## 1 Introduction  
Citrus greening, or Huanglongbing (HLB), as one of the most devastating citrus diseases likely to break out throughout the whole year, is caused by Gram-negative bacteria in the phloem of branches [1, 2], and mainly spreads by citrus psyllid. In a few previously conducted studies, there was a correlation between the sex of the psyllid and the host tree and density of the bacterial population, while the citrus species, geographical conditions, climate, planting pattern and management level, and other external factors had a certain influence on the disease and were closely related to the photosynthesis of citrus trees, which resulted in the difference in spectral information between the infected and normal samples [3, 4, 5]. It can spread rapidly once being infected. The only effective way is to remove the infected tree [6]. It should be pointed out that the spread of citrus greening poses a serious threat to the stability and development of the citrus industry [7]. Therefore, effective measures for controlling its invasion and spread lie in prevention, and the premise of prevention is to detect it in time. Currently, there are two common diagnostic methods. One is laboratory pathological analysis using polymerase chain reactions, including conventional and fluorescence quantitative polymerase chain reactions. Although this method has a high level of accuracy, it is time-consuming and expensive [8, 9]. The other method is manual field diagnosis, which is simple and not time-consuming, but has a low accuracy [10; 11]. In recent years, researchers have applied the spectroscopy techniques in citrus greening detection, and progress has been achieved. [PERSON] et al. (2011) obtained the visible (Vis) and near-infrared spectra of 100 healthy and 93 HLB-infected samples in the range of 350-2500 nm, and reduced the number of input variables via pretreatment and principal component analysis (PCA). The classification model was developed by, respectively, applying linear discriminant analysis, quadratic discriminant analysis, K-nearest neighbor, and soft independent modeling of classification analogies (SIMCA), among which the method of quadratic discriminant analysis achieved the highest average classification accuracy of 95% [12]. [PERSON] et al. (2011) obtained the positive and negative leaf spectra of HLB with near-infrared spectroscopy, and successfully established the partial least squares (PLS) regression model, which can classify positive HLB, negative HLB, other diseases, and nutritional deficiency samples with an increase in accuracy from 92% to 99% [13]. The above research was conducted under the condition of a single sensor; thus, feedback information was lower and the detection accuracy was low. The spectrum is a comprehensive representation of the physical and chemical information of the leaf, and the spectral data fusion technique leads to an increase in the amount of feedback spectral information, which can fully reflect the physical and chemical information of the leaf [14; 15; 16; 17]. The method of spectral data fusion technology is to fuse all spectral data together in different ways, which can obtain more comprehensive and useful information. Data fusion can be carried out at three levels, namely low-level fusion, middle-level fusion, and high-level fusion. Low-level fusion is the data-layer fusion of the spectrum. The data from all data sources are simply connected to a matrix in sample order. Middle-level fusion is to extract relevant features from each data source and combine them into a matrix, so it is also called feature-level fusion. High-level fusion, also known as decision-level fusion, calculates a separate classification or regression model from each data source and combines the results of each separate model to obtain the final decision [18]. This method can not only improve the model's accuracy, but also improves the model's stability [19; 20; 21]. [PERSON], [PERSON] et al. (2016).