Model for identification of citrus diseases by near infrared spectroscopy and influence of citrus diseases on physicochemical index

Pest and diseases have seriously threatened to the yield and quality of citrus. This study aims to explore the effect of citrus diseases on SPAD value using identification model by near-infrared spectroscopy with chemometrics. Leaf spectra and physicochemical indexes were further determined to detect the citrus diseases. The NIR spectra of citrus leaves with normal, canker and emery derma were obtained to measure the physicochemical indices by portable NIR spectrometer. The spectrum analysis showed that there was a reflection peak in the leaves near 553 nm, indicating the reflection peak after the chlorophyll saturated the high-frequency absorption of light. The maximum reflection peak of normal leaves was about 0.21, whereas, the lowest was about 0.13 in the leaves with the sand skin disease. The reflectance of normal leaves was higher than that of diseased leaves at 750-900 nm. The reason was that the reflectance decreased to enlarge the leaf cell cavities under disease stress. The variance was analyzed for two physical and chemical indexes. There were significant differences in the SPAD value indexes of the three types of citrus leaves, while no significant difference was found in fresh quality indexes. A qualitative and quantitative model of citrus diseases was also established to clarify the relationship with leaf spectrum. According to the ratio of 2:1, 212 blades were randomly divided into two dataset with 145 samples in training set and 67 samples in verification set. The SPA and PCA were used to screen the spectral variables. 17 and 20 optimal variables were selected to combine the RF and LWPLS. The comparison showed that the best detection was achieved in the qualitative model of LWPLS for three types of disease leaves using 401 variables of the original spectrum, with the accuracy of 94.03%. While the low accuracy of 77.61% was found in the qualitative model of RF for three types of disease leaves using SPA screening 17 variables. The main reason was that a few sensitive variables were eliminated, although the characteristic variables represented the most information of citrus leaves. A numerical model was also constructed to further verify whether the leaf diseases affected the SPAD value of normal and diseased leaves. The best performance was achieved in the LWPLS model in the normal blade SPAD value analysis with 401 spectral variables as the input. The RMSEP of LWPLS quantitative model for normal, canker and emery was 4.46%, the model had better prediction accuracy for SPAD value of leaves, R² and RPD were 0.93 and 2.19, respectively. NIR spectroscopy with chemometrics was feasible to identify the citrus diseases on physicochemical indexes of leaves. The finding can provide an important reference for the real-time field detection of citrus diseases.