Abstract
Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral features, and typical hyperspectral vegetation indices. First, the Savitzky-Golay technique was used to smooth the original spectrum, following which three types of spectral parameters describing crop spectral characteristics were extracted. Next, the successive projections algorithm (SPA) was adopted to screen out the sensitive variable set from each type of parameters. Finally, partial least squares (PLS) regression and random forest (RF) algorithms were used to comprehensively compare and analyze the performance of different types of spectral variables for estimating corn leaf nitrogen content (LNC). The results show that the integrated variable set composed of the optimal ones screened by SPA from three types of variables had the best performance for LNC estimation by the validation data set, with the values of R2, root means square error (RMSE), and normalized root mean square error (NRMSE) of 0.77, 0.31, and 17.1%, and 0.55, 0.43, and 23.9% from PLS and RF, respectively. It indicates that the PLS model with optimally multitype spectral variables can provide better fits and be a more effective tool for evaluating corn LNC.
Highlights
Hyperspectral data with high spectral resolution could reveal small changes in the biochemical components of plant leaves, and its acquisition was rapid and non-destructive
The results show that difference vegetation index (DVI) I, DVI II, and TVI had a weaker relevance with circles and lighter color when compared to the other lighter color whenSuch compared theexpressed other vegetation indices (VIs).in Figure 6, where these VIs are represented by smaller resultstoare circles and lighter color when compared to the other VIs
The two parameters were modeled with the leaf nitrogen content (LNC) (Table 6), and the results show that2 R2, estimated by the partial least squares (PLS) model and the random forest (RF) model, respectively
Summary
Hyperspectral data with high spectral resolution could reveal small changes in the biochemical components of plant leaves, and its acquisition was rapid and non-destructive. Rapid, non-destructive monitoring of plant leaf biochemical components by hyperspectral means has became an important part of the evaluation of vegetative growth status. Hyperspectral data with hundreds or even thousands of bands could provide more detailed and richer spectral information than multispectral data, it suffered from significant data redundancy, high correlation between adjacent bands, etc. More research was required to determine how best to extract from hyperspectral data the characteristic spectral variables to effectively monitor the biochemical components of crop targets. Spectral variables based on extracted hyperspectral data might be divided into three categories generally:. Hyperspectral data offer more accurate bands, which can better reflect the characteristics of vegetation. Bai et al [2] applied the successive projections to extract eight
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