[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118459-en":3,"doc-seo-118459-105":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},118459,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting Quail Egg Quality Using Machine Learning Algorithms","This study evaluates the effectiveness of machine learning algorithms for predicting quail egg quality using nine key internal and external parameters, including egg weight, egg dimensions, yolk and albumen measurements, and yolk and albumen weights. A dataset of 350 eggs from 18-week-old Japanese quails was modeled with Logistic Regression, Naive Bayes, Support Vector Machines, k-Nearest Neighbors, Random Forest, and Gradient Boosting. Models combining internal and external quality parameters produced significantly higher accuracy than external-only models. Random Forest and Gradient Boosting achieved over 97% accuracy, supporting scalable, rapid, and flexible non-invasive evaluation approaches for the poultry industry.","Brazilian Journal of Poultry Science Revista Brasileira de Ciência Avícola  \ne-ISSN: 1806-9061 2025 / v.27 / n.1 / 001-009  \n[http://dx.doi.org/10.1590/1806-9061-2024-2037](http://dx.doi.org/10.1590/1806-9061-2024-2037)  \nOriginal Article  \nPredicting Quail Egg Quality Using Machine Learning Algorithms  \n􀂄Author(s)  \nYildiz BII [https://orcid.org/0000-0001-8965-6361](https://orcid.org/0000-0001-8965-6361)  \nEskioğlu KI [https://orcid.org/0009-0003-5991-9003](https://orcid.org/0009-0003-5991-9003)  \nÖzdemir DI [https://orcid.org/0000-0003-2160-6485](https://orcid.org/0000-0003-2160-6485)  \nAkşit MII [https://orcid.org/0000-0002-8074-8208](https://orcid.org/0000-0002-8074-8208)  \nI Akdeniz University, Department of Agricultural Biotechnology, Antalya, Türkiye.  \nII Aydın Adnan Menderes University, Department of Animal Science, Aydın, Türkiye.  \n􀂄Mail Address  \nCorresponding author e-mail address Demir Özdemir  \nAkdeniz University, Department of Agricultural Biotechnology, Antalya, 07059, Türkiye.  \nPhone: +90 505 818 1279  \nEmail: [dozdemir@akdeniz.edu.tr](dozdemir@akdeniz.edu.tr)  \n􀂄Keywords  \nQuail eggs, egg quality, machine learning, quality prediction, predictive modeling.  \nSection Editor: Irenilza de Alencar Nääs  \nSubmitted: 30/November/2024  \nApproved: 05/February/2025  \nABSTRACT  \nThis study evaluates the effectiveness of machine learning algorithms in predicting quail egg quality based on nine key parameters, including egg weight, egg width, egg length, yolk height, yolk width, yolk weight, albumen height, albumen width, and albumen length. Adataset comprising 350 eggs from 18-week-old Japanese quails was analyzed using Logistic Regression, Naive Bayes, Support Vector Machines, k-Nearest Neighbors, Random Forest, and Gradient Boosting. The findings revealed that models combining internal and external quality parameters achieved significantly higher accuracy compared to models based solely on external attributes. Notably, Random Forest and Gradient Boosting algorithms achieved accuracies exceeding 97%, while predictions based only on external parameters exhibited lower accuracy but presented a promising starting point for non-invasive evaluations. This study strongly highlights the applicability and flexibility of machine learning in evaluating quail egg quality. The ability of algorithms to integrate various quality parameters and analyze complex relationships provides both rapid and scalable solutions. These findings demonstrate that machine learning technologies have the potential to drive innovative approaches in the poultry industry and inspire future research focusing on larger datasets and additional parameters to further enhance accuracy.  \nINTRODUCTION  \nEggs are a foundational component of diets globally, valued for their high-quality proteins, essential vitamins, and minerals. The rapid growth of the global population has led to increased demand for animal-based foods, underscoring the importance of modern livestock practices and intensive production systems in meeting these nutritional needs (FAO, 2015; Serraj & Pingali, 2018) . In this context, egg quality has become a critical factor, enhancing both the efficiency of production processes and the market value of egg products. Broadly, egg quality is assessed in two categories, internal and external. Internal quality parameters include yolk and albumen height, yolk width, yolk weight, albumen width, and albumen length, all of which reflect the egg’s structural and nutritional integrity. External quality parameters, such as egg weight, width, length, and shell thickness, contribute to the physical resilience and marketability of the egg (Dilawar et al., 2021) . These attributes playa pivotal role in optimizing production efficiency and influencing the egg’s market appeal. Moreover, egg quality impacts not only production outcomes and consumer satisfaction, but also hatchability rates, chick growth performance, and development metrics (Marks, 1975; Hurniket al","cbCaigujQag7578Y","https://ap.wps.com/l/cbCaigujQag7578Y","pdf",619562,1,9,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Which parameters are used to predict quail egg quality?\",\"answer\":\"The study uses nine parameters: egg weight, egg width, egg length, yolk height, yolk width, yolk weight, albumen height, albumen width, and albumen length.\"},{\"question\":\"What machine learning algorithms were evaluated?\",\"answer\":\"Logistic Regression, Naive Bayes, Support Vector Machines, k-Nearest Neighbors, Random Forest, and Gradient Boosting were used.\"},{\"question\":\"How does using internal plus external parameters affect prediction accuracy?\",\"answer\":\"Models that combine internal and external quality parameters achieve significantly higher accuracy than models based only on external attributes.\"}]","Predicting Quail Egg Quality Using Machine Learning Algorithms | PDF",1785683710,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-quail-egg-quality-using-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-quail-egg-quality-using-machine-learning-algorithms/118459/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which parameters are used to predict quail egg quality?","Question",{"text":75,"@type":76},"The study uses nine parameters: egg weight, egg width, egg length, yolk height, yolk width, yolk weight, albumen height, albumen width, and albumen length.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning algorithms were evaluated?",{"text":80,"@type":76},"Logistic Regression, Naive Bayes, Support Vector Machines, k-Nearest Neighbors, Random Forest, and Gradient Boosting were used.",{"name":82,"@type":73,"acceptedAnswer":83},"How does using internal plus external parameters affect prediction accuracy?",{"text":84,"@type":76},"Models that combine internal and external quality parameters achieve significantly higher accuracy than models based only on external attributes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]