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

Year 2025, Volume: 28 Issue: 1, 247 - 255, 12.02.2025

Berkant İsmail Yıldız , Kemal Karabağ

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

Abstract

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

Keywords

Sığır eti üretimi Sığır eti Üretim tahmini Makine öğrenimi Yapay zekâ

Ethical Statement

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.

Supporting Institution

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

Project Number

Çalışma herhangi bir proje ile desteklenmemiştir.

Thanks

-

References

  • Ahmed, M. U. & Hussain, I. (2022). Prediction of wheat production using machine learning algorithms in northern areas of Pakistan. Telecommunications Policy, 46, 102370. https://doi.org/10.1016/j.telpol.2022.102370.
  • Alonso, J., Castañón, Á. R. & Bahamonde, A. (2013). Support Vector Regression to predict carcass weight in beef cattle in advance of the slaughter. Computers and Electronics in Agriculture, 91, 116-120. https://doi.org/10.1016/j.compag.2012.08.009.
  • Alsahaf, A., Azzopardi, G., Ducro, B., Veerkamp, R. F. & Petkov, N. (2018). Predicting slaughter weight in pigs with regression tree ensembles. Frontiers in Artificial Intelligence and Applications, 310, 1-9. https://doi.org/10.3233/978-1-61499-929-4-1.
  • Bharadiya, J. P., Tzenios, N. T. & Reddy, M. (2023). Forecasting of crop yield using remote sensing data, agrarian factors and machine learning approaches. Journal of Engineering Research and Reports, 24(12), 29-44.
  • Bhardwaj, P., Kumar, S. J. K. J., Kanna, G. P. & Mithila, A. (2024). Machine learning-based approaches for livestock symptoms and diseases prediction and classification. International Conference on Communication, Computer Sciences and Engineering (IC3SE), Gautam Buddha Nagar, India, 2024, pp. 1-6.
  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324. Ching, X. L., Zainal, N. A. A. B., Luang-In, V. & Ma, N. L. (2022). Lab-based meat: The future food. Environmental Advances, 10, 100315. https://doi.org/10.1016/j.envadv.2022.100315.
  • Coşkun, G., Şahin, Ö., Altay, Y. & Aytekin, İ. (2023). Final fattening live weight prediction in Anatolian merinos lambs from some body characteristics at the initial of fattening by using some data mining algorithms. Black Sea Journal of Agriculture, 6(1), 47-53. https://doi.org/10.47115/bsagriculture.1181444.
  • Çakan, V. A. & Tipi, T. (2023). How does the change in feed prices affect meat prices? A case study of Turkey. Atatürk Üniversitesi Ziraat Fakültesi Dergisi, 54(2), 68-74. https://doi.org/10.5152/AUAF.2023.22054.
  • FAO. (2024a). Global and regional food consumption patterns and trends. Food and Agriculture Organization of the United Nations. https://www.fao.org/4/ac911e/ac911e05.htm. (Accessed Date: 12 November 2024).
  • FAO. (2024b). FAOSTAT, demographic and economic and political stability data of Türkiye. https://www.fao.org/faostat/en/#country/223. (Accessed Date: 4 November 2024).
  • GDM. (2024). General Directorate of Meteorology, official climate statistics of Türkiye. https://www.mgm.gov.tr/veridegerlendirme. (Accessed Date: 4 November 2024).
  • Godfray, H. C. J., Aveyard, P., Garnett, T., Hall, J. W., Key, T. J., Lorimer, J., Pierrehumbert, R. T., Scarborough, P., Springmann, M. & Jebb, S. A. (2018). Meat consumption, health, and the environment. Science, 361(6399), eaam5324. https://doi.org/10.1126/science.aam5324.
  • Henchion, M., Hayes, M., Mullen, A. M., Fenelon, M. & Tiwari, B. (2017). Future protein supply and demand: Strategies and factors influencing a sustainable equilibrium. Foods, 6(7), 53. https://doi.org/10.3390/foods6070053.
  • Humer, E. & Zebeli, Q. (2017). Grains in ruminant feeding and potentials to enhance their nutritive and health value by chemical processing. Animal Feed Science and Technology, 226, 133-151. https://doi.org/10.1016/j.anifeedsci.2017.02.005.
  • Klopfenstein, T. J., Erickson, G. E. & Berger, L. L. (2013). Maize is a critically important source of food, feed, energy and forage in the USA. Field Crops Research, 153, 5-11. https://doi.org/10.1016/j.fcr.2012.11.006.
  • Kononenko, I. (2001). Machine learning for medical diagnosis: History, state of the art and perspective. Artificial Intelligence in Medicine, 23, 89-109. https://doi.org/10.1016/s0933-3657(01)00077-x.
  • Li, C. (2017). The role of beef in human nutrition and health. In M. Dikeman (Ed.), Ensuring safety and quality in the production of beef (pp. 1-10). Burleigh Dodds Science Publishing. https://doi.org/10.19103/AS.2016.0009.16.
  • Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S. & Sun, Y. (2021). Combination of feature selection and catboost for prediction: The first application to the estimation of aboveground biomass. Forests, 12(2), 216. https://doi.org/10.3390/f12020216.
  • Marshall, B. M. & Levy, S. B. (2011). Food animals and antimicrobials: Impacts on human health. Clinical Microbiology Reviews, 24(4), 718-733.
  • Mishra, T. K., Mishra, S. K., Sai, K. J., Alekhya, B. S. & Nishith, A. R. (2021). Crop recommendation system using KNN and random forest considering Indian data set. 19th OITS International Conference on Information Technology (OCIT), Bhubaneswar, India, December 2021, pp. 308-312.
  • Nosratabadi, S., Ardabili, S., Lakner, Z., Mako, C. & Mosavi, A. (2021). Prediction of food production using machine learning algorithms of multilayer perceptron and ANFIS. Agriculture, 11(5), 408. https://doi.org/10.3390/agriculture11050408.
  • Patel, K. & Patel, H. B. (2021). A comparative analysis of supervised machine learning algorithm for agriculture crop prediction. Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Tamil Nadu, India, September 2021, pp. 1-5.
  • Python Software Foundation. (2024). The Python language version 3.12.2.
  • Rahman, L. F., Marufuzzaman, M., Alam, L., Bari, M. A., Sumaila, U. R. & Sidek, L. M. (2021). Developing an ensembled machine learning prediction model for marine fish and aquaculture production. Sustainability, 13, 9124. https://doi.org/10.3390/su13169124.
  • Ruxton, C. H. S. & Gordon, S. (2024). Animal board invited review: The contribution of red meat to adult nutrition and health beyond protein. Animal, 18(3). https://doi.org/10.1016/j.animal.2024.101103.
  • Srivastava, S., Lopez, B. I., Kumar, H., Jang, M., Chai, H. H., Park, W., Park, J. E. & Lim, D. (2021). Prediction of Hanwoo cattle phenotypes from genotypes using machine learning methods. Animals, 11(7), 2066. https://doi.org/10.3390/ani11072066.
  • Srutee, R. Sowmya, R. S. & Annapure, U. S. (2022). Clean meat: Techniques for meat production and its upcoming challenges. Animal Biotechnology, 33(7), 1721-1729. https://doi.org/10.1080/10495398.2021.1911810.
  • TMAF. (2024). Turkish Ministry Agriculture and Forestry, agricultural data information center. https://www.tarimorman.gov.tr/Konular/ (Accessed date: 4 November 2024).
  • Van Kernebeek, H. R., Oosting, S. J., Van Ittersum, M. K., Bikker, P., & De Boer, I. J. (2016). Saving land to feed a growing population: Consequences for consumption of crop and livestock products. The International Journal of Life Cycle Assessment, 21(5), 677-687. https://doi.org/10.1007/s11367-015-0923-6.
  • Yıldız, B. İ., Eskioğlu, K. & Karabağ, K. (2024). Developing a machine learning prediction model for honey production. Mediterranean Agricultural Sciences, 37(2), 105-110. https://doi.org/10.29136/ mediterranean.1511697.

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

Year 2025, Volume: 28 Issue: 1, 247 - 255, 12.02.2025

Berkant İsmail Yıldız , Kemal Karabağ

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

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

Beef Production estimate Machine learning Artificial intelligence Beef production

Project Number

Çalışma herhangi bir proje ile desteklenmemiştir.

References

  • Ahmed, M. U. & Hussain, I. (2022). Prediction of wheat production using machine learning algorithms in northern areas of Pakistan. Telecommunications Policy, 46, 102370. https://doi.org/10.1016/j.telpol.2022.102370.
  • Alonso, J., Castañón, Á. R. & Bahamonde, A. (2013). Support Vector Regression to predict carcass weight in beef cattle in advance of the slaughter. Computers and Electronics in Agriculture, 91, 116-120. https://doi.org/10.1016/j.compag.2012.08.009.
  • Alsahaf, A., Azzopardi, G., Ducro, B., Veerkamp, R. F. & Petkov, N. (2018). Predicting slaughter weight in pigs with regression tree ensembles. Frontiers in Artificial Intelligence and Applications, 310, 1-9. https://doi.org/10.3233/978-1-61499-929-4-1.
  • Bharadiya, J. P., Tzenios, N. T. & Reddy, M. (2023). Forecasting of crop yield using remote sensing data, agrarian factors and machine learning approaches. Journal of Engineering Research and Reports, 24(12), 29-44.
  • Bhardwaj, P., Kumar, S. J. K. J., Kanna, G. P. & Mithila, A. (2024). Machine learning-based approaches for livestock symptoms and diseases prediction and classification. International Conference on Communication, Computer Sciences and Engineering (IC3SE), Gautam Buddha Nagar, India, 2024, pp. 1-6.
  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324. Ching, X. L., Zainal, N. A. A. B., Luang-In, V. & Ma, N. L. (2022). Lab-based meat: The future food. Environmental Advances, 10, 100315. https://doi.org/10.1016/j.envadv.2022.100315.
  • Coşkun, G., Şahin, Ö., Altay, Y. & Aytekin, İ. (2023). Final fattening live weight prediction in Anatolian merinos lambs from some body characteristics at the initial of fattening by using some data mining algorithms. Black Sea Journal of Agriculture, 6(1), 47-53. https://doi.org/10.47115/bsagriculture.1181444.
  • Çakan, V. A. & Tipi, T. (2023). How does the change in feed prices affect meat prices? A case study of Turkey. Atatürk Üniversitesi Ziraat Fakültesi Dergisi, 54(2), 68-74. https://doi.org/10.5152/AUAF.2023.22054.
  • FAO. (2024a). Global and regional food consumption patterns and trends. Food and Agriculture Organization of the United Nations. https://www.fao.org/4/ac911e/ac911e05.htm. (Accessed Date: 12 November 2024).
  • FAO. (2024b). FAOSTAT, demographic and economic and political stability data of Türkiye. https://www.fao.org/faostat/en/#country/223. (Accessed Date: 4 November 2024).
  • GDM. (2024). General Directorate of Meteorology, official climate statistics of Türkiye. https://www.mgm.gov.tr/veridegerlendirme. (Accessed Date: 4 November 2024).
  • Godfray, H. C. J., Aveyard, P., Garnett, T., Hall, J. W., Key, T. J., Lorimer, J., Pierrehumbert, R. T., Scarborough, P., Springmann, M. & Jebb, S. A. (2018). Meat consumption, health, and the environment. Science, 361(6399), eaam5324. https://doi.org/10.1126/science.aam5324.
  • Henchion, M., Hayes, M., Mullen, A. M., Fenelon, M. & Tiwari, B. (2017). Future protein supply and demand: Strategies and factors influencing a sustainable equilibrium. Foods, 6(7), 53. https://doi.org/10.3390/foods6070053.
  • Humer, E. & Zebeli, Q. (2017). Grains in ruminant feeding and potentials to enhance their nutritive and health value by chemical processing. Animal Feed Science and Technology, 226, 133-151. https://doi.org/10.1016/j.anifeedsci.2017.02.005.
  • Klopfenstein, T. J., Erickson, G. E. & Berger, L. L. (2013). Maize is a critically important source of food, feed, energy and forage in the USA. Field Crops Research, 153, 5-11. https://doi.org/10.1016/j.fcr.2012.11.006.
  • Kononenko, I. (2001). Machine learning for medical diagnosis: History, state of the art and perspective. Artificial Intelligence in Medicine, 23, 89-109. https://doi.org/10.1016/s0933-3657(01)00077-x.
  • Li, C. (2017). The role of beef in human nutrition and health. In M. Dikeman (Ed.), Ensuring safety and quality in the production of beef (pp. 1-10). Burleigh Dodds Science Publishing. https://doi.org/10.19103/AS.2016.0009.16.
  • Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S. & Sun, Y. (2021). Combination of feature selection and catboost for prediction: The first application to the estimation of aboveground biomass. Forests, 12(2), 216. https://doi.org/10.3390/f12020216.
  • Marshall, B. M. & Levy, S. B. (2011). Food animals and antimicrobials: Impacts on human health. Clinical Microbiology Reviews, 24(4), 718-733.
  • Mishra, T. K., Mishra, S. K., Sai, K. J., Alekhya, B. S. & Nishith, A. R. (2021). Crop recommendation system using KNN and random forest considering Indian data set. 19th OITS International Conference on Information Technology (OCIT), Bhubaneswar, India, December 2021, pp. 308-312.
  • Nosratabadi, S., Ardabili, S., Lakner, Z., Mako, C. & Mosavi, A. (2021). Prediction of food production using machine learning algorithms of multilayer perceptron and ANFIS. Agriculture, 11(5), 408. https://doi.org/10.3390/agriculture11050408.
  • Patel, K. & Patel, H. B. (2021). A comparative analysis of supervised machine learning algorithm for agriculture crop prediction. Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Tamil Nadu, India, September 2021, pp. 1-5.
  • Python Software Foundation. (2024). The Python language version 3.12.2.
  • Rahman, L. F., Marufuzzaman, M., Alam, L., Bari, M. A., Sumaila, U. R. & Sidek, L. M. (2021). Developing an ensembled machine learning prediction model for marine fish and aquaculture production. Sustainability, 13, 9124. https://doi.org/10.3390/su13169124.
  • Ruxton, C. H. S. & Gordon, S. (2024). Animal board invited review: The contribution of red meat to adult nutrition and health beyond protein. Animal, 18(3). https://doi.org/10.1016/j.animal.2024.101103.
  • Srivastava, S., Lopez, B. I., Kumar, H., Jang, M., Chai, H. H., Park, W., Park, J. E. & Lim, D. (2021). Prediction of Hanwoo cattle phenotypes from genotypes using machine learning methods. Animals, 11(7), 2066. https://doi.org/10.3390/ani11072066.
  • Srutee, R. Sowmya, R. S. & Annapure, U. S. (2022). Clean meat: Techniques for meat production and its upcoming challenges. Animal Biotechnology, 33(7), 1721-1729. https://doi.org/10.1080/10495398.2021.1911810.
  • TMAF. (2024). Turkish Ministry Agriculture and Forestry, agricultural data information center. https://www.tarimorman.gov.tr/Konular/ (Accessed date: 4 November 2024).
  • Van Kernebeek, H. R., Oosting, S. J., Van Ittersum, M. K., Bikker, P., & De Boer, I. J. (2016). Saving land to feed a growing population: Consequences for consumption of crop and livestock products. The International Journal of Life Cycle Assessment, 21(5), 677-687. https://doi.org/10.1007/s11367-015-0923-6.
  • Yıldız, B. İ., Eskioğlu, K. & Karabağ, K. (2024). Developing a machine learning prediction model for honey production. Mediterranean Agricultural Sciences, 37(2), 105-110. https://doi.org/10.29136/ mediterranean.1511697.
There are 30 citations in total.

Details

Primary Language English
Subjects Agricultural Biotechnology (Other), Animal Science, Genetics and Biostatistics
Journal Section RESEARCH ARTICLE
Authors

Berkant İsmail Yıldız 0000-0001-8965-6361

Kemal Karabağ 0000-0002-4516-6480

Project Number Çalışma herhangi bir proje ile desteklenmemiştir.
Early Pub Date January 30, 2025
Publication Date February 12, 2025
Submission Date September 12, 2024
Acceptance Date December 20, 2024
Published in Issue Year 2025 Volume: 28 Issue: 1

Cite

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
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