• Journal of Biosystems Engineering
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• v.18 no.2
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• pp.133-143
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• 1993

In developing an Expert System(ES), there are two ways. One is to develop an ES using AI(artificial Intelligence) languages and another using ES-development tools. CLIPS is an ES-development tool and has a powerful inference engine in it. Using the tool like CLIPS, knowledge engineer can concentrate on constructing a knowledge base without wasting time in developing an inference engine. However, CLIPS is lack of user-friendly interfaces for both knowledge enginners and users. Because CLIPS was developed in USA, it can not afford to use Korean language. Therefore, several user-friendly interfaces including hmenu, htille, hpcxdisplay were develpoed and added to CLIPS. CLIPS with the interfaces is called HCLIPS(Hangul CLIPS) in this paper. HCLIPS provides a new I/O device to be utilized for expert systems in Korean. HCLIPS can be efficiently used for developing expert systems in agriculture and consulting farmers interactively who are not familiar with computer programming and ES itself.

• Journal of Biosystems Engineering
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• v.23 no.5
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• pp.499-506
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• 1998

An expert system to diagnose cucumber's diseases and nutritional disorders(ESCD) was developed using HCLIPS. The knowledge base was made through interviews with farmers who had cultivated cucumbers for years and experts who had a specialty of cucumber's diseases and disorders. The photographs of cucumber's diseases and nutritional disorders were used to assist users to comprehend queries of the ESCD. The ESCD was able to diagnose 11 fungal and bacterial diseases and 10 nutritional disorders, which could be frequently found in the fields and greenhouses. Diagnosis accuracy of the developed ESCD was validated by the farmers and the expert who joined the development.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.632-637
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• 1996

An expert system for cucumber disease and nutritional disorders(ESCD) was developed using HCLIPS in which several interface programs were added to CLIPS developed in NASA. The knowledges for constructing knowledge base was obtained from interviews with farmers, agrochemical vendors and employees of agrochemical company. The ESCD is able to diagnose eleven diseases and twelve nutritional disorders which farmers, agrochemical vendors and employees of agrochemical company. The ESCD is able to diagnose eleven diseases and twelve nutritional disorders which could frequently found in fields and greenhouses. Validation of ESCD by twenty -four experts proved a possibility of its practical use for diagnosing cucumber diseases and nutritional disorders in the future.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.1
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• pp.95-102
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• 1994

Since parameters influencing the indoor environment of livestock building interrelate so complicatedly, it is of great difficulty to identify the exact cause of environmental problems in a livestock building. Therefore, the approaches for the problem solving based on experience not numerical calculation will be helpful to the management of livestock building This study was attempt to develop the decision supporting system to diagnose environmen- tal problems in a livestock building based on an expert system and a neural network. HClips$^3$), attaching the Hangeul user interface to Clips which is known as a powerful shell for develop- ing expert system, was used. The multilayer perceptron consisting of 4 layers including back propagation learning algorithm was adpoted, which was rapidly converged within the allowable range at 50,000 learning sweeps. The expert system and neural network seemed to work well for this specific application, providing proper suggestions for some environmental problems: particularly, the neural net- work trained by an environmental problem and its corresponding answer with certainty factor, produced the same results as those by expert system.

• Journal of Bio-Environment Control
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• v.3 no.1
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• pp.28-35
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• 1994

An expert system, Watermelon Disease Expert System (WDES), was developed in this study using HCLIPS which allows input and output in Korean. WDES could diagnose 8 diseases and 1 physiological disorder frequently occurring on watermelons grown in greenhouses. The knowledge base of WDES consisted of 35 ‘IF -THEN ’rules and the forward chaining was used to make inferences. Help menu providing information on the nature of questions in text and image forms was included for users to answer questions without difficulty. Watermelon growers and researchers have validated the system and proved possibility of its practical use. In order to facilitate the practical use of WDES by watermelon growers, the knowledge base of WDES needs to be improved by including more detailed information on various diseases and disorders and restructuring rule bases.