge is gradually gaining the long expected

significance in the modern society. Agrawal (2004) noted that earlier theorists saw

indigenous knowledge and institutions as obstacles to development.

Williams and Muchena (2000) identified the unique, dynamic and creative features of

indigenous knowledge. It is unique in that it is generated in response to the natural

and human conditions of a particular environment and context. It is dynamic and

creative in that experimentation and evaluation are continually stimulated by both

adaptation requirements and external influences. Elen and Harris (1996), according to

Senanayake (2006), provided more characteristics of indigenous knowledge. These

comprehensive and conclusive characteristics are as follows.

Indigenous knowledge is local. It originates from a particular place based on several

experiences of people living in that particular place.

32

Indigenous knowledge is transmitted orally, or through imitation and demonstration

Indigenous knowledge is the consequence of practical engagement in everyday life

and is constantly reinforced by experience and trial and error.

Indigenous knowledge is empirical rather than theoretical knowledge.

Repetition is a vital characteristic of tradition even when new knowledge is added.

This is because repetition aids retention and reinforces ideas.

Tradition could be considered as „a fluid and transforming agent with no real end‟

when applied to knowledge and its central concept is negotiation. Indigenous

knowledge is not static as it is often represented; it is rather constantly changing as

well as reproduced; discovered as well as lost.

Indigenous knowledge is mainly shared to a much greater degree than other forms of

knowledge. Its distribution is, however, still segmentary and socially clustered.

Although indigenous knowledge may be focused on particular individuals and

knowledge may be focused on particular individual and may be focused on particular

individuals and may achieve a degree of coherence in rituals and other symbolic

constructs, its distribution is always fragmentary. It generally does not exist in its

totality in any one place or individual. It is developed in the practices and interactions

in which people themselves engage.

Indigenous knowledge is characteristically situated within broader cultural traditions;

separating the technical from the non-technical, the rational from the non-rational is

problematic.

Indigenous knowledge is an invaluable asset for sustainable development. It offers

new models for development that are both ecologically and socially sound.

33

(Senanayake, 2006). A World Bank report noted the relevance of indigenous

knowledge on three levels for development processes.

Firstly, indigenous knowledge is important for the local communities in which those

who bear such live and produce.

Development agents such as NGO‟s, government, donors, local leaders, private sector

initiatives also need to recognize, value and appreciate the knowledge as they interact

with the local communities. A thorough understanding of a community‟s indigenous

knowledge will result in a successful incorporation of it into development projects.

Thirdly, indigenous knowledge forms part of the global knowledge. Indigenous

knowledge in itself is valuable and relevant. It can be preserved, transferred, or

adopted and adapted elsewhere.

Agricultural indigenous knowledge is local and traditional knowledge used by

farmers in farming, dairy and poultry production, raising livestock, land evaluation,

and soil fertility to mention a few. It is the means by which farmers adapt to their

environment so as to achieve food, income, and livelihood in the midst of changing

agricultural environment.

Farmers, over the years, have gained knowledge of crops and animals around them.

This has given them knowledge about uses and usefulness of specific plant and

animals. These farmers have been, traditionally, the managers of crop germs plasm

and its diversity for generations, through the testing, preservation and exchange of

seeds through informal networks. Their special knowledge of the values and diverse

uses of plants for food security, health and nutrition is very vital. (Upreti and Upretu,

2000)

34

Farmer‟s use of indigenous knowledge is in an unorganized manner, they search for

solutions for their local farming problems through indigenous knowledge. This kind

technology is user-derived and time-tested. Senanayake (2006) noted a critical

strength of the indigenous knowledge; its ability to see the interrelation of disciplines,

and then integrate them meaningfully. This holistic perspective and the resulting

synergism show higher levels of developmental impact, adaptability and

sustainability than Western modern knowledge.

Bamigboye and Kuponiyi (2010) in their study of indigenous knowledge systems for

rice production in Ekiti state identified some reasons why most of the farmers

preferred the knowledge. The farmers use indigenous knowledge for its

Affordability: For instance grass cutter is controlled by digging trench round the

farm and setting of traps, Environmental-friendliness: most of the techniques were

also considered environmentally friendly, if not they would have been long forgotten,

Effectiveness, and Communicability: A large number of the farmers considered the

knowledge easily communicable.

2.6 Expert Systems Application in Agriculture

Production of agricultural products, whether crops or animals, has evolved into a

complex business requiring the accumulation and integration of knowledge

(indigenous knowledge inclusive) and information from many diverse sources. In

35

order to survive intense competition, the modern farmer often relies on agricultural

specialists and advisors to provide information for decision making. Unfortunately,

agricultural specialist assistance is not always available when the farmer needs it. In

order to alleviate this problem, expert systems were identified as a powerful tool with

extensive potential in agriculture.

Prasad and Babu (2006) highlighted three features of an agricultural expert system.

It simulates human reasoning about a problem domain, rather than simulating the

domain itself

It performs reasoning over representations of human knowledge

It solves problem by heuristics or approximate methods

Early expert systems in agriculture include:

POMME: This is a system which is used for apple orchid management. It offers

advices to farmers on the appropriate time to spray their apples and what to spray in

order to avoid infestation. Additionally, it also provides advice regarding treatment of

winter injuries, drought control and multiple insect problems.

CUPTEX: An expert system for Cucumber Crop Production. It has subsystems on

Disorder diagnosis, Disorder Treatment, Irrigation Scheduling, Fertilization

Scheduling, and Plant care.

CITEX: An expert system for Orange Production. It has subsystems on farm

assessment, Irrigation Scheduling, Fertilizer Scheduling, Disorder diagnosis, and

Disorder treatment.

36

TOMATEX: An expert system for Tomatoes. The disorder diagnosis subsystem

provides information about the causes of user complain and it verifies user

assumption, while the disorder treatment offers the user advice about the treatment

operation of the infected plant.

LIMEX: A multimedia expert system for Lime Production.

37

CHAPTER THREE

System Analysis

3.0 Introduction

System analysis describes in detail the existing system, thereby identifying the

deficiencies of the system as justification for the need of an improved system.

Additionally, this section will describe the alternative system briefly with emphasis

on how it will overcome the problems posed by the existing system. A thorough

analysis of the alternative system will be given in the succeeding chapter. The

methods used for data collection will also be described.

3.1 Existing System

Crop protection is a very significant aspect of agriculture which draws on the

strategies to prevent and control problems posed by pests, diseases, and weed in crop

production. Pests, diseases and weed may attack crops in either similar or dissimilar

ways, their effects on crops, however, are constant. The damages caused by pests,

diseases, and weed results in reduction of yields and low quality of yields, which

consequently reduces the profit margins for commercial farmers.

An invaluable asset in crop management is indigenous agricultural knowledge; it has

served as a means of survival through several generations. Sadly to say, indigenous

agricultural knowledge is fast disappearing. The documentation and distribution of

indigenous knowledge, according to Abioye et al. (2011), remain a big challenge

38

confronting librarians and other information professionals, particularly in Africa

where cultural practices are prevalent.

In the course of this present project it was found out there are no indigenous

agricultural knowledge databases and inquiry systems which could aid knowledge

sharing, distribution and preservation. There are documentations of general

agricultural topics but there is no documentation of agricultural indigenous

knowledge, whether in print or electronically. The importance of agricultural

indigenous knowledge is widely acknowledged by researchers but little has been done

to document it.

Rural farmers who possess this knowledge merely share with their colleagues orally

when the need for it arises. Some institutions such as Organic Farmers Association

also partake in sharing some indigenous knowledge among interested farmers, but

how much of sharing and preservation can be done by such institutions considering

the fact that these institutions have roots in rural areas and they have limited

resources, in terms of Information and Communication Technologies (ICT). The

existing system is highly limited, if it is left unattended to the available indigenous

agricultural knowledge may become extinct.

3.2 Problems of the Existing System

The problems associated with the existing system include:

Limited knowledge sharing: it is important to know that no matter how relevant

knowledge is to the society they cannot benefit from it if it is not well distributed to

39

members of the society. In the existing system knowledge cannot be easily shared

among farmers, researchers and other stakeholders.

Knowledge loss: farmers (in this sense, experts) who possess this knowledge are most

elderly people who are fast approaching their dying days. The existing system does

not have a documentation sub-system for the knowledge, thus posing a greater risk of

knowledge extinction.

Considering the physical state of the experts (elderly farmers) much cannot be done

in the existing system.

3.3 The Proposed system

The proposed alternative system is a knowledge-based system, also called an expert

system. A knowledge-based system is a computer program designed to solve

problems, in specific narrow domains, in the manner in which human expert would.

A knowledge based system has features that will enable it store, share, and process

knowledge.

Expert system in the agricultural environment is necessitated by the limitations

associated with conventional human decision-making processes. These limitations

include:

1. Human expertise is very scarce. Farmers who practice indigenous agriculture are

not as many as in the early years of farming. Most of them have taken to modern

farming.

40

2. Humans get tired from physical or mental workload and this may cause them to

forget crucial details of solutions.

4. Humans are inconsistent in their day-to-day decisions.

5. Humans have limited working memory.

6. Humans are unable to retain large amounts of data in memory and may be slow in

recalling information stored in memory.

7. Humans die.

The system is designed to capture data such as the name of pests and diseases,

treatment for the pests and diseases, preparation of treatment solution (where

necessary) and storage methods.

Fig 3.1 An overview of the knowledge-based system

Knowledge

Acquisition

Knowledge

Verifications and

Validation

Knowledge

Representation

Kn

ow

led

ge B

ase

an

d o

the

r C

om

po

ne

nts

Experts

Users

D

evel

op

er’s

Inte

rfac

e

Knowledge

Engineer

41

3.4 Benefits of the Proposed System

The knowledge-based system will capture data which will be processed to produce

results. Expert systems in the agricultural environment will offer benefits which are

solutions to the aforementioned problems. The system will:

1. Increase the probability, frequency, and consistency of making good decisions

2. Help distribute human expertise

3. Facilitate real-time, low-cost expert-level decisions by the non-expert

4. Permit objectivity by weighing evidence without bias and without regard for the

user‟s personal and emotional reactions

5. Free up the mind and time of the human expert to enable him or her to concentrate

on more creative activities.

3.5 Methods of Data Collection

The data needed for this present project is indigenous knowledge used for pest and

disease control, symptoms of pest and disease attack, and storage methods. The

researcher started out by gathering data from the farming community of Ijero Ekiti in

Ekiti state. At the end of the process the data gathered was not substantial enough to

develop a knowledge-based system. The researcher, faced with the financial

challenges and limited time, resorted to gather more data from secondary sources.

Thus, data was collected from primary sources, through interview sessions, and

secondary source such as agricultural books, journals and publications. The data

required for the proposed system includes:

42

Name of Crops

Name of Pests

Name of diseases

Ingredients used for treatments

Methods of preparing treatment solutions (where necessary) and application

43

CHAPTER FOUR

SYSTEM DESIGN

4.0 INTRODUCTION

This chapter contains a detailed description of the proposed system. The description

includes objective of the system, the entities involved in the system, and the

processing procedure used by the system.

4.1 Objectives of the system

The main objective of the alternative system is to provide expert services in

indigenous pest and disease control and storage methods. Its sub objectives include

knowledge storage and knowledge sharing.

4.2 Expert System at Work

The functioning of the expert system requires a number of elements or subject. This

begins with the knowledge expert. The knowledge expert is responsible for the

coordination of other elements required to make it work.

Secondly is the domain expert. Domain experts are those who possess the knowledge

in the domain for which the system is built. In this present study farmers are the

domain experts.

44

The users of the expert system are farmers, extension officers, students and other

stakeholders in the agriculture industry.

The user interface is the front end through which the user will interrogate the system.

The expert system has an explanation facility which documents the reasoning steps of

the system. It also contains trace facility to trace the reasoning behavior in the system

The knowledge base component captures the domain knowledge. The names of crops,

pests, and diseases, descriptions of pest and disease control, descriptions of symptoms

and storage methods which were elicited from farmers and gathered from books are

contained in this component of system.

The inference engine consists of algorithms that process the knowledge which is

represented in the knowledge base.

45

4.3 Stages of Developing an Expert System

There are some basic steps to be followed in development of an expert system.

1. Identify a problem in a domain. The development of an expert system must be

justified by a real problem that needs to be solved. This system seeks to enhance the

use of agricultural indigenous knowledge in crop protection. Additionally, it would

create a platform to protect the indigenous knowledge.

2. Outline and describe the knowledge required for the system.

3. Select development tools. These are software and hardware components required for

the system development.

4. There are a number of methods that can be used to elicit knowledge. The method(s) to

be used can be chosen based its suitability to the type of knowledge and convenience

of the domain expert.

5. The knowledge engineer acquires the knowledge with the chosen method.

6. After the knowledge has been elicited the knowledge engineer analyzes. He organizes

the knowledge into the format which will suit the knowledge representation method.

7. The design is done; it entails write of source codes. The logical and physical views

are also linked.

8. When the design has been completed the system should be tested to ensure that it is

working. By testing bugs can be detected and fixed.

9. Trainings of users and necessary structures should be put in place to make the system

ready for use.

10. In order to ensure that the functioning of the system is not interrupted, constant

checks should be carried out. Expert systems primarily need to be updated.

46

Fig 4.1 Processes of Expert System Development

Identify domain

Outline the

knowledge required

Select method for

knowledge acquisition

Acquire knowledge

Recode and

organize

knowledge

Select

Development Tool

Design

Testing and

Validation

Implementation

Maintenance

47

4.4 COMPUTING ENVIRONMENT

This comprises description of the hardware and software component required in the

development of the system.

4.4.1 Software:

1. The design is based on SWI-PROLOG 6.1.2, thus, the need for a personal computer

The system was developed with SWI-Prolog (6.1.2 version) because it offers some

good facilities.

It has a good environment: This includes „Do What I Mean‟ (DWIM), automatic

completion of atom names, history mechanism and a tracer that operates on single

key-strokes. Interfaces to some standard editors are provided (and can be extended),

as well as a facility to maintain programs.

It has very fast compiler: Even very large applications can be loaded in seconds on

most machines. If this is not enough, there is a Quick Load Format that is slightly

more compact and loading is almost always I/O bound.

Transparent compiled code: SWI-Prolog compiled code can be treated just as

interpreted code: you can list it, trace it, etc. This implies you do not have to decide

beforehand whether a module should be loaded for debugging or not. Also,

performance is much better than the performance of most interpreters.

Profiling: SWI-Prolog offers tools for performance analysis, which can be very useful

to optimize programs.

Flexibility: SWI-Prolog can easily be integrated with C, supporting non-determinism

in Prolog calling C as well as C calling Prolog. It can also be embedded in external

48

programs. System predicates can be redefined locally to provide compatibility with

other Prolog systems.

Integration with XPCE: SWI-Prolog offers a tight integration to the Object Oriented

Package for User Interface Development, called XPCE. XPCE allows you to

implement graphical user interfaces that are source-code compatible over Unix/X11,

Windows and Mac OS X using X11.

Prolog was designed by Alain Colmerauer and Robert Kowalski, and is used in

artificial intelligence (AI) and computational linguistics. Prolog stands for

“Programming in Logic”. It helps to create logic models that describe the world in

which a problem exists. It is a declarative and procedural language.

Prolog is declarative language in that facts about the problem to be solved are stated

along with its rules. The inference engine uses the stated facts and rules to reason out

solutions to problems. Its procedural feature stems from the process by which it

accomplishes a task.

According to Merrit (2002), there are three main features which influence the

expressiveness of Prolog. These features are the rule-based programming, built-in

pattern matching, and backtracking execution. The rule-based programming allows

the program code be written in a more declarative form while the built-in provides for

the flow of control in the program. Backtracking is search process used by prolog.

Whenever a non-deterministic choice is made the program is made to go back and

choose the next alternative branch. This continues until it there is a match but if after

all the nodes have been search and there is no match it displays an output “no” or

“false”.

49

A Prolog program basically consists of facts and rules. A fact is a prolog statement

which consists of an identifier (mostly referred to as Predicates) followed by an n-

tuple of constants (also called Arguments). For example:

Line 1 pest(rice,case_worm).

Line 2 pest(rice,stem_borer).

Line 3 pest(rice,grasscutter).

Line 4 pest(wheat,aphids).

Line 5 pest(wheat,mites).

Line 6 pest(Crop,Pest):-

Crop(Crop,Pest).

Lines 1 to 5 are facts. In the facts stated “pest” is the predicate while the other parts of

the statement (in parenthesis) are the arguments. Note that facts must always be ended

with a period in prolog. The facts states that rice has pests such as case worm, stem

borer, and grasscutter while wheat has pests such as aphids and mites. Lines 1 to 3

and lines 4 to 5 can be restated in the form of lists.

Line 7 pest(rice,[„case_worm‟,‟stem_borer‟,‟grasscutter‟]).

Line 8 pest(wheat,[„aphids‟,‟mites‟]).

Line 6 is a rule which consists of a head and a body separated by “:-“. The symbol “:-

“ means “if”. The head of the rule is true if all predicates in the body can be proved to

be true. The head of the rule is the conclusion or goal to be achieved while the body is

the condition(s) which must be fulfilled in order for the goal to be achieved.

Prolog was chosen for the development of this system because it is well suited for

solving problems that involve objects and relations between objects.

50

2. NetBeans IDE NetBeans is an integrated development environment (IDE) for

developing primarily with Java, but also with other languages, in particular PHP,

C/C++, and HTML5. It is also an application platform framework for Java desktop

applications and others.

The NetBeans IDE is written in Java and can run on Windows, OS X, Linux, Solaris

and other platforms supporting a compatible JVM.

The NetBeans Platform allows applications to be developed from a set of modular

software components called modules. Applications based on the NetBeans Platform

(including the NetBeans IDE itself) can be extended by third party developers.

Java Program Execution

The Java byte-code compiler translates a Java source file into machine independent

byte code. The byte code for each publicly visible class is placed in a separate file, so

that the Java runtime system can easily find it. If the program instantiates an object of

class A, for example, the class loader searches the directories listed in your

CLASSPATH environment variable for a file called A.class that contains the class

definition and byte code for class A. There is no link phase for Java programs; all

linking is done dynamically at runtime.

51

4.5 Information Flow

Below is the breakdown of information flow within the system:

i. Input

Crop selection form

Pest/Disease/Storage/Symptoms selection form

ii. Output

Treatment display form

Fig 4.2 LOGICAL VIEW CHART

The logical view above highlights the components of the front end of the knowledge-

based system. The view consists combo box such as that from which a choice of crop

is made, radio buttons which can be checked to make a choice of pest, or disease or

storage, list area which contains a list of pests or diseases (this is dependent or the

choice made with the radio buttons), text area which displays results, and buttons

which enable processing such as analyze, refresh and close. The view also contains a

progress bar and a form label. The menu bar has file and help labels.

Crops Pest/Disease/Storage Analyze Display Box

Refresh

Close

52

Fig 4.3 PROGRAM FLOW CHART

A program flowchart describes what takes place in a program; it displays specific

operations and decisions, their sequences within the program run or phase.

Start

Select

Crop

Select either

Pest or Disease

or Storage

Pest Storage Disease

Symptoms

and Control

Storage

methods

Symptoms and

Control

Refresh

53

The user selects the crop for which he wants information about and further selects of

pest, disease or storage depending on what he wants to know about the crop he

selected. He sends the information into the system by clicking on the analyze button.

The system processes the information supplied and returns answers into the text area.

The user can refresh the system if he wants to interrogate the system again and he can

close the application at the end of the session.

Fig 4.4 Opening page

The opening page displays information about the system

54

Fig 4.5 Input and Output form

Model-View-Controller Design Pattern

The application design is based on the model-view-controller (MVC) design pattern.

This design consists of three parts: the model, the view and the control.

The model contains data information. It usually responds to request for information.

The view is the platform for interrogation; it manages requests and display of

information. The controller is the intermediary between the model and the view. It

transmits signals sent to model from the view.

55

CHAPTER FIVE

SYSTEM DEVELOPMENT

5.1Introduction

This chapter describes the implementation of the system. It describes the actual

process involved in programming, compilation, specifications, installations, and

testing. Program development necessitates the transformation of system design

specifications into functional applications accessible to users.

5.2 Programming

The following was done in programming the task:

Inputting and Editing: The acquired knowledge was systematically entered into the

Edit Screen of SWI-Prolog. If in other sessions there is need to make corrections the

Edit key is used.

Testing and Debugging: In order to confirm that the system is working there is need

to test and remove bugs which could hinder its efficient performance. An added

advantage to the use of SWI-Prolog is that Prolog systems offer the possibility for

interactive edit and reload of a program even while the program is running.

5.3 Compilation

Fast compilation is very important during the interactive development of large

applications.

56

SWI-Prolog supports the commonly found set of compiler warnings: syntax errors,

singleton variables, predicate redefinition, system predicate redefinition and

predicates. Messages are processed by the hookable print message/2 predicate and

where possible associated with a file and line number. The graphics system contains a

tool that exploits the message hooks to create a window with error messages and

warnings that can be selected to open the associated source location.

5.4 Specifications

Below is a list of minimum hardware and software requirements for the development

of the system:

A Pentium IV 500MHZ processor

100GB Hard disk

512 MB RAM

14 VGA Monitor

USB enhanced Keyboard

USB enhanced Mouse

SWI-Prolog 6.2.1

5.5 Pseudocodes for the system

Start session

Treatment

Select Crop

Select Pest OR Disease OR Storage

If Pest is selected

57

Then display treatment

If Disease is selected

Then display treatment

If Storage is selected

Then display method

End of session

Diagnosis

If Symptoms

Load Pest OR Disease

Then display treatment

End of session.

5.6 Program testing and debugging

The essence of testing and debugging the system is to ensure that it delivers fully the

service it is designed for. The knowledge-based system was tested at two stages.

The first test was carried out on SWI-Prolog and NetBeans by the knowledge

engineer. SWI-Prolog is the physical view which the knowledge engineer writes the

codes necessary for the functioning of the system.

The second test was carried out on the front end of the system by the users. It is the

logical view of the system which the users can interrogate the system. This is to

ensure that the logical and physical views are well bridged to provide the efficient use

of the system.

58

CHAPTER SEVEN

SUMMARY, CONCLUSON AND RECOMMENDATION

7.0 Summary

The focus of this study has been to make a computer an expert by providing

indigenous knowledge on symptoms of pest and disease attack in crops, indigenous

solutions for pests and diseases in crops and indigenous storage methods. The study

also sheds more light on the integration of information systems into the agricultural

system in order to preserve indigenous knowledge, and enhance knowledge sharing.

The software used in building the system was SWI-Prolog version 6.1.2 and

NetBeans.

The knowledge base was developed majorly from secondary resources such as books,

journals, and publications. Knowledge was also elicited from farmers.

A review of the existing system made clear the need for a knowledge-based system

for indigenous pest and disease control, and storage methods.

7.1 Conclusion

There is popular saying that “when an old man dies in Africa, a whole library perishes

with him”. The common means of transferring indigenous knowledge has been the

oral method and as soon as the person who has the knowledge dies the method of

transference is terminated. This emphasizes the urgent need for documentation of

indigenous knowledge.

Thus, there is need to develop strong system to enhance the use of indigenous

knowledge. It would accelerate the diffusion of indigenous knowledge.

59

7.2 Recommendations

The recommendations from this research are based on the researchers experience in

the course of the study.

1. Thorough documentation and management of agricultural indigenous

knowledge in Nigeria

A very significant part of this study that requires further research is documentation of

agricultural indigenous knowledge. In the process of data acquisition it was found out

that indigenous knowledge is not documented. There are quite a number of literatures

on indigenous knowledge but much effort has not being given to documenting the

knowledge. The researcher encountered huge difficulties in the process of data

acquisition.

2. Collaboration between research institutes and libraries of departments of

agriculture in the universities should be boosted.

3. Literature reveals that inadequate funding is a major obstacle faced in

documenting and sharing of indigenous knowledge. It is suggested that government

and private institutions should collaborate in funding.

4. National agricultural indigenous knowledge resource centers should be

established. This would serve as a home for agricultural indigenous knowledge where

researchers can easily find knowledge required for research and development.

60

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65

APPENDIX I

A knowledge-based system for indigenous pests and disease control, and storage

Submitted in partial fulfillment of the requirements for M.Inf. Sc Degree of the Africa

Regional Centre for Information Science, University of Ibadan, Ibadan.

optionsview(disease,Crop):-

diseaseview(Crop),

write('Enter corresponding number to disease : '),

read(DiseaseNumber),

nth1(DiseaseNumber,Diseases,Disease),

disease(Crop,Diseases),

member(Disease,Diseases),

symptom(Crop,Disease,Symptom),

write('The symptoms of '),write(Disease),write(' are '),nl,

writelist(Symptom),

diseasecontrol(Crop,Disease,Control),

write(Disease),write(' can be controlled in the following ways : '),nl,

writelist(Control),nl,nl,

main.

optionsview(pest,Crop):-

pestview(Crop),

write('Enter corresponding number to pest : '),

66

read(PestNumber),

nth1(PestNumber,Pests,Pest),

pest(Crop,Pests),

member(Pest,Pests),

symptom(Crop,Pest,Symptom),

write('The symptoms of '),write(Pest),write(' are '),nl,

writelist(Symptom),

pestcontrol(Crop,Pest,Control),

write(Pest),write(' can be controlled in the following ways : '),nl,

writelist(Control),nl,

main.

optionsview(storage,Crop):-

storage(Crop,Storage),

writelist(Storage),nl,

main.

indexedmenu([],_).

indexedmenu([H|T],Index1):-

write('Type '),write(Index1),write(' for '),write(H),nl,

succ(Index1,Index2),

indexedmenu(T,Index2).

67

diseaseview(Crop):-

disease(Crop,Diseases),

indexedmenu(Diseases,1).

pestview(Crop):-

pest(Crop,Pests),

indexedmenu(Pests,1).

cropview:-

crops(Crops),

indexedmenu(Crops,1).

optionsview(Info_options):-

info_options(Info_options),

indexedmenu(Info_options,1).

dcontrolview(Crop,Disease,Control):-

diseasecontrol(Crop,Disease,Control),

indexedmenu(Control,1).

writelist([]):-

nl.

writelist([H|T]):-

write(H),nl,

68

writelist(T).

main:-

write('Main Menu'),nl,

cropview,

write('Enter corresponding number to desired crop: '),

read(CropNumber),nl,

crops(Crops),

nth1(CropNumber,Crops,Crop),

info_options(Options),

indexedmenu(Options,1),

write('Enter corresponding number to desired option: '),

read(OptionNumber),

nth1(OptionNumber,Options,Option),

optionsview(Option,Crop).