Objective  To monitor the health status of fruit trees and predict the yield of sweet pomelo (Citrus maxima), a detection model of chlorophyll content in C. maxima leaves was established in a C. maxima orchard of Nankang, Ganzhou.

Method  The leaf spectrum and SPAD value of C. maxima were measured using Field Spec4 portable earth spectrometer and SPAD-502 chlorophyll meter. The hyperspectral nondestructive detection model of chlorophyll content was constructed by single variable regression, stepwise regression and partial least squares (PLS) method, and the accuracy was examined.

Result  The reflectances of original spectrum at 553 nm and the first order spectra at 692 and 752 nm had the highest correlation with chlorophyll content. These three bands were sensitive bands of spectral reflectance of C. maxima leaves. When the number of principal components was four, PLS had the highest level of precision. PLS model had higher accuracy, fitting degree and determination coefficient (r²=0.869) compared with the single variable and stepwise regression models, and PLS model had the lowest root mean square error (RMSE) being 3.013 and the lowest relative error (RE) being 6.82%. Comparing and analyzing the estimation models of original spectrum, first derivative spectrum and PLS fitting, PLS model was superior to the two traditional models in terms of sample precision and prediction ability.

Conclusion  PLS model is suitable for the estimation of chlorophyll content by hyperspectral data, and the best nondestructive detection model for the chlorophyll content of C. maxima.