文献类型：期刊文献

英文题名：Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial least-square regression and support vector machine regression methods

作者：Zhai, Yanfang[1,2,3] Cui, Lijuan[4] Zhou, Xin[1,2] Gao, Yin[1,2] Fei, Teng[1,2] Gao, Wenxiu[5]

第一作者：Zhai, Yanfang

通信作者：Gao, WX[1]

机构：[1]Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China;[2]Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China;[3]Chongqing Inst Surveying & Mapping, Chongqing 400014, Peoples R China;[4]Chinese Acad Forestry, Inst Wetland Res, Beijing 100091, Peoples R China;[5]Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China

年份：2013

卷号：34

期号：7

起止页码：2502-2518

外文期刊名：INTERNATIONAL JOURNAL OF REMOTE SENSING

收录：;EI(收录号：20130415923757);Scopus(收录号：2-s2.0-84872311164);WOS:【SCI-EXPANDED(收录号:WOS:000315722200015)】；

基金：This study was supported by the Special Foundation of the Ministry of Finance of China for Nonprofit Research of Forestry Industry (Grant No. 200904001) and the National Natural Science Foundation of China (Grant No. 41171290).

语种：英文

外文关键词：Infrared devices - Laboratories - Near infrared spectroscopy - Nitrogen - Phosphorus - Plants (botany) - Potassium - Reflection - Support vector machines - Support vector regression - Tea

摘要：Nitrogen, phosphorus, and potassium are some of the most important biochemical components of plant organic matter, and hence, estimation of their contents can help monitor the metabolism processes and health of plants. This study, conducted in the Yixing region of China, aimed to compare partial least squares regression (PLSR) and support vector machine regression (SVMR) methods for estimating the nitrogen (C N), phosphorus (C P), and potassium (C K) contents present in leaves of diverse plants using laboratory-based visible and near-infrared (Vis-NIR) reflectance spectroscopy. A total of 95 leaf samples taken from rice, corn, sesame, soybean, tea, grass, shrub, and arbour plants were collected, and their C N, C P, C K, and Vis-NIR reflectance data were measured in a laboratory. The PLSR and SVMR methods were calibrated to estimate the C N, C P, and C K contents of the obtained samples from spectral reflectance. Cross-validation with an independent data set was employed to assess the performance of the calibrated models. The calibration results indicated that the PLSR method accounted for 59.1%, 50.9%, and 50.6% of the variation of C N, C P, and C K, whereas the SVMR method accounted for more than 90% of the variation of C N, C P, and C K. According to cross-validation, the SVMR method achieved better estimation accuracies, which had determination coefficients of 0.706, 0.722, and 0.704 for C N, C P, and C K, respectively, than the PLSR method, which had determination coefficients of 0.663, 0.643, and 0.541. It was concluded that the SVMR method combined with laboratory-based Vis-NIR reflectance data has the potential to estimate the contents of biochemical components.