Estimating Biochemical Component Contents of Diverse Plant Leaves with Different Kernel Based Support Vector Regression Models and VNIR Spectroscopy
CHEN Fang-yuan1, 2, ZHOU Xin1, 2, CHEN Yi-yun1, 2, WANG Yi-han3, LIU Hui-zeng4, 5, WANG Jun-jie5, 6, WU Guo-feng1, 5, 6*
1. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
2. Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan 430079, China
3. Surveying and Mapping Engineering Institute of Hubei Province, Wuhan 430074, China
4. Department of Geography, Hong Kong Baptist University, Hong Kong, China
5. Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
6. College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
Abstract：Nitrogen (N), phosphorus (P) and potassium (K) are important biochemical components of plant organic matters, and estimating their contents are useful for monitoring plant metabolism processes and health. Visible and near-infrared (VNIR) spectroscopy has been applied to monitor plant biochemical parameters with many modeling methods, in which support vector machine (SVM) has been proved to be a potential approach for modeling the nonlinear relationships between the reflectance spectra and biochemical parameters of plant organic matters, and the successful application of SVM relies on the proper selection of kernels. This study aimed to compare the performances of radial basis function (RBF), polynomial and sigmoid kernels based support vector machine regression (SVR) models in estimating the contents of nitrogen (cN), phosphorus (cP) and potassium (cK) of diverse plant leaves using laboratory-based VNIR spectroscopy. The cN, cP, cK and VNIR reflectance of leaf samples in eight plant species(rice, corn, sesame, soybean, tea, grass, shrub and arbor) were measured in laboratory. Three transformation methods, namely the first derivative(FD), standard normal variate (SNV) and logrithmic reciprocal transformation (Log(1/R)) were used for spectral transformation. The SVR models using three aforementioned kernels were calibrated and validated with 1 000 bootstrap sample datasets. The average determination coefficients (R2) as well as ratio of performance to standard deviate (RPD) were calculated to compare the performances of three different kernels. The results showed that, the RBF kernel based SVR model with FD and absorbance transformation obtained the best accuracy for cN and cK estimations (cN: mean R2=0.64, mean RPD=1.67; cK: mean R2=0.56, mean RPD=1.48), and the RBF kernel based SVR model with FD transformation obtained the best accuracy for cP estimations (cP: mean R2=0.68, mean RPD=1.73). The study indicated that RBF kernel based SVR model has great potential in estimating biochemical component contents of diverse plant leaves with VNIR spectroscopy.
Key words：Kernel function; Support vector machine; VNIR spectroscopy; Biochemical content
陈方圆，周 鑫，陈奕云，王奕涵，刘会增，王俊杰，邬国锋. 不同核函数支持向量机和可见-近红外光谱的多种植被叶片生化组分估算[J]. 光谱学与光谱分析, 2019, 39(02): 428-434.
CHEN Fang-yuan, ZHOU Xin, CHEN Yi-yun, WANG Yi-han, LIU Hui-zeng, WANG Jun-jie, WU Guo-feng. Estimating Biochemical Component Contents of Diverse Plant Leaves with Different Kernel Based Support Vector Regression Models and VNIR Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(02): 428-434.
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