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Süt Sığırlarının Buzağılama Zamanının Tahmininde Makine Öğrenme Yöntemlerinin Kullanımı Çalışmaları Üzerine Bir Değerlendirme

Year 2023, Volume: 2023 Issue: 18, 27 - 39, 03.07.2023

Abstract

Süt sığırlarının buzağılama zamanlarının tahmin edilmesindeki öneme odaklanan bu çalışmada, makine öğrenme yöntemlerinin kullanımını değerlendirilmektedir. Buzağılama zamanının tahmin edilmesi süt üretiminde önemli bir görevdir. Buzağılama zamanının erken tahmini, çiftçilerin inekleri özel bir buzağılama bölmesine ne zaman taşıyacakları veya yem miktarını ne zaman artıracakları gibi sürü yönetimi hakkında bilinçli kararlar almalarına yardımcı olabilmektedir. Çalışmada, süt sığırlarında buzağılama zamanını tahmin etmek için makine öğrenmesi yöntemlerinin kullanımı değerlendirilmiştir. Makine öğrenme yöntemleri, büyük veri kümelerindeki kalıpları ve ilişkileri tanımlayarak tahmin yapabilen bir yöntem olarak öne çıkmaktadır. İncelenen çalışmalarda destek vektör makineleri, naïve bayes, evrişimsel sinir ağları, tekrarlayan sinir ağları, rastgele orman, lojistik regresyon ve sinir ağları dâhil olmak üzere çeşitli makine öğrenmesi yöntemleri kullanılmıştır. Bu çalışma, farklı makine öğrenme modellerinin buzağılama zamanı tahminlerini değerlendirerek, süt sığırları yetiştiricilerine daha doğru bir şekilde buzağılama zamanlarını tahmin etme konusunda rehberlik etme amacıyla hazırlanmış olup, makine öğrenmesi yöntemlerinin süt çiftçileri için değerli bir araç olabileceğini göstermektedir. Bu yöntemler, çiftçilerin sürü yönetimi hakkında daha bilinçli kararlar almasına yardımcı olabilir ve bu da hayvan refahının iyileştirilmesine ve süt üretiminin artmasına yol açabilir.

References

  • [1] Hamşa H (2002). Ceylanpınar Tarım İşletmesinde yetiştirilen siyah alaca sığırlarda yetiştirme ve süt verim özellikleri, VAN: Yüzüncü Yıl Üniversitesi.
  • [2] Calcante A, Tangorra F ve Marches G (2014). A GPS/GSM based birth alarm system for grazing cows, Computers and electronics in agriculture, pp. 123-130.
  • [3] Cangar Ö, Leroy T, Guarino M, Vranken E, Fallon R, Lenehan J, Berckmans D ve Mee J (2008) Automatic real-time monitoring of locomotion and posture behaviour of pregnant cows prior to calving using online image analysis, Computers and Electronics in Agriculture, pp. 53-60.
  • [4] Wright I, White I, Russel A, Whyte T ve Bean Mc (1988). Prediction of calving date in beef cows by real-time ultrasonic scanning., The Veterinary Record, pp. 228-229.
  • [5] Matsas D, Nebel R ve Pelzer K (1992). Evaluation of an on-farm blood progesterone test for predicting the day of parturition in cattle, Theriogenology, cilt 37, no. 4, pp. 859-868.
  • [6] Andresen S (2002). John McCarthy: father of AI, IEEE Intelligent Systems, cilt 17, no. 5, pp. 84-85.
  • [7] Sönmez O ve Zengin K (2019). Yiyecek ve İçecek İşletmelerinde Talep Tahmini: Yapay Sinir Ağları ve Regresyon Yöntemleriyle Bir Karşılaştırma, Avrupa Bilim ve Teknoloji, pp. 302-308.
  • [8] Vasseur A, Borderas F, Cue R, Lefebvre D, Rushen J, Wade K ve Passillé A (2010). A survey of dairy calf management practices in Canada that affect animal welfare, Journal of Dairy Science, pp. 1307-1316.
  • [9] Karslı M ve Evci Ş (2018). Buzağı Kayıplarının Önlenmesinde İnek ve Buzağı Beslemesinin Önemi, Lalahan Hayvancılık Araştırma Enstitüsü, cilt 58, no. Özel, pp. 23-34.
  • [10] Akbaş, O., Yılmaz, S., & Başalan, M. (2017). Buzağı Kayıpları Sempozyumu. Kırıkkale: Kırıkkale Ünv.
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  • [12] El Naqa I ve Murphy M (2020). What Is Machine Learning? , Springer International Publishing., 2015.
  • [13] Mahesh B (2020). Machine Learning Algorithms- A Review, International Journal of Science and Research , pp. 381-386.
  • [14] Alpaydin, E. (2021). Machine learning. Mit Press.
  • [15] Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350-361.
  • [16] Berglund B, Philipsson J ve Danell ö (1987). External signs of preparation for calving and course of parturition in Swedish dairy cattle breeds, Animal Reproduction Science, cilt 15, no. 1-2, pp. 61-79.
  • [17] Kornmatitsuk B, Önigsson K, Kindahl H, Gustafsson H, Forsberg M ve Madej A (2000). Clinical Signs and Hormonal Changes in Dairy Heifers after Induction of Parturition with Prostaglandin F2α, Journal of Veterinary Medicine Series A, cilt 47, no. 7, pp. 395-409.
  • [18] Streyl D, Sauter-Louis C, Braunert A, Lange D, Weber F ve Zerbe H (2011). Establishment of a standard operating procedure for predicting the time of calving in cattle, Journal of Veterinary Science, cilt 12, no. 2, pp. 177-185.
  • [19] Burfeind O, Suthar V, Voigtsberger R, Bonk S ve Heuwieser W (2011). Validity of prepartum changes in vaginal and rectal temperature to predict calving in dairy cows, Journal of Dairy Science, pp. 5053-5061.
  • [20] Palombi C, Paolucci M, Stradaioli G, Corubolo M, Pascolo P ve Monaci M (2013). Evaluation of remote monitoring of parturition in dairy cattle as a new tool for calving management, BMC Veterinary Research, pp. 1-9.
  • [21] Clark C, Lyons N, Millapan L, Talukder S, Cronin G, Kerrisk K ve Garcia S (2015). Rumination and activity levels as predictors of calving for, Animal, pp. 691-695.
  • [22] Borchers M, Chang Y, Proudfoot K, Wadsworth B, Stone A ve Bewley J (2016). Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle, Journal of dairy science, no. 100, pp. 1-11.
  • [23] Ouellet V, Vasseur E, Heuwieser W, Burfeind O, Maldague X ve Charbonneau É (2016). Evaluation of calving indicators measured by automated monitoring devices to predict the onset of calving in Holstein dairy cows, Journal of Dairy Science, cilt 99, no. 2, pp. 1539-1548.
  • [24] Yıldız A (2016). Büyükbaş Hayvanlarda Kızgınlığın (Östrus) , Tokat Gaziosmanpaşa Üniversitesi Fen Bilimleri Enstitüsü, Tokat.
  • [25] Rutten C, Kamphuis C, Hogeveen H, Huijps K, Nielen M ve Steeneveld W (2017). Sensor data on cow activity, rumination, and ear temperature improve prediction of the start of calving in dairy cows, Computers and Electronics in Agriculture, no. 132, pp. 108-118.
  • [26] Fadul M, Bogdahn C, Alsaaod M, Hüsler J, Starke A, Steiner A ve Hirsbrunner G (2017). Prediction of calving time in dairy cattle, Animal Reproduction Science, no. 187, pp. 37-46.
  • [27] Zehner N, Niederhauser J, Schick M ve Umstatter C (2019). Development and validation of a predictive model for calving time based on sensor measurements of ingestive behavior in dairy cows, Computers and Electronics in Agriculture, no. 161, pp. 62-71.
  • [28] Miller G, Mitchell M, Barker Z, Giebel K, Codling E, Amory J, Michie C, Davison C, Tachtatzis C, Andonovic I ve Duthie C (2019). Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows, Animal, pp. 1-9.
  • [29] Keceli A, Catal C, Kaya A ve Tekinerdoğan B (2020). Development of a recurrent neural networks-based calving prediction model using activity and behavioral data, Computers and Electronics in Agriculture, no. 170, pp. 1-9.
  • [30] Peng Y, Kondo N, Fujiura T, Suzuki T, Ouma S, Wulandari, Yoshioka H ve Itoyama E (2020). Dam behavior patterns in Japanese black beef cattle prior to calving:, Computers and Electronics in Agriculture, cilt 169, pp. 1-7.
  • [31] Higaki S, Koyama K, Sasaki Y, Abe K, Honkawa K, Minamino Y, Mikurino Y, Okada H, Miwakeichi F, Darhan H ve Yoshioka K (2020). Technical note: Calving prediction in dairy cattle based on continuous measurements of ventral tail base skin temperature using supervised machine learning, Journal of dairy science, no. 103, pp. 8535-8540.
  • [32] Liseune A, Poel V, Hut P, Eerdenburg F ve Hostens M (2021). Leveraging sequential information from multivariate behavioral sensor data to predict the moment of calving in dairy cattle using deep learning, Computers and Electronics in Agriculture, no. 191. [33] Sumi K, Maw S, Zin T, Tin P, Kobayashi I ve Horii Y (2021). Activity-Integrated Hidden Markov Model to Predict Calving Time, Animals, cilt 11.
  • [34] Wełeszczuk J, Kosinska-Selbi B ve Cholewińska P (2022). Prediction of Polish Holstein’s economical index and calving interval using, Livestock Science.
  • [35] Khanzode, K. C. A., & Sarode, R. D. (2020). Advantages and disadvantages of artificial intelligence and machine learning: A literature review. International Journal of Library & Information Science (IJLIS), 9(1), 3.

Using Machine Learning Methods to Predict the Calving Time of Dairy Cattle: An Overview

Year 2023, Volume: 2023 Issue: 18, 27 - 39, 03.07.2023

Abstract

Focusing on the importance of predicting the calving time of dairy cattle, this study evaluates the use of machine learning methods. Predicting calving time is an important task in milk production. Early prediction of calving time can help farmers to make informed decisions about herd management, such as when to move cows to a specialized calving pen or when to increase the amount of feed. This study evaluates the use of machine learning methods to predict calving time in dairy cattle. Machine learning methods stand out as a method that can make predictions by identifying patterns and relationships in large datasets. Various machine learning methods including support vector machines, naïve bayes, convolutional neural networks, recurrent neural networks, random forest, logistic regression and neural networks have been used in the reviewed studies. By evaluating the calving time predictions of different machine learning models, this study guides dairy farmers to more accurately predict calving times and shows that machine learning methods can be a valuable tool for dairy farmers. These methods can help farmers make more informed decisions about herd management, which can lead to improved animal welfare and increased milk production.

References

  • [1] Hamşa H (2002). Ceylanpınar Tarım İşletmesinde yetiştirilen siyah alaca sığırlarda yetiştirme ve süt verim özellikleri, VAN: Yüzüncü Yıl Üniversitesi.
  • [2] Calcante A, Tangorra F ve Marches G (2014). A GPS/GSM based birth alarm system for grazing cows, Computers and electronics in agriculture, pp. 123-130.
  • [3] Cangar Ö, Leroy T, Guarino M, Vranken E, Fallon R, Lenehan J, Berckmans D ve Mee J (2008) Automatic real-time monitoring of locomotion and posture behaviour of pregnant cows prior to calving using online image analysis, Computers and Electronics in Agriculture, pp. 53-60.
  • [4] Wright I, White I, Russel A, Whyte T ve Bean Mc (1988). Prediction of calving date in beef cows by real-time ultrasonic scanning., The Veterinary Record, pp. 228-229.
  • [5] Matsas D, Nebel R ve Pelzer K (1992). Evaluation of an on-farm blood progesterone test for predicting the day of parturition in cattle, Theriogenology, cilt 37, no. 4, pp. 859-868.
  • [6] Andresen S (2002). John McCarthy: father of AI, IEEE Intelligent Systems, cilt 17, no. 5, pp. 84-85.
  • [7] Sönmez O ve Zengin K (2019). Yiyecek ve İçecek İşletmelerinde Talep Tahmini: Yapay Sinir Ağları ve Regresyon Yöntemleriyle Bir Karşılaştırma, Avrupa Bilim ve Teknoloji, pp. 302-308.
  • [8] Vasseur A, Borderas F, Cue R, Lefebvre D, Rushen J, Wade K ve Passillé A (2010). A survey of dairy calf management practices in Canada that affect animal welfare, Journal of Dairy Science, pp. 1307-1316.
  • [9] Karslı M ve Evci Ş (2018). Buzağı Kayıplarının Önlenmesinde İnek ve Buzağı Beslemesinin Önemi, Lalahan Hayvancılık Araştırma Enstitüsü, cilt 58, no. Özel, pp. 23-34.
  • [10] Akbaş, O., Yılmaz, S., & Başalan, M. (2017). Buzağı Kayıpları Sempozyumu. Kırıkkale: Kırıkkale Ünv.
  • [11] Mee J (2004). Managing the dairy cow at calving time, Veterinary Clinics: Food Animal Practice, cilt 20, no. 3, pp. 521-546.
  • [12] El Naqa I ve Murphy M (2020). What Is Machine Learning? , Springer International Publishing., 2015.
  • [13] Mahesh B (2020). Machine Learning Algorithms- A Review, International Journal of Science and Research , pp. 381-386.
  • [14] Alpaydin, E. (2021). Machine learning. Mit Press.
  • [15] Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350-361.
  • [16] Berglund B, Philipsson J ve Danell ö (1987). External signs of preparation for calving and course of parturition in Swedish dairy cattle breeds, Animal Reproduction Science, cilt 15, no. 1-2, pp. 61-79.
  • [17] Kornmatitsuk B, Önigsson K, Kindahl H, Gustafsson H, Forsberg M ve Madej A (2000). Clinical Signs and Hormonal Changes in Dairy Heifers after Induction of Parturition with Prostaglandin F2α, Journal of Veterinary Medicine Series A, cilt 47, no. 7, pp. 395-409.
  • [18] Streyl D, Sauter-Louis C, Braunert A, Lange D, Weber F ve Zerbe H (2011). Establishment of a standard operating procedure for predicting the time of calving in cattle, Journal of Veterinary Science, cilt 12, no. 2, pp. 177-185.
  • [19] Burfeind O, Suthar V, Voigtsberger R, Bonk S ve Heuwieser W (2011). Validity of prepartum changes in vaginal and rectal temperature to predict calving in dairy cows, Journal of Dairy Science, pp. 5053-5061.
  • [20] Palombi C, Paolucci M, Stradaioli G, Corubolo M, Pascolo P ve Monaci M (2013). Evaluation of remote monitoring of parturition in dairy cattle as a new tool for calving management, BMC Veterinary Research, pp. 1-9.
  • [21] Clark C, Lyons N, Millapan L, Talukder S, Cronin G, Kerrisk K ve Garcia S (2015). Rumination and activity levels as predictors of calving for, Animal, pp. 691-695.
  • [22] Borchers M, Chang Y, Proudfoot K, Wadsworth B, Stone A ve Bewley J (2016). Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle, Journal of dairy science, no. 100, pp. 1-11.
  • [23] Ouellet V, Vasseur E, Heuwieser W, Burfeind O, Maldague X ve Charbonneau É (2016). Evaluation of calving indicators measured by automated monitoring devices to predict the onset of calving in Holstein dairy cows, Journal of Dairy Science, cilt 99, no. 2, pp. 1539-1548.
  • [24] Yıldız A (2016). Büyükbaş Hayvanlarda Kızgınlığın (Östrus) , Tokat Gaziosmanpaşa Üniversitesi Fen Bilimleri Enstitüsü, Tokat.
  • [25] Rutten C, Kamphuis C, Hogeveen H, Huijps K, Nielen M ve Steeneveld W (2017). Sensor data on cow activity, rumination, and ear temperature improve prediction of the start of calving in dairy cows, Computers and Electronics in Agriculture, no. 132, pp. 108-118.
  • [26] Fadul M, Bogdahn C, Alsaaod M, Hüsler J, Starke A, Steiner A ve Hirsbrunner G (2017). Prediction of calving time in dairy cattle, Animal Reproduction Science, no. 187, pp. 37-46.
  • [27] Zehner N, Niederhauser J, Schick M ve Umstatter C (2019). Development and validation of a predictive model for calving time based on sensor measurements of ingestive behavior in dairy cows, Computers and Electronics in Agriculture, no. 161, pp. 62-71.
  • [28] Miller G, Mitchell M, Barker Z, Giebel K, Codling E, Amory J, Michie C, Davison C, Tachtatzis C, Andonovic I ve Duthie C (2019). Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows, Animal, pp. 1-9.
  • [29] Keceli A, Catal C, Kaya A ve Tekinerdoğan B (2020). Development of a recurrent neural networks-based calving prediction model using activity and behavioral data, Computers and Electronics in Agriculture, no. 170, pp. 1-9.
  • [30] Peng Y, Kondo N, Fujiura T, Suzuki T, Ouma S, Wulandari, Yoshioka H ve Itoyama E (2020). Dam behavior patterns in Japanese black beef cattle prior to calving:, Computers and Electronics in Agriculture, cilt 169, pp. 1-7.
  • [31] Higaki S, Koyama K, Sasaki Y, Abe K, Honkawa K, Minamino Y, Mikurino Y, Okada H, Miwakeichi F, Darhan H ve Yoshioka K (2020). Technical note: Calving prediction in dairy cattle based on continuous measurements of ventral tail base skin temperature using supervised machine learning, Journal of dairy science, no. 103, pp. 8535-8540.
  • [32] Liseune A, Poel V, Hut P, Eerdenburg F ve Hostens M (2021). Leveraging sequential information from multivariate behavioral sensor data to predict the moment of calving in dairy cattle using deep learning, Computers and Electronics in Agriculture, no. 191. [33] Sumi K, Maw S, Zin T, Tin P, Kobayashi I ve Horii Y (2021). Activity-Integrated Hidden Markov Model to Predict Calving Time, Animals, cilt 11.
  • [34] Wełeszczuk J, Kosinska-Selbi B ve Cholewińska P (2022). Prediction of Polish Holstein’s economical index and calving interval using, Livestock Science.
  • [35] Khanzode, K. C. A., & Sarode, R. D. (2020). Advantages and disadvantages of artificial intelligence and machine learning: A literature review. International Journal of Library & Information Science (IJLIS), 9(1), 3.
There are 34 citations in total.

Details

Primary Language Turkish
Subjects Experimental Methods in Fluid Flow, Heat and Mass Transfer
Journal Section Review articles
Authors

Oğuzhan Sönmez 0000-0003-4456-7036

Kenan Zengin 0000-0002-7940-6315

Early Pub Date June 27, 2023
Publication Date July 3, 2023
Submission Date June 6, 2023
Acceptance Date June 12, 2023
Published in Issue Year 2023 Volume: 2023 Issue: 18

Cite

APA Sönmez, O., & Zengin, K. (2023). Süt Sığırlarının Buzağılama Zamanının Tahmininde Makine Öğrenme Yöntemlerinin Kullanımı Çalışmaları Üzerine Bir Değerlendirme. Journal of New Results in Engineering and Natural Sciences, 2023(18), 27-39.