Visual discrimination of citrus HLB based on image features

Citrus greening (Huanglongbing, HLB) has seriously threatened the healthy development of citrus industry in recent years, and it is great significance to diagnose citrus HLB timely and accurately. Hyperspectral technology has the characteristics of spectral analysis and image processing, which has shown great advantages in plant disease detection. The visual discrimination methods of citrus HLB based on features of images combined with hyperspectral imaging technology were discussed. Five types of citrus leaves, including the mild HLB, moderate HLB, serious HLB, malnourished and normal were studied and polymerase chain reaction (PCR) test was used to verify visual division. Effective spectral variables were selected by successive projections algorithm (SPA) and features of images were extracted by principal component analysis (PCA). Texture features of images based on grayscale co-occurrence matrix (GLCM) were used to develop the partial least squares discriminant analysis (PLS-DA) models. The influence of the number of textures on the models was discussed, and the effect of discrimination model was the best when a total of 36 variables which were obtained from 4 independent texture features were as input, which resulted in greater misjudgment rate of 3.12%, higher correlation coefficient for prediction (RP) of 0.98, lower root mean square error for prediction (RMSEP) of 0.32, and the number of principal component factors (PCs) of 16. The results highlighted that the image texture features based on GLCM combined with the PLS-DA models could realize the identification of citrus HLB, and provide the important reference value for the visual discrimination research of HLB.