Yapay Zekâ Tabanlı Otomatik Ahır İklim Yönetimi

Year 2025, Volume: 20 Issue: 2, 371 - 388, 30.09.2025

Asilhan Mehmet Nacar , Haydar Safa Kılıç , Ali Günlü , Zeynep Özpolat

https://doi.org/10.55525/tjst.1615709

Abstract

İklim değişikliğinin sebep olduğu yüksek hava sıcaklığı günlük yaşam rutinleri içerisinde birçok alanı etkilemektedir. Hayvancılık alanı için örnek vermek gerekirse süt sığırlarında ısı stresi durumunu ortaya çıkarmaktadır. Isı stresi hayvanların sağlığını ve süt verimliliğini düşürdüğü için bu işletmeler ahırlarda çeşitli serinletme ve iklimlendirme sistemleri kullanmaktadır. Ancak bu sistemler geleneksel olup akıllı kararlar alan bir mekanizmaya sahip değildir. Bu çalışmada işletmelerin verimliliklerini arttırmak ve sürdürülebilir bir yapı oluşturabilmek adına serinletme sistemlerine yapay zekâ destekli görüntü ve veri işleme modeli entegrasyonu üzerinde durulmaktadır. Yapay zekâ modeli sayesinde kurulu olan serinletme sistemleri daha duyarlı ve etkili hale geleceğinden, değişen ortam koşullarında daha doğru çalışan ve kaynak kullanımını azaltan bir serinletme sistemi sağlanması hedeflenmektedir. Serinletme sistemlerinin hayvanların verimlilikleri üzerindeki etkileri araştırılmakta ve bu sistemlerin daha etkili verimli kullanılabilmesi adına geliştirmeler yapılmaktadır. Sistemin yapay zekâ ve görüntü işleme modeli ile çalışması verimlilik açısından değerlendirilmektedir. Akıllı sistemlerin entegrasyonu sonucunda kaynak kullanımının azaltılması üzerinde durulmaktadır. Hayvan refahı ve sağlığının daha etkili ve az kaynak kullanımıyla sağlanması, serinletme sistemlerinin otomatize ve akıllı karar mekanizmalarıyla çalışması ve sonuç olarak işletme maliyetlerinin minimize edilerek kararlılıklarının arttırılması hedeflenmektedir. Bunun yanında gerçek zamanlı çalışan bir sistemin işletme üzerinde ne gibi avantajlara sahip olabileceği tartışılmaktadır. Hayvan sağlığı üzerinde gerçek zamanlı takibin hayvan davranışlarını inceleyerek anlık sistem tepkileri üretilmesinin faydaları araştırılmaktadır. Gerçekleştirilen testler sonucunda, sistemin ortalama 1.8 saniyelik tepki süresi ile çalıştığı, geleneksel sistemlere kıyasla %28 oranında su tasarrufu sağladığı ve inek tespitinde %91,2 mAP doğruluğu elde ettiği belirlenmiştir.

Keywords

Yapay zekâ , görüntü işleme , akıllı sistemler , hayvancılık , süt verimliliği

Artificial Intelligence-Based Automated Barn Climate Management

Abstract

High air temperatures caused by climate change affect various aspects of daily life, including the livestock sector. In dairy farming, heat stress in cows emerges as a major issue, reducing both animal health and milk productivity. To mitigate these effects, farmers commonly use barn cooling and climate control systems. However, these traditional systems lack intelligent decision-making mechanisms. This study focuses on the integration of an AI-powered image and data processing model into existing cooling systems to improve efficiency and establish a sustainable infrastructure. Through the use of artificial intelligence, the cooling systems are expected to operate more sensitively and efficiently, adapting to changing environmental conditions while minimizing resource usage. The impact of cooling systems on livestock productivity is analyzed, and improvements are proposed for more effective use. The integration of image processing and AI-based models is evaluated in terms of operational efficiency. Reducing resource usage through smart systems is emphasized as a key advantage. The system aims to ensure animal welfare and health with minimal resource consumption, by automating climate control systems and enabling intelligent decision-making processes. As a result, operational costs are expected to decrease and system stability to increase. Additionally, the study discusses the potential benefits of a real-time system for operational management. Real-time monitoring of animal behavior and automated system responses are explored for their potential to improve farm management. Experimental results showed that the proposed system achieved an average response time of 1.8 seconds, a 28% reduction in water consumption compared to traditional systems, and a cow detection accuracy of 91.2% mAP, confirming its effectiveness in real-time barn environments.

Keywords

Artificial intelligence , image processing , smart systems , livestock farming , milk productivity

Supporting Institution

KAZANCI HOLDİNG

Thanks

Ahmet Vefa Erdem, Çağrı Ata

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