Prediction of Beef Production Using Linear Regression, Random Forest and k-Nearest Neighbors Algorithms

Berkant İsmail Yıldız; Kemal Karabağ

doi:10.18016/ksutarimdoga.vi.1548951

TR EN

Doğrusal Regresyon, Rastgele Orman ve k-En Yakın Komşu Algoritmaları Kullanılarak Sığır Eti Üretiminin Tahmin Edilmesi

Öz

Küresel nüfusun hızla artması ve değişen beslenme alışkanlıkları, yüksek kaliteli protein kaynaklarına olan talebi önemli ölçüde artırmıştır. Önemli bir protein kaynağı olan sığır eti, bu artan talebin karşılanmasında kritik bir rol oynamaktadır. Bu çalışma, meteorolojik, tarımsal ve ekonomik veriler kullanarak sığır eti üretimini tahmin etmek için bir makine öğrenimi modeli geliştirmeyi ve değerlendirmeyi amaçlamaktadır. Bu amacı gerçekleştirmek için, üç farklı makine öğrenmesi algoritması—Doğrusal Regresyon, Rastgele Orman ve k-En Yakın Komşu—kullanılmıştır. Sonuçlar, Rastgele Orman algoritmasının R² ve hata metrikleri açısından diğer yöntemlerden daha iyi performans gösterdiğini ve üstün tahmin doğruluğu sağladığını göstermektedir. Çalışma, sığır eti üretiminin tahmin edilmesinde makine öğrenimi tekniklerinin potansiyelini vurgulamakta ve beslenme ihtiyaçlarını karşılamak için stratejik karar alma süreçlerine dahil olan paydaşlar için değerli bilgiler sunmaktadır. Küresel protein talebinin artmaya devam etmesiyle, bu tür tahmin modellerinin önemi giderek daha belirgin hale gelmekte ve makine öğrenmesi yaklaşımlarının bu bağlamda sunduğu belirgin avantajları öne çıkarmaktadır

Anahtar Kelimeler

Prediction of Beef Production Using Linear Regression, Random Forest and k-Nearest Neighbors Algorithms

Abstract

The rapid increase in the global population and evolving dietary habits have significantly heightened the demand for high-quality protein sources. Beef, as a vital protein source, plays a crucial role in meeting this growing demand. This study aims to develop and evaluate a machine-learning model to predict beef production using meteorological, agricultural, and economic data. To achieve this, three different machine learning algorithms—Linear Regression, Random Forest, and k-Nearest Neighbors—were employed. The results indicate that the Random Forest algorithm outperformed the other methods in terms of R² and error metrics, demonstrating superior predictive accuracy. The study highlights the potential of machine learning techniques in predicting beef production, offering valuable insights for stakeholders involved in strategic decision-making to meet nutritional needs. As the global demand for protein continues to rise, the importance of such predictive models becomes increasingly significant, emphasizing the distinct advantages that machine learning approaches provide in this context.

Keywords

Destekleyen Kurum

Bu çalışma herhangi bir kurum veya kuruluş tarafından maddi destek almamıştır.

Proje Numarası

Çalışma herhangi bir proje ile desteklenmemiştir.

Etik Beyan

Çalışmada insan veya hayvan denekleri kullanılmamış olup, etik kurul onayı gerektiren bir durum söz konusu değildir. Çalışmanın tüm aşamalarında bilimsel araştırma ve yayın etiği ilkelerine riayet edilmiştir.

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Tarımsal Biyoteknoloji (Diğer) , Zootekni, Genetik ve Biyoistatistik

Bölüm

Araştırma Makalesi

Yazarlar

Berkant İsmail Yıldız
0000-0001-8965-6361
Türkiye

Kemal Karabağ ^*
0000-0002-4516-6480
Türkiye

Erken Görünüm Tarihi

30 Ocak 2025

Yayımlanma Tarihi

12 Şubat 2025

Gönderilme Tarihi

12 Eylül 2024

Kabul Tarihi

20 Aralık 2024

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 1

DOI

https://doi.org/10.18016/ksutarimdoga.vi.1548951

IZ

https://izlik.org/JA68FK39YK

Kaynak Göster

RIS / Bibtex

APA

Yıldız, B. İ., & Karabağ, K. (2025). Prediction of Beef Production Using Linear Regression, Random Forest and k-Nearest Neighbors Algorithms. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 28(1), 247-255. https://doi.org/10.18016/ksutarimdoga.vi.1548951