[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127778-en":3,"doc-seo-127778-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127778,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Classification of Hatchery Eggs Using a Machine Learning Algorithm Based on Image Processing Methods - A Comparative Study","Eggs serve as a core ingredient in the food industry, making efficient and high-quality hatchery eggs essential for reliable chick, egg, and white meat production. Smart quality-control systems increasingly rely on product images and machine learning to achieve rapid, low-error classification. This study designs an AI-based classification system to evaluate chick egg quality and efficiency using image processing and SVM, achieving accurate L/M/S separation.","Brazilian Journal of Poultry Science Revista Brasileira de Ciência Avícola  \ne-ISSN: 1806-9061 2024 / v.26 / n.2 / 001-012  \n[http://dx.doi.org/10.1590/1806-9061-2023-1882](http://dx.doi.org/10.1590/1806-9061-2023-1882)  \nOriginal Article  \nClassification of Hatchery Eggs Using a Machine Learning Algorithm Based on Image Processing Methods: A Comparative Study  \n􀂄Author(s)  \nÇelik AI [https://orcid.org/0000-0002-6288-3182](https://orcid.org/0000-0002-6288-3182)  \nTekin EI [https://orcid.org/0000-0003-2189-9265](https://orcid.org/0000-0003-2189-9265)  \nI Tavşanlı Vocational School, Kütahya Dumlupınar University, Kütahya, Turkey.  \nII Faculty of Engineering, Computer Engineering, Kütahya Dumlupınar University, Kütahya, Turkey.  \n􀂄Mail Address  \nCorresponding author e-mail address Ahmet Çelik  \nTavşanlı Vocational School, Kütahya Dumlupınar University, Kütahya, 43300, Turkey.  \nPhone: +90 2744436745  \nEmail: [ahmet.celik@dpu.edu.tr](ahmet.celik@dpu.edu.tr)  \n􀂄Keywords  \nEgg production, egg classification, white meat technology, image processing, machine learning.  \nSubmitted: 15/November/2023  \nApproved: 19/March/2024  \nABSTRACT  \nEggs 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 byproduct 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 adataset. 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.  \nINTRODUCTION  \nChicken eggs are one of the most widely consumed foods by humans (Rachmawanto etal., 2020) . As the need for egg consumption increases due to the increase in the world population, chicken egg production must follow this trend. Chicken eggs are also one of the basic nutrient sources needed for human life, and so their production must be sustainable and fulfill the demand.  \nEfficient egg-laying hens produce a higher quantity of eggs of higher quality. Hatcheries serve as chicken production centers. The use of efficient eggs in hatcheries leads to high-quality and efficient chicks (Tekin, 2023), producing efficient egg-laying hens or broilers that will yield more eggs and white meat. Measuring the weight of chicken eggs and evaluating their size have a strong impact on egg classification. Accurate classification helps to increase the efficiency of chicken production and produce eggs of better quality (Rahmat et al., 2023) .  \nToday’s high technologies, such as the Internet of Things (IoT), various optical sensors, robotics, artificial intelligence (AI), big data processing, and cloud computing methods have transformed the traditional industry into a smart and sustainable egg industry, now also  \n1  \neRBCA-2023-1882  \nÇelikA, ","cbCaihlr5dz1i4EN","https://ap.wps.com/l/cbCaihlr5dz1i4EN","pdf",1452475,1,12,"English","en",105,"# Abstract\n# Introduction\n# Material and Methods","[{\"question\":\"What problem does the study address in hatcheries?\",\"answer\":\"It addresses the need to classify chick eggs efficiently and accurately, since manual inspection increases error and delays when egg numbers are high.\"},{\"question\":\"How were the egg images and dataset created?\",\"answer\":\"The study acquired 425 egg images using an image acquisition system on the hatchery belt, then recorded data for each egg to build a dataset.\"},{\"question\":\"Which methods were combined to classify egg quality and size?\",\"answer\":\"The proposed model used image processing techniques (Morphological operations and Hough Transform) together with an SVM machine learning algorithm.\"}]","Classification of Hatchery Eggs Using a Machine Learning Algorithm Based on Image Processing Methods - 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