A rule based system of indigenous knowledge for crop protectiion


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A rule based system of indigenous knowledge for crop protectiion

  1. 1. CHAPTER ONE1.0 BACKGROUND TO THE STUDYKnowledge and the way it is managed, according to Jashapara (2011), has been withhumankind since the beginning of time. Knowledge is an asset which does not depleteafter its use rather it grows through transfer or exchange. However, knowledge, if notclosely watched or kept may go extinct. Whether indigenous or modern, knowledgehas become the key asset to drive organizational survival and success and as such isan 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, andimpute meaning to their experiences (Studley, 1998).It should not be forgotten that indigenous knowledge formed part of humanity‟scommon heritage. Indigenous, Local and Traditional are terms that have been usedinterchangeably to describe the peculiarity of arts, beliefs, language, practice orknowledge (the list being in-exhaustive) to communities. Indigenous Knowledge(IK) has been defined by a number of authors, though different yet similar in theirideas of what indigenous knowledge is. The recurring terms in the various definitionsof indigenous knowledge are: natural resources, local, communities, experience andinnovation. Kolawole (2001) used the term local or indigenous knowledge (IK) todistinguish the knowledge developed by a given community from internationalknowledge systems or scientific knowledge. 1
  2. 2. The United Nations Environment Programme (UNEP) defined IK as the knowledgethat an indigenous local community accumulates over generations of living in aparticular environment. UNEP also identified a number of terms that are often usedinterchangeably to refer to the concept of indigenous knowledge. These termsinclude Traditional Knowledge (TK), Indigenous Technical Knowledge (ITK), LocalKnowledge (LK), and Indigenous Knowledge Systems (IKS). Indigenous knowledge(IK) is unique to every culture and society and it is embedded in communitypractices, institutions, relationships and rituals. It is considered a part of the localknowledge in that it has roots in particular communities and is situated within broadercultural traditions.Agricultural indigenous knowledge (AIK) refers to the knowledge through whichlocal communities go about their agricultural practice to ensure survival. Indigenousknowledge (IK), and AIK for that matter, is knowledge that has been in existencesince the existence of man. It is knowledge that evolved as man perceived the onlymeans for survival was to adapt to his environment, and by adapting there was needto identify which plants and animal were edible, how to cultivate the land aroundthem so as to reproduce these plants, how to protect the plants and animals fromdiseases 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 ofthe community are. 2
  3. 3. AIK has been observed to be a significant asset to communities in the area of decisionmaking towards sustainability. Adedipe et.al (2004) testified to the undeniableimportance of IK when they stated that this kind of knowledge , i.e. IK, are evidentlyrelated to global science traits of Conservation; Biodiversity maintenance; Plantphysiological; Plant psychological; and Entomological principles of crop protectionand Pest management. START (Global Change System for Analysis Research andTraining) in its flood risk analysis in the coastal communities in Nigeria noted thatsome communities in the Niger Delta have used indigenous knowledge to forecastfloods with some degree of accuracy.Africa is a continent rich in indigenous knowledge and Nigeria, by all indication, is amajor 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 mitein Poultry farming in the South to control method for pest and disease of cattle in theNorth, to mention a few. A lot of research has been carried out with the aim ofidentifying some of the indigenous agricultural practices in selected places in Nigeriabut this knowledge does not exist in any structured form. Based on the researcherssearch so far there is no such collection or large documentation of indigenousknowledge in Nigeria. 3
  4. 4. It has been shown that organizations that are able to harness knowledge grow strongerand are more competitive. This validates the more a saying about knowledge beingpower. In the economy today, corporate success can be achieved through anorganizations ability to acquire, codify, and transfer knowledge more effectively andwith greater speed than the competition. Jashapara (2011) considers knowledge as„actionable information‟. Unlike data and information, which are letters and numberswithout and with context, respectively, knowledge equips one with a greater ability topredict 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 drawnautomatically 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 isknowledge in the human brain; it is personal knowledge.Agricultural indigenous knowledge (AIK) can be classified as tacit knowledge. Thecore feature of AIK which qualifies it as tacit knowledge is that it is embedded in thefarmer‟s brain. Tacit knowledge is accumulated through study and experience. It is akind of knowledge that grows through the practice of trial and error and series ofsuccess and failure experience. These features are also peculiar with traditionalagricultural practices. 4
  5. 5. Knowledge-based systems otherwise known as Expert systems are computerprograms that use knowledge of the application domain to solve problems in thatdomain, obtaining essentially the same solutions that a person with experience in thesame domain would obtain. It is a system that tries to solve problems that willnormally 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 expertsystem it is important to engineer knowledge appropriately otherwise it would be acase of “garbage in, garbage out”. The same can be said of a medical doctor who hasnot immersed himself well enough in practice to diagnose a patient with malaria. Hemust be equipped with knowledge acquired through studies and experiences whichwill 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), tomineral resources (PROSPECTOR), to mention a few. Expert systems can be appliedto perform functions such as interpreting and identifying, predicting, diagnosing,designing, planning, monitoring, debugging and testing, instruction and training, andcontrolling. 5
  6. 6. 1.2 Statement of ProblemIn Africa there is limited documented literature in IK. This owes to the fact that IK istransmitted among generations orally or through observation. It is passed untogenerations through traditional socialization processes by elders of indigenouscommunities. These modes of learning are insufficient and unreliable in protectingIK from going into extinction. According to Msuya (2007), lack of written memoryon IK has also led to its marginalization. He also pointed out that the new generationfolks spend most of the time nowadays in formal education and as such are exposedthe more to western education and less to IK.Western education, which brings with it global knowledge, no doubt has advantagesbut global knowledge without local knowledge is inefficient. Every knowledgesystem has its origin and functions for which it came into existence. Rather than usea knowledge system as a benchmark for other knowledge systems, each knowledgesystem 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 thelanguage and logic of Western knowledge systems as each of them has its own logicand epistemological foundations.Banuri; Apffel-Marglin et al (1993) explained the differences between indigenousknowledge and western knowledge. One of the points they noted as the differencebetween the stated types of knowledge is based on a contextual ground. That is, 6
  7. 7. indigenous knowledge differs from western knowledge because indigenousknowledge is more deeply rooted in its environment. It is people‟s knowledge.Brokensha et al., 1980, as cited by Agrawal (2004), therefore emphasized that toignore peoples knowledge is almost to ensure failure in development.The agriculture profession is one that has been facing intensive marginalization sincethe discovery of oil in Nigeria. There is an increasing demand for white collar jobswhile the farm work is left for the poor rural farmers. Agriculture is not an area ofinterest to an average Nigerian graduate; even the so called graduates of agriculturalsciences abandon their farming tools for pens.Abebe et al as cited by Kolawole (2001) reviewed that farmers have quite asophisticated knowledge of agriculture based on insights from several generation andhe stressed the need to document and preserve the knowledge in situ and ex situ. Theemphasis, 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 expertsystem for AIK, there are accompanying advantages of protection, preservation, andimprovement (in its use) of the knowledge.1.3 Overall ObjectiveThe overall objective of the study is to develop a knowledge-based system which willmanage indigenous knowledge for crop protection. 7
  8. 8. 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 8
  9. 9. systems will enable agriculturalists take advantage of the benefits offered by the ageold practices.Warren and Rajasekaran (1993) noted that policy makers and agriculturaldevelopment planners are beginning to give attention to existing indigenousknowledge systems and decision-making processes. Indigenous knowledge if builtupon will enhance local development, enhance sustainability and capacity buildingsuch as this study provides. This is based on the fact that a clear understanding of acommunity‟s indigenous knowledge will provide the basis for basic communicationwith the farmers. Indigenous knowledge should form the foundations for agriculturaland food policy initiatives and technological interventions.Every phase of this present project is vital but a more significant phase without whichthis project would not be relevant is the knowledge acquisition phase. Knowledgeacquisition refers to the processes by which knowledge is acquired, either fromprimary or secondary sources. Primary and secondary sources were considered for thesupply of the knowledge required for this project but while some of them haveyielded the results many of these sources have not proven to provide sufficientknowledge for the purpose due to some constraints.The Faculty of Agriculture and Forestry at the University of Ibadan was selected as asource for data needed for this present project. On visitation to some of the 9
  10. 10. departments of the faculty (Agricultural Extension and Rural Developmentdepartment, Crop Protection and Environmental Biology department, and Agronomydepartment) the senior researchers whom the researcher interacted could not providesuch 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 providesome information. In their opinion such data can only be elicited from farmers, thus,they suggested that the researcher visits various farming communities in order toacquire such information in details.Based on the recommendation of the senior researchers, the researcher interviewedfarmers 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 theyspecialize in. It was observed that the farmers, being the elderly ones, were graduallyforgetting the indigenous methods. It took some of the farmers significant time toremember the names of pests, the names of leaves or other ingredients used to preparesolutions for treating infested crops. This owes to the fact that they have beenintroduced to the use of modern pesticides and herbicides which has reduced the usedof local pesticides.The researcher proceeded to some research institutes such as International Institutefor Tropical Agriculture (IITA), National Stored Products Research Institute, and 10
  11. 11. Nigerian Institute of Social and Economic Research (NISER). The researchers spokenwith said they do not have documented indigenous knowledge. As a matter of factthey strongly believe that such information should be available at the Faculty of theAgriculture and Forestry at University of Ibadan.The researcher also visited the indigenous knowledge library at Nigerian Institute ofSocial and Economic Research (NISER). The books, periodicals and journals whichwere consulted did not spell out the indigenous knowledge used for pest control anddisease management rather they emphasized the importance of indigenous knowledgefor development. A source at the National Centre for Genetic Resources andBiotechnology (NAGRAB) whom the researcher spoke with said based on hisinteraction with farmers during his duties as an extension officer he has no doubt thatagricultural indigenous knowledge is invaluable but to his knowledge there is nocollection whether in prints or in an electronic database to preserve these elements ofknowledge.This demonstrates the urgency of harvesting and documenting of all availableindigenous knowledge and the necessity of a much bigger project which could bewell organized and funded by national or international research institutes. 11
  12. 12. 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. 12
  13. 13. 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 13
  14. 14. CHAPTER TWO Literature Review2.0 Artificial IntelligenceThe name „artificial intelligence‟ dates back to 1955 when McCarthy, Minsky,Rochester, and Shannon at the Dartmouth conference made a proposal to studyartificial intelligence. The study, they said,” was to proceed on the basis of theconjecture that every aspect of learning or any other feature of intelligence can inprinciple 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 hispaper „Computing Machinery and Intelligence‟ when he asked the question “Canmachines think?” Turing in an attempt to prove the said intelligent behavior of amachine against that of a human being, proposed a test which he called the imitationgame. In the imitation game, he placed the machine and a human in a room and asecond human in another room. The second human is the interrogator in the game.The interrogator then communicates with the human counterpart and the machine inthe other room via a textual device. The interrogator through a question and answersession is expected to distinguish the computer from the human based on theresponses he gets for the questions he poses. If the interrogator is unable to tell thedifference, Turing argues, the computer can be assumed to be intelligent. 14
  15. 15. 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: 15
  16. 16.  “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. 16
  17. 17. Minsky (1991), on the contrary, points out a problem of attempts to unify theories ofintelligence. He assigns blame to lack of clarity in distinguishing between some broadaspects of what constitutes intelligence. Minsky offered the definition of intelligenceas “…the ability to solve hard problems”. But there arise a question such as “at whatpoint is a problem regarded as hard?” and “who decides which problem is hard?” Aproblem remains hard as long as one does not know how to go about solving it andthe moment it is solved it becomes easy.Schwartz (2006) therefore regards intelligent any organism or system that is able tomake decisions. Decisions are vital ingredients of survival and as long as there aregoals 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 individualorganism. According to Carne (1965), as cited by Schwartz (2006), the basic attributeof an intelligent organism is its capability to learn to perform various functions withina changing environment so as to survive and to prosper.Several definitions have also been offered for artificial intelligence. Artificialintelligence (AI) is the study of how to make computers do things which, at themoment, people are better (Rich, 1983). Artificial intelligence can be referred to as aninformation-processing program, the information-processing element which can belikened to human thinking. Simon (1966), according Frantz (2003), identified threeoperations that are peculiar to human thinking and information-processing programs. 17
  18. 18. He noted that human thinking and information-processing programs scan data forpatterns, store the patterns in memory, and then apply the patterns to make inferencesor extrapolations.After a thorough examination of some definitions of artificial intelligence Russell andNorvig (2003) observed a pattern along the definitions. The definitions he examineddescribed artificial intelligence along four main dimensions: thinking rationally,acting rationally, thinking humanly, and acting humanlySystems that think like humans Systems that think rationally“The exciting new effort makes “The study of mental facultiescomputers think…machines with through the use of computationalminds, in the full and literal sense.” models.” (Chamiak and McDermott,(Haugeland, 1985) 1985)“[the automation of] activities that we “The study of the computation thatassociate with human thinking, make it possible to perceive, reason,activities such as decision-making, and act.” (Winston, 1992)problem solving, learning..”(Bellman, 1978)Systems that act like humans Systems that act rationally“The art of creating machines that “Computational Intelligent is theperform functions that require study of the design of intelligentintelligence when performed by agents.” (Poole et al., 1998)people.” (Kurzweil, 1990) “AI …is concerned with intelligent“The study of how to make behavior in artifacts.” (Nilsson, 1998)computers do things at which, at themoment, people are better.” (Rich andKnight, 1991) 18
  19. 19. 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: 19
  20. 20.  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 20
  21. 21. 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 21
  22. 22.  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 22
  23. 23. 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) 23
  24. 24.  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. 24
  25. 25. 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. 25
  26. 26.  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. 26
  27. 27. Example Government Protected Frame Bird Frame Endangered species: robins, eagles Families: Robin Robin Frame Is a: Bird Is an: Endangered species Fly: Yes, Wings: yes Facet Instance of Location: pine tree Mini: instance frame Is a: robin Facet: Lives in: nest Location: Wang’s yardAn advantage of using the frame model is that information about an object is stored inone place, however when the object to be described has a lot of properties and manyrelationship need be reflected, it becomes complex.O-A-V triplet: the Object, Attribute, and Values method simply representsknowledge showing their characteristics and the measure of the attribute. Objects herecould either be physical or conceptual.Example Weight, Colour, 15kg, White, Poodle Dog Breed 27
  28. 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). colour Blue Fish lives Wate in Is a r Aquarium Jerry owns Phil 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 28
  29. 29. of reasoning is based on if it leads to a true conclusion in every situation where thepremises are true.The types of logic representation are Propositional logic, Predicate logic, First orderlogic, Temporal logic, and Fuzzy logic.Irrespective of the knowledge representation model an engineer selects for a projecthe/she should bear in mind the stages that must be followed, so as to enhance thedesired outcome. Poole (1999) developed a framework for representing knowledge. solve Problem Solution represent interpret informal compute formalRepresentation Output2.5 Agriculture and Indigenous KnowledgeThe agricultural sector has the potential to provide a jumping-off point for a nation‟sindustrial and economic development. This is owed to the multiplier effect whichsprings 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 29
  30. 30. emphasizes that the agricultural sector is the engine of growth in virtually alldeveloped economies.Of the79 million hectares of arable land which Nigeria has 32 million hectares arecultivated. Eighty percent of all farm produce in the country is produced mostly bysubsistence farmers, thus, leaving crop and livestock production below potentials.(Nwajiuba, 2012)Indigenous knowledge (IK) is accumulated store of cultural knowledge that isgenerated and transmitted by communities from one generation to another. Thisknowledge encompasses how to adapt to, make use of, and act upon physicalenvironments and the material resources in order to satisfy human wants and needs(Gbenda, 2010). Indigenous knowledge, according to Workineh et. al (2010), standsout. 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-culturalrelationships 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 aresynonymous to indigenous knowledge. These terms include indigenous knowledgesystems, indigenous technical knowledge, ethno-science, local science, traditionalscience, people‟s science, and village science. Irrespective of its size everycommunity has its own local knowledge, as the local knowledge is the keystone fordecision making to ensure harmonious survival with nature. 30
  31. 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. 31
  32. 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. 32
  33. 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) 33
  34. 34. Farmer‟s use of indigenous knowledge is in an unorganized manner, they search forsolutions for their local farming problems through indigenous knowledge. This kindtechnology is user-derived and time-tested. Senanayake (2006) noted a criticalstrength of the indigenous knowledge; its ability to see the interrelation of disciplines,and then integrate them meaningfully. This holistic perspective and the resultingsynergism show higher levels of developmental impact, adaptability andsustainability than Western modern knowledge.Bamigboye and Kuponiyi (2010) in their study of indigenous knowledge systems forrice production in Ekiti state identified some reasons why most of the farmerspreferred the knowledge. The farmers use indigenous knowledge for itsAffordability: For instance grass cutter is controlled by digging trench round thefarm and setting of traps, Environmental-friendliness: most of the techniques werealso considered environmentally friendly, if not they would have been long forgotten,Effectiveness, and Communicability: A large number of the farmers considered theknowledge easily communicable.2.6 Expert Systems Application in AgricultureProduction of agricultural products, whether crops or animals, has evolved into acomplex business requiring the accumulation and integration of knowledge(indigenous knowledge inclusive) and information from many diverse sources. In 34
  35. 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. 35
  36. 36. TOMATEX: An expert system for Tomatoes. The disorder diagnosis subsystemprovides information about the causes of user complain and it verifies userassumption, while the disorder treatment offers the user advice about the treatmentoperation of the infected plant.LIMEX: A multimedia expert system for Lime Production. 36
  37. 37. CHAPTER THREE System Analysis3.0 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 37
  38. 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 38
  39. 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. 39
  40. 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. Experts Knowledge Base and other Components Knowledge Developer’s Interface AcquisitionKnowledge KnowledgeEngineer Verifications and Users Validation Knowledge Representation Fig 3.1 An overview of the knowledge-based system 40
  41. 41. 3.4 Benefits of the Proposed SystemThe knowledge-based system will capture data which will be processed to produceresults. Expert systems in the agricultural environment will offer benefits which aresolutions to the aforementioned problems. The system will:1. Increase the probability, frequency, and consistency of making good decisions2. Help distribute human expertise3. Facilitate real-time, low-cost expert-level decisions by the non-expert4. Permit objectivity by weighing evidence without bias and without regard for theuser‟s personal and emotional reactions5. Free up the mind and time of the human expert to enable him or her to concentrateon more creative activities.3.5 Methods of Data CollectionThe data needed for this present project is indigenous knowledge used for pest anddisease control, symptoms of pest and disease attack, and storage methods. Theresearcher started out by gathering data from the farming community of Ijero Ekiti inEkiti state. At the end of the process the data gathered was not substantial enough todevelop a knowledge-based system. The researcher, faced with the financialchallenges and limited time, resorted to gather more data from secondary sources.Thus, data was collected from primary sources, through interview sessions, andsecondary source such as agricultural books, journals and publications. The datarequired for the proposed system includes: 41
  42. 42.  Name of Crops Name of Pests Name of diseases Ingredients used for treatments Methods of preparing treatment solutions (where necessary) and application 42
  43. 43. CHAPTER FOUR SYSTEM DESIGN4.0 INTRODUCTIONThis chapter contains a detailed description of the proposed system. The descriptionincludes objective of the system, the entities involved in the system, and theprocessing procedure used by the system.4.1 Objectives of the systemThe main objective of the alternative system is to provide expert services inindigenous pest and disease control and storage methods. Its sub objectives includeknowledge storage and knowledge sharing.4.2 Expert System at WorkThe functioning of the expert system requires a number of elements or subject. Thisbegins with the knowledge expert. The knowledge expert is responsible for thecoordination of other elements required to make it work.Secondly is the domain expert. Domain experts are those who possess the knowledgein the domain for which the system is built. In this present study farmers are thedomain experts. 43
  44. 44. The users of the expert system are farmers, extension officers, students and otherstakeholders 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 ofthe system. It also contains trace facility to trace the reasoning behavior in the systemThe knowledge base component captures the domain knowledge. The names of crops,pests, and diseases, descriptions of pest and disease control, descriptions of symptomsand storage methods which were elicited from farmers and gathered from books arecontained in this component of system.The inference engine consists of algorithms that process the knowledge which isrepresented in the knowledge base. 44
  45. 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. 45
  46. 46. Fig 4.1 Processes of Expert System Development Identify domain Outline the knowledge required Select Development Tool Select method for knowledge acquisition Acquire knowledge Recode and organize knowledge Design Testing and Validation Implementation Maintenance 46
  47. 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 47
  48. 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”. 48
  49. 49. A Prolog program basically consists of facts and rules. A fact is a prolog statementwhich 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 ofthe statement (in parenthesis) are the arguments. Note that facts must always be endedwith a period in prolog. The facts states that rice has pests such as case worm, stemborer, and grasscutter while wheat has pests such as aphids and mites. Lines 1 to 3and 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 tobe true. The head of the rule is the conclusion or goal to be achieved while the body isthe 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 forsolving problems that involve objects and relations between objects. 49
  50. 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. 50
  51. 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 formii. Output  Treatment display form Fig 4.2 LOGICAL VIEW CHART Crops Pest/Disease/Storage Analyze Display Box Refresh Close 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. 51
  52. 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 Storage Disease PestSymptoms Storage Symptoms andand Control methods Control Refresh 52
  53. 53. The user selects the crop for which he wants information about and further selects ofpest, disease or storage depending on what he wants to know about the crop heselected. 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 canclose the application at the end of the session.Fig 4.4 Opening pageThe opening page displays information about the system 53
  54. 54. Fig 4.5 Input and Output formModel-View-Controller Design PatternThe 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 ofinformation. The controller is the intermediary between the model and the view. Ittransmits signals sent to model from the view. 54
  55. 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. 55
  56. 56. SWI-Prolog supports the commonly found set of compiler warnings: syntax errors,singleton variables, predicate redefinition, system predicate redefinition andpredicates. Messages are processed by the hookable print message/2 predicate andwhere possible associated with a file and line number. The graphics system contains atool that exploits the message hooks to create a window with error messages andwarnings that can be selected to open the associated source location.5.4 SpecificationsBelow is a list of minimum hardware and software requirements for the developmentof the system:A Pentium IV 500MHZ processor100GB Hard disk512 MB RAM14 VGA MonitorUSB enhanced KeyboardUSB enhanced MouseSWI-Prolog Pseudocodes for the systemStart sessionTreatment Select Crop Select Pest OR Disease OR Storage If Pest is selected 56
  57. 57. Then display treatmentIf Disease is selected Then display treatmentIf Storage is selected Then display methodEnd of sessionDiagnosisIf Symptoms Load Pest OR DiseaseThen display treatmentEnd of session.5.6 Program testing and debuggingThe essence of testing and debugging the system is to ensure that it delivers fully theservice 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 knowledgeengineer. SWI-Prolog is the physical view which the knowledge engineer writes thecodes 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 thelogical view of the system which the users can interrogate the system. This is toensure that the logical and physical views are well bridged to provide the efficient useof the system. 57
  58. 58. CHAPTER SEVEN SUMMARY, CONCLUSON AND RECOMMENDATION7.0 SummaryThe focus of this study has been to make a computer an expert by providingindigenous knowledge on symptoms of pest and disease attack in crops, indigenoussolutions for pests and diseases in crops and indigenous storage methods. The studyalso sheds more light on the integration of information systems into the agriculturalsystem in order to preserve indigenous knowledge, and enhance knowledge sharing.The software used in building the system was SWI-Prolog version 6.1.2 andNetBeans.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 systemfor indigenous pest and disease control, and storage methods.7.1 ConclusionThere is popular saying that “when an old man dies in Africa, a whole library perisheswith him”. The common means of transferring indigenous knowledge has been theoral method and as soon as the person who has the knowledge dies the method oftransference is terminated. This emphasizes the urgent need for documentation ofindigenous knowledge.Thus, there is need to develop strong system to enhance the use of indigenousknowledge. It would accelerate the diffusion of indigenous knowledge. 58
  59. 59. 7.2 RecommendationsThe recommendations from this research are based on the researchers experience inthe course of the study.1. Thorough documentation and management of agricultural indigenousknowledge in NigeriaA very significant part of this study that requires further research is documentation ofagricultural indigenous knowledge. In the process of data acquisition it was found outthat indigenous knowledge is not documented. There are quite a number of literatureson indigenous knowledge but much effort has not being given to documenting theknowledge. The researcher encountered huge difficulties in the process of dataacquisition.2. Collaboration between research institutes and libraries of departments ofagriculture in the universities should be boosted.3. Literature reveals that inadequate funding is a major obstacle faced indocumenting and sharing of indigenous knowledge. It is suggested that governmentand private institutions should collaborate in funding.4. National agricultural indigenous knowledge resource centers should beestablished. This would serve as a home for agricultural indigenous knowledge whereresearchers can easily find knowledge required for research and development. 59
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  65. 65. APPENDIX IA knowledge-based system for indigenous pests and disease control, and storageSubmitted in partial fulfillment of the requirements for M.Inf. Sc Degree of the AfricaRegional 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 : ), 65
  66. 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). 66
  67. 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, 67
  68. 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). 68