In this paper we discussed the application of spectral and textural features in identifying early stage of the citrus greening disease (Huanglongbing or HLB). A total of 176 hyperspectral images of citrus leaves (60 for healthy, 60 for HLB-infected and 56 for zinc-deficient) were captured by using a near-ground hyperspectral imaging system. Regions of interest (ROI) were extracted manually from the part of pathological changes in the images to calculate the average reflectance spectra of each sample as the sample spectra, ranging from 396 to 1 010 nm. The dimensions of the sample spectra were reduced with the algorithms of principal component analysis (PCA) and successive projection analysis (SPA). Classification models were built with the original spectra and candidate variables, the first four PCs selected by PCA and a set of wavelengths (630.5, 679.4, 749.4 and 899.9 nm) selected by SPA. The results based on a classifier of least square-support vector machine (LS-SVM) showed that the classification models built with the candidate variables selected by PCA and SPA had a better performance, achieving 89.7% and 87.4% in terms of average accuracy. In addition, two groups of textural features, extracted from gray images of the four selected wavelengths based on gray-level histogram and gray-level co-occurrence matrix (GLCM), were also used for the classifier. The first ten features ranked by SPA promoted the average accuracy of classifier significantly, achieving 100%, 93.3% and 92.9% for the three class samples respectively. The results of this study indicated that it would be feasible to identify HLB using the image textural features based on selected wavelengths, and it provided a basis for developing a portable HLB detection system with multispectral imaging techniques.