Classification of Hatchery Eggs Using a Machine Learning Algorithm Based on Image Processing Methods: A Comparative Study

Abstract

ABSTRACT Eggs are a cornerstone of the food industry. They are a versatile ingredient used in a wide variety of products for their rich protein, vitamin and mineral contents. The use of efficient and high-quality eggs is of great importance in hatcheries, as well as in direct food consumption. The use of quality and efficient eggs in hatcheries has a strong impact on chick, egg, and white meat production. Artificial intelligence-based smart systems usage for product quality classification is growing steadily in productive sectors. In many of these systems, product images are used as input data. The use of such smart systems provides both fast and low-error quality control. Smart systems can quickly and accurately classify new products with algorithms trained by product images. In this study, an intelligent classification system using a machine learning algorithm, which is a subfield of artificial intelligence, was designed to classify the quality and efficiency of chick eggs in a chicken hatchery. Eggs are most commonly classified according to their size as either Large (L), Medium (M) or Small (S). In this study, 425 egg images were obtained using the image acquisition system designed on the hatchery belt system, and the data for each egg was recorded in a dataset. In the next stage, image processing methods (Morphological operations and Hough Transform) and the SVM machine learning algorithm were used together in the proposed model. According to our results, the classification of eggs into L, M, and S was successfully achieved at 98.0% using the SVM algorithm on the dataset.

Keywords

• Egg production
• egg classification
• white meat technology
• image processing
• machine learning