Optimization and prediction of tomato health-promoting properties by using agricultural big data
| Project/Area Number |
18K05907
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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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| Review Section |
Basic Section 41040:Agricultural environmental engineering and agricultural information engineering-related
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| Research Institution | University of Miyazaki |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | トマト / 機能性成分 / 予測モデル / アスコルビン酸 / ポリフェノール / 抗酸化活性 / 栽培環境 / 農業データ / ビタミンC / 農業ビッグデータ / 光環境 |
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| Outline of Final Research Achievements |
英文
The aim of this study was to explore the factors that improve the content of health-promoting properties in tomatoes using agricultural big data obtained from cultivation sites, and to develop a predictive model for their contents, based on the clarification of the mechanism. As a result, the study was able to clarify the importance of light environment for the improvement of health-promoting properties (ascorbic acid, polyphenol, and antioxidant activity) of tomatoes using agricultural big data obtained from cultivation sites, and and prediction models for their contents could be constructed with high accuracy.
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| Academic Significance and Societal Importance of the Research Achievements |
トマトは機能性成分が豊富な野菜の一つであるが，その含量を予測する技術は未開発である．トマトの収量や生育の予測モデルは国内外の多くの研究グループが進めているが，機能性成分含量予測モデルを構築した研究成果は国内外を見渡しても無く，本研究が初めてである．これらのことから，本研究で得られた知見は，我が国のデータ駆動型スマート農業技術への貢献だけでなく，機能性成分における新たな研究の幕開けとなる大きなインパクトを持つ．
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Report
(4 results)
Research Products
(10 results)
