Abstract: Citrus Huanglongbing (HLB) is one of the most serious plant diseases in modern agriculture. Hyperspectral reflectance can contain the physical morphological features of the leaf surfaces. This study aimed to improve the accuracy of the non-destructive diagnosis for the Citrus HLB. Hyperspectral reflectance was also fused with the transmittance information of the sample surface, particularly for the internal structural and compositional features. A hyperspectral reflectance-transmittance fusion was proposed to detect the HLB in the citrus leaves. Firstly, the polymerase chain reaction (PCR) was used to identify the severity levels of the citrus HLB in leaves. After that, the leaves were categorized into four classes: healthy, mild infection, moderate infection, and severe infection. Subsequently, the reflectance and transmittance hyperspectral data in the wavelength range of 372.66-1 039.65 nm of the citrus leaves were collected at the different severity levels of the citrus HLB. The hyperspectral data were preprocessed using standard normal variate (SNV), savitzk-golay (SG) filtering, and multiplicative scatter correction (MSC). Then, the uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA) were adopted to conduct the feature variable selection for the preprocessed data. Three machine learning methods—least squares support vector machine (LS-SVM), random forest (RF), and back-propagation neural network (BPNN)—were utilized to perform the classification modeling. Meanwhile, the spectral analysis was conducted on the single reflectance and transmittance hyperspectral data for the leaves at the various HLB levels. The classification models were established using reflectance and transmittance spectra. Finally, three grading models were developed for the citrus HLB severity after hyperspectral reflectance-transmittance fusion (data-, feature-, and decision-level fusion). The experimental results showed that the single reflectance spectra, single transmittance spectra, and reflectance-transmittance fused classification were all effectively classified and then detected the citrus HLB. The feature and decision-level fusion significantly outperformed the single-spectral approaches. Particularly, the decision-level fusion achieved the best prediction performance at the different severity levels of the HLB under various models. Furthermore, the prediction accuracy was also improved by 3.2% to 11.6%, compared with the single-spectrum data. Among them, the decision-level fusion with the BPNN classification model—where SG filter preprocessing was combined with the CARS for the characteristic band selection—was achieved in the best classification, with an accuracy rate of 99.98%. Hyperspectral imaging technology with the reflectance-transmittance fusion can be expected for the effective, rapid, and high-precision detection of the citrus HLB severity. Decision-level fusion can maximize the performance of the model. The findings can also provide a strong reference to detect plant diseases.