Huanglongbing (HLB, or citrus greening) has been one of the devastating diseases in citrus production. The infection of HLB can seriously affect the photosynthetic capacity and growth of citrus plants. Thus, early, accurate, and rapid in-situ diagnosis of HLB can be highly critical to reducing the economic losses for the citrus industry. This study aims to develop an in-situ diagnosis of citrus HLB disease for the photosynthetic response of citrus leaves using sun-induced chlorophyll fluorescence. Four groups were labelled on the citrus trees with the asymptomatic HLB (aHLB), symptomatic HLB (sHLB), and macular (with symptoms similar to HLB) infection, as well as the healthy trees in the orchard. A Li-6800 portable photosynthetic system was utilized to measure the photosynthetic parameters (net photosynthetic rate (Pn), intercellular CO2 concentration (Ci), transpiration rate (Tr), stomatal conductance (Gs)) of citrus leaf samples. The photosynthetic CO2 response curve (A-Ci) was obtained under the different carbon dioxide concentrations, further to calculate the maximum leaf carboxylation rate (Vcmax) and the maximum electron transfer rate (Jmax). Then, the upward and downward sun-induced chlorophyll fluorescence (SIF) and reflectance spectra of leaf samples were collected using an analytical spectral devices (ASD) spectrometer combined with a FluoWat clip. Finally, the photosynthetic pigments content (chlorophyll a, chlorophyll b, and carotenoids) of the leave samples were measured using the spectrophotometry in the laboratory, and the true infection status of the leaves was confirmed using the real-time quantitative polymerase chain reaction (qPCR). A systematic investigation was made on the photosynthetic parameters and the pigments content of the leave samples. An optimal combination of wavebands was selected using the competitive adaptive reweighted sampling (CARS) algorithm and reflectance spectra. The upward (Up) and downward (Dw) SIF yield indices (Up687, Up741, Dw687, Dw741, Up687/741, Dw687/741) were constructed using the peak position (687 nm and 741 nm) of the SIF spectra. Furthermore, the classification models of HLB were established to combine with the K-nearest neighbor (KNN) algorithm, according to the optimal wavebands of reflectance spectra and SIF yield indices. The results showed that the infection of HLB pathogen led to a significant decrease in the photosynthesis of citrus leaves even at the asymptomatic stage, indicating an excellent performance of SIF signals in the early diagnosis of HLB. The diagnostic accuracies of KNN models with the leaf reflectance using the optimal wavebands were 72.7% and 75.6% for aHLB and sHLB leaves, and 82.2% and 64.1% for healthy and macular leaves, respectively. By contrast, the diagnostic accuracies of KNN models using the Up687/741 (ratio of upward SIF yield at 687 nm to 741 nm) SIF yield index for aHLB and sHLB were 84.8% and 91.1%, and that of healthy leaves and macular leaves were 88.9% and 82.1%, respectively. Consequently, the KNN models with the leaf SIF spectra presented a higher potential in the early diagnosis of HLB than those with leaf reflectance spectra. These findings can provide a strong reference for the early, rapid, and in-situ diagnosis of citrus HLB in the incubation period. © 2022 Chinese Society of Agricultural Engineering. All rights reserved.
