Estimating the distribution of chlorophyll content in CYVCV infected lemon leaf using hyperspectral imaging
Xunlan Li, Zhaoxin Wei, Fangfang Peng, Jianfei Liu, Guohui Han
Abstract
Yellow vein clearing disease of lemon caused by the citrus yellow vein clearing virus (CYVCV) is a new devastating disease in lemon. Chlorophyll contents and distributions are critical biochemical parameters to assess this new disease. The objective of this study was to develop an efficient method for detecting chlorophyll contents and distributions in lemon leaves infected with CYVCV. Spectral reflectance and chlorophyll content data of healthy, nitrogen deficient, pesticide-damaged and the corresponding CYVCV-infected leaves were obtained. Successive projections algorithm, random frog, competitive adaptive reweighted sampling (CARS), synergy interval partial least squares and synergy interval partial least squares combined with successive projections algorithm were applied for reducing data dimension, respectively. Prediction models were built by applying least squares-support vector machine algorithm (LS-SVM). The results showed that the characteristic wavelengths were basically located in “green peak” (near 550 nm), “red valley” (near 680 nm), and “red edge” (680–750 nm). Prediction model based on the wavelengths extracted by CARS achieved the optimal prediction results with determination coefficient of 0.94, relative prediction deviation of 3.91 and root mean square error of 0.10 in testing set. Thus, the CARS algorithm combined with LS-SVM was applied for distributions of chlorophyll contents. The chlorophyll content of each pixel of the six types of leaves were displayed, and significantly reduced in the mesophyll tissue near the veins of CYVCV-infected leaves. These results suggest that the hyperspectral imaging coupled with CARS and LS-SVM can be used to efficiently measure the distribution of chlorophyll content in CYVCV infected lemon leaf, which provides a reference for a better understanding of the symptoms of disease.