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Monitoring Photosynthetic Pigments of Shade-Grown Tea from Hyperspectral Reflectance
https://repository.naro.go.jp/records/3747
https://repository.naro.go.jp/records/37471a0216c2-abd1-473b-9fd7-6f4d4450e1b5
名前 / ファイル | ライセンス | アクション |
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SC30201903070001_Postprint (374.9 kB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2020-06-02 | |||||
タイトル | ||||||
タイトル | Monitoring Photosynthetic Pigments of Shade-Grown Tea from Hyperspectral Reflectance | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Monitoring Photosynthetic Pigments of Shade-Grown Tea from Hyperspectral Reflectance | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | hyperspectral | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | machine learning | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | photosynthetic pigments | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | shade grown tea | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | hyperspectral | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | machine learning | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | photosynthetic pigments | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | shade grown tea | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
佐野, 智人
× 佐野, 智人× 堀江, 秀樹 |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | The highest quality green tea is cultivated using shading treatments in Japan; however, shading can lead to early mortalities of tea due to excessive environmental stress. The allocation of photosynthetic pigments, chlorophyll a, b and carotenoids, could be a good indicator for evaluating production or environmental stress in plants; thus, developing an in-situ method to monitor photosynthetic pigments is useful for agricultural management. To assess the accuracy of the estimation of photosynthetic pigment contents with existing supervised learning models, four different approaches were compared including random forests, kernel-based extreme learning machine (KELM), deep belief nets and support vector machine. Overall, KELM had the highest performance with a root mean square error of 1.95 ± 0.36 μg cm-2, 1.08 ± 0.11 μg cm-2 and 0.68 ± 0.10 μg cm-2 for estimating chlorophyll a, b and carotenoid contents, respectively. | |||||
書誌情報 |
Canadian journal of remote sensing en : Canadian journal of remote sensing 巻 44, 号 2, p. 104-112, 発行日 2018-06-12 |
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出版者 | ||||||
出版者 | Canadian Remote Sensing Society | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1712-7971 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1080/07038992.2018.1461555 | |||||
権利 | ||||||
権利情報 | The Author(s) | |||||
情報源 | ||||||
関連名称 | SC30201903070001 | |||||
情報源 | ||||||
関連名称 | NARO成果DBa | |||||
情報源 | ||||||
関連名称 | author manuscript /This journal has an embargo period of 12 months. | |||||
情報源 | ||||||
関連名称 | This is an [Accepted Manuscript] of an article published by Taylor & Francis in [Canadian journal of remote sensing] on [Jun, 2018], available at http://wwww.tandfonline.com/10.1080/07038992.2018.1461555. | |||||
情報源 | ||||||
関連名称 | このアーカイブは著者版です。出版社版ではありません。引用の際には出版社版をご利用ください。 | |||||
情報源 | ||||||
関連名称 | This is not the published version. Please cite only the published version. | |||||
関連サイト | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1080/07038992.2018.1461555 | |||||
関連名称 | Canadian journal of remote sensing | |||||
著者版フラグ | ||||||
出版タイプ | AM | |||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa |