Abstract: It is one of difficult problems to be resolved in egg hatching industry to identify the fertile information of hatching eggs and eliminate infertile eggs prior to the incubation. Many infertile eggs have been wasted in the process of incubation every year, which has caused considerable economic loss. The existing domestic infertile egg detection mainly depends on traditional manual candle method. However, this detection method requires high intensity of labor and is time-consuming. In addition, the result of detection is subjective and its accuracy can not be guaranteed. The detection of infertile eggs prior to incubation can improve the economic efficiency of incubation and the quality of egg processing in late period, and it can bring considerable economic benefits. This paper proposed that the hyperspectral imaging technology consisting of image and spectral information and the relevance vector machine (RVM) were used for detecting the fertile information of eggs before incubation. To build a hyperspectral transmission image acquisition system, the light source, the light intensity, the resolution, the exposure time, the platform moving speed and other parameters were adjusted when the images of hyperspectral instrument were captured. Ultimately, the exposure time of the camera was determined as 0.1 s, the resolution of image as 400×400 pixels, and the platform moving speed as 1.7 mm/s. Before hatching eggs incubation, hyperspectral images system was used to acquire the images of hatching eggs between 400 and 1000 nm. The characteristic information of the ratios of length to short axis, the elongation, the roundness and the ratios of the yolk area to the whole area was extracted based on the images. Based on the comparison of the calibration results among 3 waveband regions (400-760, 760-1000, and 400-1000 nm), the visible light in band range of 400-760 nm was chosen to classify actual type of hatching eggs. Different spectra pretreatment methods were used to analyze the spectra, e.g. multiplicative scatter correction (MSC), normalize, standard normal variate transformation (SNV), first derivative (FD), MSC+FD, SNV+FD, normalize+FD, among which the normalized pretreatment method was the most effective, and its classification accuracy was better than other methods. The normalization method was used as the spectral data preprocessing, and then 155 spectral characteristic variables were extracted from 520 wavebands through the correlation coefficient method. Principal component analysis (PCA) method was adopted to reduce the dimension of image-spectrum fusion information which consisted of 4 image characteristic variables and 155 spectral characteristic variables, and then the top 6 principal components were extracted. According to the distribution principle of 2:1 for 300 hatching eggs, the numbers of eggs for training set and testing set were 200 and 100 respectively. RVM and support vector machine (SVM) were used to establish classification models, which were based on image, spectrum and image-spectrum fusion information respectively. The accuracies of the RVM models were 90%, 91% and 96% respectively, while the accuracies of the SVM models were 84%, 90% and 93% respectively. The cost time of the RVM models was 0.6619, 1.0821 and 0.5016 s respectively, while that the SVM models was 5.9386, 5.9886 and 5.6672 s respectively. The experimental results showed that the model based on image-spectrum fusion information was better than the single information model; the RVM model was superior to the SVM model for detecting fertile information of hatching eggs before incubation; and the cost time of RVM model was shorter than that of SVM model. The fertile and infertile eggs were identified very quickly. This project implementing would provide theoretical basis for the real-time online detection and testing of hating eggs for the instrument. Thus using hyperspectral fusion information and RVM can improve the detection accuracy of hatching eggs before incubation.