Female and male identification of early chicken embryo based on blood line features of hatching egg image and deep belief networks

Abstract: Nondestructive testing is usually carried out in two directions from the visual features and intrinsic mechanism features of the object. In order to identify the early embryonic sex of chicken eggs, a machine vision image acquisition system was constructed in this study. Under the light source of LED, the development pattern of the egg embryo in different incubation period was collected, and the images of 180 chicken eggs were obtained in 3th, 4th, 5th, 6th, 8th and 10th day. According to the principle of the definition of blood line and blood line integrity in the field of vision of the machine vision, the image of 4th day of incubation was determined to be used to identify the male and female of the embryo. The preprocessing of chicken embryo egg image was carried out, such as component extraction, median filtering, and region of interest (ROI) extraction, followed by the use of contrast limited adaptive histogram equalization (CLAHE), morphological processing, threshold segmentation of Otsu and eight connected domain denoise method to highlight the blood line texture. Through the gray level co-occurrence matrix to extract 5 dimensional features and the direction of the gradient histogram (HOG) to extract 2916 dimensional full information image features. In order to reduce the computational complexity, the processed image was sampled, compressed to a size of 35 pixels × 35 pixels, and the full information features of the 1225 dimension were extracted. Finally, the simplified features of 96 dimensional which was combination of PCA dimensionality reduction--gray level co-occurrence matrix were used to construct three types of chicken embryo eggs male and female identification model which were support vector machine (SVM), back-propagation (BP) neural network, the deep belief networks (DBN). Also the full information features of 1225 dimensional which was combination of PCA dimensionality reduction-gray level co-occurrence matrix were used to construct three types of chicken embryo eggs male and female identification model which were support vector machine (SVM), back-propagation (BP) neural network, the deep belief networks (DBN). In the experiment, the information feature of the image was more accurate than that of the simplified feature in the same model, and the recognition accuracy of the whole information feature - DBN model was the highest, reaching 83.33%. Among them, the accuracy of male identification was 76.67% and that of female identification was 90%. The discrimination time of the three models was analyzed for the test set samples, the discriminant time of the three models was SVM, BP, DBN in order of shortest to longest. Correspondingly, the higher the dimension of the input features, the longer the discriminant time of the model, and finally with the highest recognition accuracy which was the full information feature -- the DBN model had the longest discriminant time, which was 7.8350s. The results showed that the machine vision technology provided a feasible method for sex determination of early hatching of chicken embryo eggs.