Estimation of nitrogen in cotton leaves using different hyperspectral region data

Abstract

As an important index of a plant’s N nutrition, leaf nitrogen content (LNC) can be quickly monitored in real time with hyperspectral information, which is helpful to guide the precise application of N in cotton leaves. In this study, taking cotton dripping in Xinjiang, China, as the object of study, five N application treatments (0, 120, 240, 360, 480 kg·ha-1) were set up, and the hyperspectral data and the N content of main stem functional leaves at the cotton flower and boll stage were collected. The results showed that (1) comparing the correlations of the three types of spectral data from the original spectra, first derivative spectra, and second derivative spectra with the LNC of cotton, the first derivative spectra increased the correlation between the reflectance in the peak and valley ranges of the spectral curves and the LNC of cotton; (2) in the three hyperspectral regions of VIS, NIR, and SWIR, all R2 values of the estimation model for the LNC of cotton established based on the characteristic wavelengths of the original and the first derivative spectra were greater than 0.8, and the model accuracy was better than that of the second derivative spectra; and (3) the normalized root mean square error (n-RMSE) values of the validated model using MLR, PCR, and PLSR regression methods were all in the range of 10–20%, indicating that the established model could well estimate the nitrogen content of cotton leaves. The results of this study demonstrate the potential of the three hyperspectral domains of VIR, NIR, and SWIR to estimate the LNC of cotton and provide a new basis for hyperspectral data application in crop nutrient monitoring.