変化点分析法を用いた圃場環境情報の特徴量抽出とその妥当性の検証

岡安 崇史, Prima Nugroho Andri, 尾崎 彰則, 光岡 宗司, 南石 晃明, 井上 英二, 平井 康丸

研究成果: ジャーナルへの寄稿記事

抄録

Environmental data are measured and collected by simple monitoring devices installed in agricultural fields. These data are very important not only for understanding and predicting factors such as the growth of crops and the occurrence of pests and diseases, but also for optimizing agricultural production processes such as planting, irrigation, fertilization, pest control, and harvesting. However, the amount of data stored in agricultural information databases is rapidly increasing owing to an increase in the number of monitoring devices and sensors installed in such devices. To properly detect characteristic values from such a vast amount of data, it is necessary to develop new numerical technologies and methods. We developed a simple field monitoring system to establish field observations, production optimization, and information sharing between farmers. We also developed and applied a change point analysis program based on the singular spectrum transformation to conduct change point analyses of the field environmental data measured by the monitoring devices. The calculated change point values were then verified through a comparison with farm data recorded by a corporative farmer in the feasibility study.
元の言語Japanese
ページ(範囲)174-182
ページ数9
ジャーナル農業情報研究
22
発行部数3
DOI
出版物ステータス出版済み - 2013

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monitoring
pest control
feasibility study
monitoring system
agricultural production
irrigation
farm
sensor
crop
analysis
environmental information
method
environmental data
comparison
pest
planting
analysis programme

これを引用

変化点分析法を用いた圃場環境情報の特徴量抽出とその妥当性の検証. / 岡安崇史; Andri, Prima Nugroho; 尾崎彰則; 光岡宗司; 南石晃明; 井上英二; 平井康丸.

:: 農業情報研究, 巻 22, 番号 3, 2013, p. 174-182.

研究成果: ジャーナルへの寄稿記事

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abstract = "Environmental data are measured and collected by simple monitoring devices installed in agricultural fields. These data are very important not only for understanding and predicting factors such as the growth of crops and the occurrence of pests and diseases, but also for optimizing agricultural production processes such as planting, irrigation, fertilization, pest control, and harvesting. However, the amount of data stored in agricultural information databases is rapidly increasing owing to an increase in the number of monitoring devices and sensors installed in such devices. To properly detect characteristic values from such a vast amount of data, it is necessary to develop new numerical technologies and methods. We developed a simple field monitoring system to establish field observations, production optimization, and information sharing between farmers. We also developed and applied a change point analysis program based on the singular spectrum transformation to conduct change point analyses of the field environmental data measured by the monitoring devices. The calculated change point values were then verified through a comparison with farm data recorded by a corporative farmer in the feasibility study.",
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