Research on Detection of Fertility of Group Hatching Eggs Based on Adaptive Image Segmentation

Abstract

HighlightsAn adaptive group eggs segmentation was proposed, which separates a single egg from the group eggs image.Key color features and texture features were extracted from the RGB and HSI histograms, respectively.The Support Vector Machine (SVM) model and Least Squares Support Vector Machine (LS-SVM) model were built to distinguish infertile egg and dead-embryo egg, respectively.LS-SVM model reached 100% accuracy for infertile eggs on day 4 of incubation, and dead-embryo eggs on day 10 of incubation.Abstract. For the incubation factory, it is of vital importance to detect infertile eggs and dead-embryo eggs in the industrial egg trays as early as possible. In this article, an activity detection computer vision system was proposed and evaluated. Due to the dense layout of eggs in industrial egg trays, the image segmentation task to separate each single egg becomes difficult. To this end, an adaptive image segmentation method for group eggs was proposed. Firstly, the binary image was obtained by the Canny operator using dynamic threshold and processed to reduce redundant information. Then ellipse fitting was employed to obtain the egg contour of the single egg. Moreover, the Red, Green, Blue (RGB) and Hue, Saturation, Intensity (HSI) histograms were selected for feature extraction. According to the analysis on color features and texture features of the images, 13 features [positions for peak values of R and I in histogram, peak values of G and I within histogram, averages of R and G, variances of R and G, contrast, roughness, inverse different moment (IDM), correlation and angular second moment (ASM)] were chosen as the criterion for detecting dead-embryo eggs. Meanwhile, 12 features (positions for peak values of R, G, H, and I in histogram, averages of R and G, slope and variance of G, contrast, roughness, IDM, and correlation) were selected for detecting infertile eggs. Lastly, the Support Vector Machine (SVM) model and Lease Squares Support Vector Machine (LS-SVM) model were built to distinguish infertile egg and dead-embryo egg, respectively. According to the comparison, the LS-SVM model reached higher accuracy in determining infertile egg and dead-embryo egg than the SVM model, with less time consumed. The LS-SVM model reached 100% accuracy in detection for infertile eggs on day 4 of incubation, as well as for dead-embryo eggs on day 10 of incubation. The result demonstrated that the proposed method can conduct the activity detection for group eggs both accurately and fast, which meets the commercial requirement for non-destructive detection in the hatchery industry. Keywords: Adaptive image segmentation, Computer vision, Fertility, Group hatching eggs, LS-SVM, Non-destructive detection, SVM