Hyperspectral estimation for nitrogen and phosphorus content in Camellia oleifera leaves based on machine learning algorithms

Abstract

Nitrogen and phosphorus are essential elements of plants, which play important roles in representing plant growth, physiological function regulation, fruit harvest, etc. Hyperspectral technology provides a nondestructive, rapid, highly accurate, and cost-efficient method for plant leaf nutrient content estimation. There are very limited studies on nutrient diagnosis of Camellia oleifera leaves using hyperspectral technology. In this work, 160 Camellia oleifera samples were used. Hyperspectral data were obtained using a full-band spectrometer. On the basis of preprocessing, the spectral response characteristics of leaf nitrogen content (LNC) and leaf phosphorus content (LPC) were revealed by comparing different combinations of spectral indices, and the spectral variables were further selected. The optimal LNC and LPC estimation models based on three machine learning algorithms [i.e., support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN)] were constructed. The results showed that the spectral sensitive regions of leaf nitrogen and phosphorus content were mainly reflected in green band, followed by red band and the long-wave direction of short-wave infrared band. Savitzky-Golay first derivative (SGFD) pretreatment method was generally better than multiplicative scatter correction. The maximum correlation coefficients of the absolute values of LNC, LPC, and spectral transformation features were 0.56 and 0.49. The optimal LNC and LPC models were both SGFD-TBNDSI-BPNN, with R 2 of 0.81 and 0.79, and RMSEP of 0.55 and 0.06 g∕kg, respectively. The research results can provide a reliable theoretical basis for large-scale optical remote sensing monitoring of nutrient content for Camellia oleifera.