Application of hyperspectral canopy reflectance measurement and partial least square regression to predict within-field spatial variation in crop growth and nitrogen status before heading stage of rice

Hung. T. Nguyen; Jun Han Kim; Anh T. Nguyen; Jin Chul Shin; Byun-Woo Lee

Application of hyperspectral canopy reflectance measurement and partial least square regression to predict within-field spatial variation in crop growth and nitrogen status before heading stage of rice

In: Precision Agriculture â05

Authors:

Hung. T. Nguyen

Hung. T. Nguyen ¹Department of Plant Science, College of Agriculture and Life Sciences, Seoul National University, Seoul 151-741, Korea

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Jun Han Kim

Jun Han Kim ¹Department of Plant Science, College of Agriculture and Life Sciences, Seoul National University, Seoul 151-741, Korea

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Anh T. Nguyen

Anh T. Nguyen ¹Department of Plant Science, College of Agriculture and Life Sciences, Seoul National University, Seoul 151-741, Korea

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Jin Chul Shin

Jin Chul Shin ²National Institute of Crop Science, Rural Development Administration, Suwon 441-857, Korea.

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, and

Byun-Woo Lee

Byun-Woo Lee ¹Department of Plant Science, College of Agriculture and Life Sciences, Seoul National University, Seoul 151-741, Korea
leebw@snu.ac.kr

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Type:: Chapter

Pages:: 251â260

DOI:: https://doi.org/10.3920/978-90-8686-549-9_031

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For site-specific prescription of fertilizer topdressing in rice cultivation, nondestructive diagnosis of the rice growth and nutrition status is indispensable. Three experiments were carried to develop and test a model using canopy reflectance for nondestructive diagnosis of the rice growth and N status. Two experiments for model development were conducted, one in 2000 and one in 2003 at the Experimental Farm (37^o16â N, 126^o59â E) of Seoul National University, Suwon, Korea. The experiment included two rice varieties and four nitrogen (N) rates in year 2000 and four rice varieties and 10 nitrogen treatments in year 2003. Hyperspectral canopy reflectance (300 to 1100 nm) data recorded at various growth stages before heading were used in developing a partial least square regression (PLS) model to predict eight crop variables such as plant biomass and N nutrition status. 342 observations were split for model calibration (75%) and validation (25%). The PLS model was then tested to predict within-field spatial variation of three crop variables using hyperspectral canopy reflectance data measured for 10 Ã 10m grids from a paddy field of 6500 m² in 2004. Coefficient of determination (R²), root mean square of error in prediction (RMSEP) and relative error of prediction (REP) were calculated for the model quality evaluation. The results revealed that PLS using hyperspectral canopy reflectance data to predict eight plant variables in year 2000 and 2003 produced an acceptable model precision and accuracy. The model R2 and REP ranged from 0.81 to 0.88 and 10.0 to 23.8% for calibration and 0.76 to 0.85 and 11.1 to 24.6% for validation, respectively. The model R² was reduced in the test data of year 2004 but the error in prediction (RMSEP and REP) was smaller. The PLS model using canopy reflectance data could be a promising method to predict within-field spatial variation of rice crop growth and N status if error sources occurring during canopy reflectance measurement were successfully controlled.

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Precision Agriculture â05

E-Book ISBN:: 9789086865499

Publisher:: Wageningen Academic

Print Publication Date:: 24 Oct 2005

Subjects
- Life Sciences
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  - Agriculture & Environment

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Application of hyperspectral canopy reflectance measurement and partial least square regression to predict within-field spatial variation in crop growth and nitrogen status before heading stage of rice

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