Diagnosis of Citrus Greening Based on the Fusion of Visible and Near-Infrared Spectra

A disease, known as citrus greening, is a major threat to the citrus industry. The objective of this study was to investigate the feasibility of rapid detection and improving the identification accuracy of citrus greening with visible and near-infrared spectra under spectral fusion. After we obtained the spectra of the collected citrus leaves and used the polymerase chain reaction for part of them, five types of samples were sorted out: slight, moderate, serious, nutrient deficiency, and normal. This study of spectral fusion was conducted on three levels as spectral data, characteristic, and model decision, and the identification capacity was tested using prediction samples. It was found that the effect of a least squares support vector machine model for feature-level fusion based on principal component analysis presented the best performance, while in the Lin_Kernel function; the accuracy was 100%, penalty coefficient & gamma; was 0.09, and operation time was 0.66 s. It is better than the single spectral discriminant model. The results showed that the fusion of visible and near-infrared spectra was feasible for the nondestructive detection of citrus greening disease. This method is of great significance for the healthy development of the citrus industry, and provides important reference value for the application of spectral fusion in other fields.

Automated summary

This study investigated the feasibility of using visible and near-infrared spectra for rapid detection and improved identification accuracy of citrus greening through spectral fusion. The results showed that a least squares support vector machine model based on principal component analysis had the best performance in feature-level fusion. The accuracy of this model was 100% for the detection of citrus greening.