| Project/Area Number |
61560286
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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 |
農業気象・生物環境制御学
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| Research Institution | University of Tokyo |
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Principal Investigator |
KURATA Kenji University of Tokyo, 農学部, 助教授 (90161736)
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| Co-Investigator(Kenkyū-buntansha) |
HONJO Tsuyoshi University of Tokyo, 農学部, 助手 (60173655)
TAKAKURA Tadashi University of Tokyo, 農学部, 教授 (50011929)
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| Project Period (FY) |
1986 – 1987
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| Project Status |
Completed (Fiscal Year 1987)
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| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 1987: ¥300,000 (Direct Cost: ¥300,000)
Fiscal Year 1986: ¥2,000,000 (Direct Cost: ¥2,000,000)
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| Keywords | Artificial Intelligence / Inductive Inference / Machine Learning / マシンラーニング / 作物管理 / 知識工学 |
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| Research Abstract |
In protected cultivation, much work is needed to get higher yield especially in controlling the environmental factors at the optima or in managing the crops optimally. This project aims at developping a system which reduced amount of work needed in managing the crops. Management of the crops are dependent on local conditions such as water table level or local climate. It is, therefore, questionable if we can apply a general algorithm to such a quite locally prescribed management. Taking this point into consideration we are developping a system which first tries to learn the methods or principles of management of the farmer (rules, hereafter) from the measured conditions prescrbing the management (eg. climate conditions) and the management behavior of the farmer. After inducing the rules from the measurements, the system applies the rules to the management, which releases the farmer from the management labor. Farmers control the environmental factors not necessarily based on the physically measured values but in most cases according to what they feel. Farmers also do not act as accurately as machines. These mean that the learning algoritjm should be able to cope with fuzziness and noise, which will make the learning strategy quite difficult. In spite of considerable effort we have not yet been successful in this point. However, the learning algorithm we have developped proved to be able to completely copy the management behavior, if it containes no fuzziness nor noise. This algorithm can be applied to most cases of the crop management.
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