Identification of double-yolked duck egg using computer vision

<div><p>The double-yolked (DY) egg is quite popular in some Asian countries because it is considered as a sign of good luck, however, the double yolk is one of the reasons why these eggs fail to hatch. The usage of automatic methods for identifying DY eggs can increase the efficiency in the poultry industry by decreasing egg loss during incubation or improving sale proceeds. In this study, two methods for DY duck egg identification were developed by using computer vision technology. Transmittance images of DY and single-yolked (SY) duck eggs were acquired by a CCD camera to identify them according to their shape features. The Fisher’s linear discriminant (FLD) model equipped with a set of normalized Fourier descriptors (NFDs) extracted from the acquired images and the convolutional neural network (CNN) model using primary preprocessed images were built to recognize duck egg yolk types. The classification accuracies of the FLD model for SY and DY eggs were 100% and 93.2% respectively, while the classification accuracies of the CNN model for SY and DY eggs were 98% and 98.8% respectively. The CNN-based algorithm took about 0.12 s to recognize one sample image, which was slightly faster than the FLD-based (about 0.20 s). Finally, this work compared two classification methods and provided the better method for DY egg identification.</p></div

Principal component images based on the entire spectral region.

<p>(a-c) first three principle component (PC1, PC2 and PC3) images; (d) selection of optimal wavelengths from eigenvectors of PC1 image.</p

Binary images illustrating of image morphologic characteristics extraction analysis for eggs of different groups.

<p>a. egg with non developed embryo. b. egg with embryo development. c. egg with weak developed embryo.</p

Flow chart of morphological characteristics extraction algorithm.

<p>(a) ROI image. (b) Threshold segmentation. (c) Subtraction operation. (d) Eggshell boundary removal.</p

Reconstruction error versus the number of FDs.

<p>Reconstruction error versus the number of FDs.</p

Gray level images illustrating sample presentation of the evolution of embryo development for eggs of different groups.

<p>a. egg with non developed embryo. b. egg with embryo development. c. egg with weak developed embryo.</p

Recognition results of training and testing sets of egg samples by LVQ neural network based on spectral characteristic parameter.

<p>Recognition results of training and testing sets of egg samples by LVQ neural network based on spectral characteristic parameter.</p

Flow chart of DY egg identification algorithms.

<p>(a) The FLD-based algorithm. (b) The CNN-based algorithm.</p

Classification results for duck egg yolk type using FLD.

<p>Classification results for duck egg yolk type using FLD.</p

Typical yolk image with two separate yolk regions.

<p>(a) Original color image. (b) Yolk binary image. (c) Yolk boundary image using the method described by Gonzalez [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190054#pone.0190054.ref030" target="_blank">30</a>].</p