Spectral reflectance response to nitrogen fertilization in field grown corn

作     者：Chuanqi Xie Ce Yang Alexander Hummel Jr Gregg A Johnson Forrest T Izuno 

作者机构：Department of Bioproducts and Biosystems EngineeringUniversity of MinnesotaSaint PaulMN 55108United States Farm Nutrients Inc.4949 Hwy 71RembrandtIA 50576United States Department of Agronomy and Plant GeneticsUniversity of MinnesotaSaint PaulMN 55108United States Southern Research and Outreach CenterUniversity of MinnesotaWasecaMN 56093United States 

出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)

年 卷 期：2018年第11卷第4期

页      面：118-126页

核心收录：

学科分类：08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：This work was supported by University of Minnesota Informatics Institute(UMII)on the Horizon Initiative and the Minnesota Long-Term Agricultural Research Network(LTARN)Program 

主　　题：spectrum effective wavelengths principal component analysis-loading(PCA-loading) prediction vegetation indices(VIs) corn 

摘      要：This study was carried out to analyze the spectral reflectance response of different nitrogen levels for corn *** different nitrogen treatments of 0%,80%,100%and 120%BMP(best management practice)were *** component analysis-loading(PCA-loading)was used to identify the effective *** least squares(PLS)and multiple linear regression(MLR)models were built to predict different nitrogen *** indices(VIs)were calculated and then used to build more prediction *** full and selected wavelengths-based models showed similar prediction *** overall PLS model obtained the coefficient of determination(R^(2))of 0.6535 with a root mean square error(RMSE)of 0.2681 in the prediction *** selected wavelengths for overall MLR model obtained the R^(2) of 0.6735 and RMSE of 0.3457 in the prediction *** results showed that the wavelengths in visible and near infrared region(350-1000 nm)performed better than the two either spectral regions(1001-1350/1425-1800 nm and 2000-2400 nm).For each data set,the wavelengths around 555 nm and 730 nm were identified to be the most important to predict nitrogen *** vogelmann red edge index 2(VOG 2)performed the best among all *** demonstrated that spectral reflectance has the potential to be used for analyzing nitrogen response in corn.