| Project/Area Number |
06660186
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
林学
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
SAKAI Tetsuro Kyoto Univ., Faculty of Agriculture, Associate Professor, 農学部, 助教授 (10101247)
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| Project Period (FY) |
1994 – 1995
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| Project Status |
Completed (Fiscal Year 1995)
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| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 1995: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1994: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | Data-base / tree / expert system / natule education / photo-database / Hokkaido / Fuji deduction / forest education / ファジィ推論 / 画像データ |
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| Research Abstract |
We studied on expert system of tree distinguished during past two years. The objects of this studies are to appear a knowledge for tree distinguished and to construct of prototype expert system. We get some methods according to how to gather photo-data of tree using camera, video and digital camera, to arrange them and to maintain photo-database. We make some programs of photo-database for maintenance and reverse using P.C..Next, we construct two expert systems of tree distinguished against 64 species on a Kyoto University forest in Hokkaido using expert shell. The method of deduction is IF-THEN method that is popular. We arranged classify indexes according to specifics of each tree which are a shape of leaf, a form of leaf vein and etc.and constructed knowledge database. Analyzing for path of deduction, it was appear that all classify indexes are not necessary to distinguish. The first expert system is similar to reference of a plant book. That is a spreading into branch deduction method using some classify indexes. The technic of Fuji deduction is used for length and width of leaf. The second expert system is same to the first until middle classify. After middle classify, all proposed trees are shown at display with their photograph and classify indexes. If user selects one of them that is corresponding to classify indexes of object tree, the tree characteristic is shown. For a beginner who doesn't understand a mean of classify index's term, this method is useful to learn how to distinguish tree names.
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