  The hatching process also influences the success of hatching eggs beside the
initial egg factor. So that the results have a large percentage of hatching, it
is necessary to check the development of the embryo at the beginning of the
hatching. This process aims to sort eggs that have embryos to remain hatched
until the end. Maximum checking is done the first week in the hatching period.
This study aims to detect the presence of embryos in eggs. Detection of the
existence of embryos is processed using segmentation. Egg images are segmented
using the K-means algorithm based on Lab color images. The results of the
images acquisition are converted into Lab color space images. The results of
Lab color space images are processed using K-means for each color. The K-means
process uses cluster k=3, where this cluster divided the image into three
parts, namely background, eggs, and yolk eggs. Yolk eggs are part of eggs that
have embryonic characteristics. This study applies the concept of color in the
initial segmentation and grayscale in the final stages. The results of the
initial phase show that the image segmentation results using k-means clustering
based on Lab color space provide a grouping of three parts. At the grayscale
image processing stage, the results of color image segmentation are processed
with grayscaling, image enhancement, and morphology. Thus, it seems clear that
the yolk segmented shows the presence of egg embryos. Based on this process and
results, K-means segmentation based on Lab color space can be used for the
initial stages of the embryo detection process. The evaluation uses MSE and
MSSIM, with values of 0.0486 and 0.9979; this can be used as a reference that
the results obtained can indicate the detection of embryos in egg yolk.
