| Project/Area Number |
15K07678
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Agricultural environmental engineering/Agricultural information engineering
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| Research Institution | Kyushu University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
山川 武夫 九州大学, 農学研究院, 准教授 (20220238)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 水稲 / 要因解析 / パターン認識 / 収量構成要素 / 籾数 / 登熟歩合 / 千粒重 / 穀物生育モデル / サポートベクターマシン / コメ生産 / 収量 / 品質 / ばらつき / 診断 |
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| Outline of Final Research Achievements |
The objective of this study was to develop the diagnosis method for rice production that can be used in actual fields to improve rice yield and quality. First, pattern recognition was applied to develop the diagnosis method using data (yield, quality, farmer’s management, weather, soil, growth) collected under various environment and management styles in actual fields. This method was not accurate for individual fields but was useful to identify common problems in a certain region and take measures to improve rice yield and quality. On the other hand, crop model was judged to be difficult to apply in actual fields because it can’t take water management into account, has many parameters to set, and can’t predict ripened brown rice yield.
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