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論文
タイトル
タイトル(英)
Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging: growing degree-days integrated model
参照URL
https://researchmap.jp/7000020455/published_papers/40343613
著者
著者(英)
Hiroyuki Onoyama,Chanseok Ryu,Masahiko Suguri,Michihisa Iida
担当区分
筆頭著者
概要
概要(英)
Ground-based hyperspectral imaging was applied to rice plants at the panicle initiation stage to estimate nitrogen content. We developed a partial least squares regression (PLSR) model that incorporated both the reflectance and growing degree-days (GDD) to account for differences in growing temperature conditions across a 3-year period. The acquired images were divided into two components: (1) the rice plant and (2) other elements (e.g., irrigation water, soil background) by using the GreenNDVI - NDVI equation. Rice plant reflectance (Ref (RICE) ) was calculated as the ratio of rice plant reflectance to that of a reference board. Three types of PLSR models were constructed: 1-year, 2-year, and 2-year GDD. Mutual estimation was used to infer the predictive power of the three models, which was calculated by estimating the values for the other years. The root mean square error of prediction (RMSE) of the mutual estimation for the 1- and 2-year PLSR models was high because of overestimation and underestimation. In contrast, the RMSE of the mutual estimation for the 2-year GDD PLSR models clearly decreased. It was inferred that hyperspectral imaging at 400-1000 nm could not predict variation in the amount of growth caused by weather variation expressed as GDD. This study indicates that the combination of reflectance and temperature data could be used to potentially construct an adaptable model to identify variance in growing conditions.
出版者・発行元
出版者・発行元(英)
SPRINGER
誌名
誌名(英)
PRECISION AGRICULTURE
16
5
開始ページ
558
終了ページ
570
出版年月
2015年10月
査読の有無
査読有り
招待の有無
掲載種別
研究論文(学術雑誌)
ISSN
1385-2256
DOI URL
https://doi.org/10.1007/s11119-015-9394-9
共同研究・競争的資金等の研究課題
研究者
小野山 博之 (オノヤマ ヒロユキ)