Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging: growing degree-days integrated model

Nitrogen prediction model of rice plant at panicle initiation stage using ground-based... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging: growing degree-days integrated model

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Publisher
Springer Journals
Copyright
Copyright © 2015 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-015-9394-9
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Precision AgricultureSpringer Journals

Published: Apr 12, 2015

References

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