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

Abstract

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.