Identification of the Citrus Greening Disease Using Spectral and Textural Features Based on Hyperspectral Imaging.

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.

[Detection of citrus greening based on Vis-NIR spectroscopy and spectral feature analysis].

In the present paper we discussed the methods of classification of citrus greening and extraction of spectral features based on the spectral reflectance of four different statuses of citrus leaves (healthy, HLB, iron deficiency and nitrogen deficiency). Between two classes of classification, the values of discriminability of different spectra were calculated to extract spectral features. The greater value of discriminability showed a bigger difference of the two spectra, which means it would be easier to distinguish the two classes.