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Comparison of Artificial Neural Network and Multiple Linear Regression for Prediction of Live Weight in Hair Goats

Year 2017, Volume 27, Issue 1, 21 - 29, 31.03.2017
https://doi.org/10.29133/yyutbd.263968

Abstract

Artificial neural networks are artificial intelligence based methods which learns like humans, as humans did from instances. In recent years, artificial neural networks are often preferred in prediction studies of farm animals as like in many different fields as an alternative to regression analyses. In this study, based on measurements of morphologic traits of 475 Hair goats, the impact of different morphological measures on live weight has been modelled by artificial neural networks and multiple linear regression analyses. Comparison of these two models has been done. In the analyses done with the artificial neural networks method three different back propagation algorithms, such as Levenberg-Marquart, Bayesian regularization and Scaled conjugate, have been used. Methods performances have been determined with different criteria as coefficient of determination, mean absolute deviation, root mean square error and mean absolute percentage error. According to the analyses results, it’s noted that artificial neural networks method is more successful than multiple linear regression in prediction of body weight in hair goats.

References

  • Alpar R (2011). Uygulamalı Çok Değişkenli İstatistiksel Yöntemler. Detay Yayıncılık, Ankara, 853p.
  • Atıl A, Akıllı A (2016). Comparison of artificial neural network and K-means for clustering dairy cattle. Int. J. Sustainable Agricultural Management and Informatics. 2(1): 40-52.
  • Cavero D, Tölle KT, Buxade C, Krieter J (2006). Mastitis detection in dairy cows by application of fuzzy logic. Livest. Prod. Sci. 105(1-3): 207-213.
  • Chen LJ, Cui LY, Xing L, Han LJ (2008). Prediction of the nutrient content in dairy manure using artificial neural network modeling. J. Dairy. Sci. 91: 4822-4829.
  • Craninx M, Fievez V, Vlaeminck B, De Baets B (2008). Artificial neural network models of the rumen fermentation pattern in dairy cattle. Comput. Electron. Agric. 60: 226-238.
  • Dong R, Zhao G (2014). The use of artificial neural in vitro rumen methane production using the CNCPS carbohydrate fractions as dietary variables. Livest. Prod. Sci. 162: 159-167.
  • Görgülü O (2012). Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks. South African Journal of Animal Science, Vol. 42, No. 3, pp.280-287.
  • Grzesiak W, Zaborski D, Sablik P, Żukiewicz A, Dybus A, Szatkowska, I (2010). Detection of cows with insemination problems using selected classification models. Computers and Electronics in Agriculture, Vol. 74, No. 2, pp.265-273.
  • Grzesiak W, Lacroix R, Wójcik J, Blaszczyk P (2003). A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records. Can. J. Anim. Sci. 83: 307-310.
  • Hassan, K. J., Samarasinghe, S., Lopez- Benavidest, M. G. 2009. Use of neural networks to detect minor and major pathogens that cause bovine mastitis. J. Dairy. Sci. 92: 1493-1499.
  • Haykin S (2008). Neural Networks and Learning Machines. Pearson Prentice Hall, New Jersey, 906p.
  • Hosseinia P, Edrisi M, Edriss MA, Nilforooshan MA (2007). Prediction of second parity milk yield and fat percentage of dairy cows based on first parity information using neural networks system. J. Appl. Sci. 7: 3274-3279.
  • Kominakis AP, Abas Z, Maltaris I, Rogdakis E (2002) A preliminary study of the application of artificial neural networks to prediction of milk yield in dairy sheep. Computers and Electronics in Agriculture, 35(1): 35-48.
  • Krieter J, Stamer E, Junge W (2006). Control charts and neural networks for oestrus detection in dairy cows. Lecture Notes in Informatics, Land- und Ernährungswirtschaft im Wandel-Aufgaben und Herausforderungen für die Agrar und Umweltinformatik, Referate der 26, GIL Jahrestagung, 6–8 March 2006, Potsdam, pp.133-136.
  • Küçükönder H, Boyacı S, Akyüz A (2016). A modeling study with an articial neural network: Developing estimation models for the tomato plant leaf area. Turkish Journal of Agriculture and Forestry, 40: 203-212.
  • Szyndler-Nędza M, Eckert R, Blicharski T, Tyra M, Prokowski A (2015). Prediction of carcass meat percentage in young pigs using linear regression models and artificial neural networks. Annals of Animal Science, DOI: 10.1515/aoas-2015-0069.
  • Negnevitsky M (2002). Artificial Intelligence, A Guide to Intelligent Systems. Pearson Education, Harlow, 415 p.
  • Öztemel E (2006). Yapay sinir ağları. Papatya Yayıncılık, İstanbul.
  • Roush WB, Wideman Jr RF, Cahaner A, Deeb N, Cravener TL (2001). Minimal number of chicken daily growth velocities for artificial neural network detection of pulmonary hypertension syndrome PHS, Poultry Science. 80(3): 254-259.
  • Salawu EO, Abdulraheem M, Shoyombo A, Adepeju A, Davies S, Akinsola O, Nwagu B (2014). Using Artificial Neural Network to Predict Body Weights of Rabbits. Open Journal of Animal Sciences. 4, 182-186.
  • Salehi F, Lacroix R, Yang XZ, Wade KM (1997). Effects of data preprocessing on the performance of artificial neural networks for dairy yield prediction and cow culling classification. Transactions of the American Society of Agricultural and Biological Engineers. 40(3): 839–846.
  • Salehi F, Lacroix R, Wade KM (1998). Improving dairy yield predictions through combined record classifiers and specialized artificial neural networks. Comput. Electron. Agric. 20: 199-213.
  • Sanzogni L, Kerr D (2001). Milk production estimates using feed forward artificial neural networks. Computers and Electronics in Agriculture. 32(1): 21-30.
  • Shahinfar S, Mehrabani-Yeganeh, H, Lucas C, Kalhor A, Kazemian M, Weigel KA (2012). Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems. Comput. Math. Methods Med. Article ID 127130, 9 pages.
  • Sharma AK, Sharma RK, Kasana HS (2007). Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling. Appl. Soft Comput. 7: 1112-1120.
  • Sun Z. (2008) Application of Artificial Neural Networks in Early Detection of Mastitis from Improved Data Collected On-Line by Robotic Milking Stations, dissertation, Lincoln University, New Zealand.
  • Takma Ç, Atıl H, Aksakal V (2012). Çoklu doğrusal regresyon ve yapay sinir ağı modellerinin laktasyon süt verimlerine uyum yeteneklerinin karşılaştırılması. Kafkas Üniv. Vet. Fak. Derg. 18(6): 941-944.
  • Yang XZ, Lacroix R, Wade KM (2000). Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks. Can. J. Anim. Sci. 80: 415–426.
  • Zhang T, You X (2015). Improvement of the training and normalization method of artificial neural network in the prediction of indoor environment. Procedia Engineering. 121: 1245-1251.

Kıl Keçilerinin Canlı Ağırlık Tahmininde Yapay Sinir Ağları ve Çoklu Doğrusal Regresyon Yöntemlerinin Karşılaştırılması

Year 2017, Volume 27, Issue 1, 21 - 29, 31.03.2017
https://doi.org/10.29133/yyutbd.263968

Abstract

Yapay sinir ağları, insanlara benzer şekilde, örnekler üzerinden öğrenen yapay zeka temelli bir yöntemdir. Yapay sinir ağları yöntemi birçok farklı alanda olduğu gibi son yıllarda hayvancılık alanında da özellikle tahmin çalışmalarında regresyon analizine alternatif olarak sıklıkla kullanılmaktadır. Bu çalışmada 475 baş Kıl keçisine ilişkin morfolojik özellik ölçümlerinin canlı ağırlık üzerine etkileri yapay sinir ağları ve çoklu doğrusal regresyon analizi ile modellenmiş ve yöntemler bir karşılaştırmaya tabi tutulmuştur. Çalışmada yapay sinir ağları ile gerçekleştirilen analizlerde Levenberg-Marquart, Bayesian regularization and Scaled conjugate olmak üzere üç farklı geri yayılım algoritması kullanılmıştır. Yöntemlerin performansları düzeltilmiş belirleme katsayısı, hata kareler ortalamasının karekökü, ortalama mutlak sapma ve ortalama mutlak yüzde hata istatistikleri ile değerlendirilmiştir. Analiz sonucunda, Kıl keçilerinde canlı ağırlık tahmini bakımından yapay sinir ağlarının çoklu doğrusal regresyon analizine göre daha başarılı olduğu belirlenmiştir.

References

  • Alpar R (2011). Uygulamalı Çok Değişkenli İstatistiksel Yöntemler. Detay Yayıncılık, Ankara, 853p.
  • Atıl A, Akıllı A (2016). Comparison of artificial neural network and K-means for clustering dairy cattle. Int. J. Sustainable Agricultural Management and Informatics. 2(1): 40-52.
  • Cavero D, Tölle KT, Buxade C, Krieter J (2006). Mastitis detection in dairy cows by application of fuzzy logic. Livest. Prod. Sci. 105(1-3): 207-213.
  • Chen LJ, Cui LY, Xing L, Han LJ (2008). Prediction of the nutrient content in dairy manure using artificial neural network modeling. J. Dairy. Sci. 91: 4822-4829.
  • Craninx M, Fievez V, Vlaeminck B, De Baets B (2008). Artificial neural network models of the rumen fermentation pattern in dairy cattle. Comput. Electron. Agric. 60: 226-238.
  • Dong R, Zhao G (2014). The use of artificial neural in vitro rumen methane production using the CNCPS carbohydrate fractions as dietary variables. Livest. Prod. Sci. 162: 159-167.
  • Görgülü O (2012). Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks. South African Journal of Animal Science, Vol. 42, No. 3, pp.280-287.
  • Grzesiak W, Zaborski D, Sablik P, Żukiewicz A, Dybus A, Szatkowska, I (2010). Detection of cows with insemination problems using selected classification models. Computers and Electronics in Agriculture, Vol. 74, No. 2, pp.265-273.
  • Grzesiak W, Lacroix R, Wójcik J, Blaszczyk P (2003). A comparison of neural network and multiple regression predictions for 305-day lactation yield using partial lactation records. Can. J. Anim. Sci. 83: 307-310.
  • Hassan, K. J., Samarasinghe, S., Lopez- Benavidest, M. G. 2009. Use of neural networks to detect minor and major pathogens that cause bovine mastitis. J. Dairy. Sci. 92: 1493-1499.
  • Haykin S (2008). Neural Networks and Learning Machines. Pearson Prentice Hall, New Jersey, 906p.
  • Hosseinia P, Edrisi M, Edriss MA, Nilforooshan MA (2007). Prediction of second parity milk yield and fat percentage of dairy cows based on first parity information using neural networks system. J. Appl. Sci. 7: 3274-3279.
  • Kominakis AP, Abas Z, Maltaris I, Rogdakis E (2002) A preliminary study of the application of artificial neural networks to prediction of milk yield in dairy sheep. Computers and Electronics in Agriculture, 35(1): 35-48.
  • Krieter J, Stamer E, Junge W (2006). Control charts and neural networks for oestrus detection in dairy cows. Lecture Notes in Informatics, Land- und Ernährungswirtschaft im Wandel-Aufgaben und Herausforderungen für die Agrar und Umweltinformatik, Referate der 26, GIL Jahrestagung, 6–8 March 2006, Potsdam, pp.133-136.
  • Küçükönder H, Boyacı S, Akyüz A (2016). A modeling study with an articial neural network: Developing estimation models for the tomato plant leaf area. Turkish Journal of Agriculture and Forestry, 40: 203-212.
  • Szyndler-Nędza M, Eckert R, Blicharski T, Tyra M, Prokowski A (2015). Prediction of carcass meat percentage in young pigs using linear regression models and artificial neural networks. Annals of Animal Science, DOI: 10.1515/aoas-2015-0069.
  • Negnevitsky M (2002). Artificial Intelligence, A Guide to Intelligent Systems. Pearson Education, Harlow, 415 p.
  • Öztemel E (2006). Yapay sinir ağları. Papatya Yayıncılık, İstanbul.
  • Roush WB, Wideman Jr RF, Cahaner A, Deeb N, Cravener TL (2001). Minimal number of chicken daily growth velocities for artificial neural network detection of pulmonary hypertension syndrome PHS, Poultry Science. 80(3): 254-259.
  • Salawu EO, Abdulraheem M, Shoyombo A, Adepeju A, Davies S, Akinsola O, Nwagu B (2014). Using Artificial Neural Network to Predict Body Weights of Rabbits. Open Journal of Animal Sciences. 4, 182-186.
  • Salehi F, Lacroix R, Yang XZ, Wade KM (1997). Effects of data preprocessing on the performance of artificial neural networks for dairy yield prediction and cow culling classification. Transactions of the American Society of Agricultural and Biological Engineers. 40(3): 839–846.
  • Salehi F, Lacroix R, Wade KM (1998). Improving dairy yield predictions through combined record classifiers and specialized artificial neural networks. Comput. Electron. Agric. 20: 199-213.
  • Sanzogni L, Kerr D (2001). Milk production estimates using feed forward artificial neural networks. Computers and Electronics in Agriculture. 32(1): 21-30.
  • Shahinfar S, Mehrabani-Yeganeh, H, Lucas C, Kalhor A, Kazemian M, Weigel KA (2012). Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems. Comput. Math. Methods Med. Article ID 127130, 9 pages.
  • Sharma AK, Sharma RK, Kasana HS (2007). Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling. Appl. Soft Comput. 7: 1112-1120.
  • Sun Z. (2008) Application of Artificial Neural Networks in Early Detection of Mastitis from Improved Data Collected On-Line by Robotic Milking Stations, dissertation, Lincoln University, New Zealand.
  • Takma Ç, Atıl H, Aksakal V (2012). Çoklu doğrusal regresyon ve yapay sinir ağı modellerinin laktasyon süt verimlerine uyum yeteneklerinin karşılaştırılması. Kafkas Üniv. Vet. Fak. Derg. 18(6): 941-944.
  • Yang XZ, Lacroix R, Wade KM (2000). Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks. Can. J. Anim. Sci. 80: 415–426.
  • Zhang T, You X (2015). Improvement of the training and normalization method of artificial neural network in the prediction of indoor environment. Procedia Engineering. 121: 1245-1251.

Details

Subjects Engineering
Journal Section Articles
Authors

Suna AKKOL
YÜZÜNCÜ YIL ÜNİVERSİTESİ
0000-0001-5123-7516
Türkiye


Aslı AKILLI This is me
AHİ EVRAN ÜNİVERSİTESİ
Türkiye


İbrahim CEMAL
ADNAN MENDERES UNIV
Türkiye

Publication Date March 31, 2017
Published in Issue Year 2017, Volume 27, Issue 1

Cite

APA Akkol, S. , Akıllı, A. & Cemal, İ. (2017). Kıl Keçilerinin Canlı Ağırlık Tahmininde Yapay Sinir Ağları ve Çoklu Doğrusal Regresyon Yöntemlerinin Karşılaştırılması . Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi , 27 (1) , 21-29 . DOI: 10.29133/yyutbd.263968

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Yuzuncu Yil University Journal of Agricultural Sciences by Van Yuzuncu Yil University Faculty of Agriculture is licensed under a Creative Commons Attribution 4.0 International License.