Deteksi Kualitas Telur Menggunakan Analisis Tekstur

Abstract

Currently to find out the quality of eggs was conducted on visual observation directly on the egg, both the outside of the egg in the form of eggshell conditions or the inside of the egg by watching out using sunlight or a flashlight. This method requires good accuracy, so in the process it can affect results that are not always accurate. This is due to the physical limitations of each individual is different. This study examines the utilization of digital image processing for the detection of egg quality using eggshell image. The feature extraction method performed  a texture feature based on the histogram that is the average intensity, standard deviation, skewness, energy, entropy, and smoothness properties. The detection method for  training and testing is  K-Means Clustering algorithm. The results of this application are able to help the user to determine the quality of good chicken eggs and good quality chicken eggs, with accurate introduction of good quality eggs by 90% and poor quality eggs by 80%.