2023 Fiscal Year Final Research Report
Development of a method for gathering crop growth information for wide-area evaluation
| Project/Area Number |
21K14841
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 39020:Crop production science-related
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| Research Institution | Kindai University |
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Principal Investigator |
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Keywords | イネ / ダイズ / ササゲ / 非破壊計測 / 機械学習 / 植生指標 / 収量予測 |
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| Outline of Final Research Achievements |
In recent years, there has been a growing demand for increased efficiency in land-use crop cultivation. It is considered important to evaluate crop growth conditions through non-destructive measurements and develop evaluation methods suitable for a wide geographical area. In this study, we compared and verified the parameters of growth dynamics with crop productivity and suggested that the accuracy of yield estimation can be improved by using cultivar-specific physiological parameters during the late growth stage. Additionally, we identified suitable vegetation indices to evaluate the growth dynamics of rice and soybeans. Furthermore, a cowpea yield prediction model was developed using machine learning and crop growth information obtained through continuous non-destructive measurements.
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| Free Research Field |
作物学
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| Academic Significance and Societal Importance of the Research Achievements |
リモセンを用いた作物生育の広域評価手法の開発によって，従来の観測手法では難しかった広範な農地の状態を効率的に把握することが可能になると考えられる．また，異なる環境条件下での作物の生育パラメータと生産性との関連性を解明し，品種ごとの生育特性や生理パラメータの影響を詳細に調査した本研究は，作物生理学的な理解を深め，新たな育種戦略の提案にもつながると考えられる．さらに，今後気候変動などの影響を受ける農地においては、効率的な作物栽培管理は持続可能な農業を実現するために不可欠であり，本研究で開発した機械学習を利用した作物収量予測モデルは安定したストレス環境下の農業生産に寄与することが期待される．
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