Artificial Intelligence-Assisted Selection Strategies in Sheep: Linking Reproductive Traits with Behavioral Indicators

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dc.contributor.authorEmsen, Ebru
dc.contributor.authorKutluca Korkmaz, Muzeyyen
dc.contributor.authorOdevci, Bahadir Baran
dc.date.accessioned2026-06-19T06:37:54Z
dc.date.available2026-06-19T06:37:54Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractReproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning (ML) algorithms, offer new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes the current research on AI-powered behavioral monitoring tools and proposes a conceptual model, ReproBehaviorNet, that maps age- and sex-specific behaviors to biological processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring, leading to earlier detection of reproductive events and greater breeding precision. However, the implementation of such technologies also presents challenges, including the need for high-quality data, a costly infrastructure, and technical expertise that may limit access for small-scale producers. Despite these barriers, AI-assisted behavioral phenotyping has the potential to improve genetic progress, animal welfare, and sustainability. Interdisciplinary collaboration and responsible innovation are essential to ensure the equitable and effective adoption of these technologies in diverse farming contexts.
dc.description.sponsorshipStart-Up Project-UAEU [12F079]
dc.description.sponsorshipThis research was partly funded by Start-Up Project-UAEU (12F079), Flock Forward: Integrative PLF and Image Analysis for Enhanced Reproductive and Welfare Outcomes in Sheep Breeding.
dc.identifier.doi10.3390/ani15142110
dc.identifier.issn2076-2615
dc.identifier.issue14
dc.identifier.orcid0000-0003-1791-2535
dc.identifier.orcid0000-0001-5222-4223
dc.identifier.pmid40723573
dc.identifier.scopus2-s2.0-105011617511
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.3390/ani15142110
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5272
dc.identifier.volume15
dc.identifier.wosWOS:001549301800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofAnimals
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectArtificial Intelligence
dc.subjectBehavioral Phenotyping
dc.subjectReproductive Traits
dc.subjectSheep
dc.subjectSelection Strategies
dc.subjectMachine Learning
dc.subjectLivestock Technology
dc.titleArtificial Intelligence-Assisted Selection Strategies in Sheep: Linking Reproductive Traits with Behavioral Indicators
dc.typeReview

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