  The quality of the hatching process influences the success of the hatch rate
besides the inherent egg factors. Eliminating infertile or dead eggs and
monitoring embryonic growth are very important factors in efficient hatchery
practices. This process aims to sort eggs that only have embryos to remain in
the incubator until the end of the hatching process. This process aims to sort
eggs with 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 and processed by segmentation. Egg images are segmented using
the K-means algorithm based on Lab color images. The results of the image
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 and divides into three parts: background, eggs, and yolk. Egg
yolks 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 initial phase results 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 results, the initial stages of the embryo detection process used
K-means segmentation based on Lab color space. The evaluation uses MSE and
MSSIM, with values of 0.0486 and 0.9979; this can be used to reference that the
results obtained can detect embryos in egg yolk. This protocol could be used in
a non-destructive quantitative study on embryos and their morphology in a
precision poultry production system in the future.
