Machine learning algorithms for predicting peak yield in buffaloes using linear traits

SUNESH; A K BALHARA; N K DAHIYA; HIMANSHU; RISHI PAL SINGH; A P RUHIL

doi:10.56093/ijans.v92i8.122008

Authors

SUNESH
A K BALHARA Guru Jambeshwar University of Science and Technology, Hisar, Haryana 125 001 India
N K DAHIYA Guru Jambeshwar University of Science and Technology, Hisar, Haryana 125 001 India
HIMANSHU Guru Jambeshwar University of Science and Technology, Hisar, Haryana 125 001 India
RISHI PAL SINGH Guru Jambeshwar University of Science and Technology, Hisar, Haryana 125 001 India
A P RUHIL Guru Jambeshwar University of Science and Technology, Hisar, Haryana 125 001 India

https://doi.org/10.56093/ijans.v92i8.122008

Keywords:

Buffalo, Dairy, Linear traits, Machine learning algorithms, Selection

Abstract

Various studies have proved that linear traits have strong relationship with milk productivity but no such models are available for selection of animals based on linear traits. The present study conducted during 2020-22, is an attempt to develop an intelligent model using machine learning algorithms to predict peak milk yield based on its linear traits for selection of best dairy animals. A dataset on 14 linear traits of 259 buffalos across 5 lactations with peak milk yield was created and used for developing models. Data was collected from the buffalos having 8 to 26 kg peak milk yield maintained at Animal Farm Section, Central Institute for Research on Buffaloes, Hisar and also from private farms maintained by farmers. Predictive models were developed using various machine learning algorithms (artificial neural network, support vector regression and random forest) along with multi-linear regression executed on WEKA machine learning platform. Performance of these models was evaluated using evaluation metrics root mean squared error (RMSE). Results revealed that the Artificial Neural Network (ANN) model performed best with minimum RMSE 2.0308. Rear udder height and Lactation number emerged as the two most important attributes affecting the peak milk yield. Such model will be useful and handy for the stakeholders in selection of best dairy animals based on linear traits in absence of authentic record of peak milk yield.

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References

Al-Hered M A A, Khataf S S, Atkass J E and Juma K H. 2005. Some factors related to height and circumference of udders among lactating Holstein cows. Jordian Journal of Agricultural Science 1(1): 26–30.

Borghese A, Rasmussen M and Thomas C S. 2007. Milking management of dairy buffalo. Italian Journal of Animal Science 6(2): 39–50. DOI: https://doi.org/10.4081/ijas.2007.s2.39

Breiman L, Friedman J H, Olshen R A and Stone C J. 1984. Classification and Regression Trees. Wadsworth & Brooks, Monterey, CA.

Breiman L. 2001. Random forests. Machine Learning 45: 5–32. DOI: https://doi.org/10.1023/A:1010933404324

Dahiya S P, Kumar M and Dhillod S. 2020. Relationship of linear type traits with production and reproduction performance in Murrah buffaloes. Indian Journal of Animal Sciences 90(6): 942–46.

Daliri Z, Hafezian S H, Shad Parvar A and Rahimi G. 2008. Genetic relationships among longevity, milk production and linear type traits in Iranian Holstein Cattle. Journal of Animal and Veterinary Advance 7: 512–15.

Devi I, Singh P, Dudi K, Lathwal S S, Ruhil A P, Singh Y, Malhotra R, Baithalu R K and Sinha R. 2019. Vocal cues-based Decision Support System for estrus detection in water buffaloes (Bubalus bubalis). Computers and Electronics in Agriculture 62: 183–88. DOI: https://doi.org/10.1016/j.compag.2019.04.003

Dhillod S, Kar D, Patil C S, Sahu S and Singh N. 2017. Study of the dairy characters of lactating Murrah buffaloes on the basis of body parts measurements. Veterinary World 10(1): 17–21. DOI: https://doi.org/10.14202/vetworld.2017.17-21

Dhoble A S, Ryan K T, Lahiri P, Chen M, Pang X, Cardoso F C and Bhalerao K D. 2019. Cytometric fingerprinting and machine learning (CFML): A novel label-free, objective method for routine mastitis screening. Computers and Electronics in Agriculture 162: 505–13. DOI: https://doi.org/10.1016/j.compag.2019.04.029

Dongre V B, Gandhi R S, Singh A and Ruhil A P. 2012. Comparative efficiency of artificial neural networks and multiple linear regression analysis for prediction of first lactation 305-day milk yield in Sahiwal cattle. Livestock Science 147: 192–97. DOI: https://doi.org/10.1016/j.livsci.2012.04.002

Fausett L. 1994. Fundamentals of Neural Network. Prentice Hall, USA.

Gandhi R S, Raja T V, Ruhil A P and Kumar A. 2009. Prediction of lifetime milk production using artificial neural network in Sahiwal cattle. Indian Journal of Animal Sciences 79(10): 1038–40.

Gandhi R S, Raja T V, Ruhil A P and Kumar A. 2010. Artificial Neural Network versus Multiple Regression Analysis for prediction of lifetime milk production in Sahiwal cattle. Journal of Applied Animal Research 38(2): 233–37. DOI: https://doi.org/10.1080/09712119.2010.10539517

Gu Z B, Yang S L, Wang J, Ma C, Chen Y, Hu W L, Tang S K, Zhou H S, Liu C B, Chen T, Fu X H, Xu S H, Shi Z P, Li R S, Mei G D and Mao, H M. 2018. Relationship between Peak milk yield and udder parameters of Dehong crossbred dairy buffaloes. Iranian Journal of Applied Animal Science 8(1): 25–32.

ICAR, 2018. Section-5 - ICAR guidelines for conformation recording of dairy cattle, beef cattle, dual purpose cattle and dairy goats, https:// www.icar.org/Guidelines/05- Conformation-Recording.pdf

Kalyankar S D and Gujar S V. 2003. Peak yield, days to attain peak yield and lactation milk yield in Marathwadi buffaloes. Indian Journal of Animal Research 37(2): 119–21.

Kamphuis C, Mollenhorst H, Heesterbeek J A P and Hogeveen H. 2010. Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction. Journal of Dairy Science 93(8): 3616–27. DOI: https://doi.org/10.3168/jds.2010-3228

Keceli A S, Catal C, Kaya A and Tekinerdogan B. 2020. Development of a recurrent neural networks-based calving prediction model using activity and behavioural data. Computers and Electronics in Agriculture 170: 105285. DOI: https://doi.org/10.1016/j.compag.2020.105285

Kumar V, Chakravarty A K, Magotra A, Patil C S and Shivahre P R. 2019. Comparative study of ANN and conventional methods in forecasting first lactation milk yield in Murrah buffalo. Indian Journal of Animal Sciences 89(11): 1262–68.

Lin C Y, Lee A J, McAllister A J, Batra T R, Roy G L, Vesely J A, Wauthy J M and Winter K A. 1987. Intercorrelations among milk production traits and body and udder measurements in Holstein heifers. Journal of Dairy Sciences 70: 2385–93. DOI: https://doi.org/10.3168/jds.S0022-0302(87)80299-0

Manoj M, Gandhi R S, Raja T V, Ruhil A P, Singh A and Gupta A K. 2014. Comparison of artificial neural network and multiple linear regression for prediction of first lactation milk yield using early body weights in Sahiwal cattle. Indian Journal of Animal Sciences 84(4): 427–30.

McQueen R J, Garner S R, Nevill-Manning C G and Witten I H. 1995. Applying machine learning to agricultural data. Computers and Electronics in Agriculture 12(4): 275–93. DOI: https://doi.org/10.1016/0168-1699(95)98601-9

NDDB. 2017. Guidelines for Type Classification of Cattle and Buffalo, National Dairy Development Board, Anand, Gujarat. Accessed at https://www.dairyknowledge.in/sites/default/files/animal_type_classification_guidelines.pdf on January 20th, 2021.

Nguyena Q T, Fouchereaub R, Frénoda E, Gerardc C and Sinchollec V. 2020. Comparison of forecast models of production of dairy cows combining animal and diet parameters. Computers and Electronics in Agriculture 170: 105258. DOI: https://doi.org/10.1016/j.compag.2020.105258

Patel Y G, Trivedi M M, Rajpura R J, Savaliya F P and Parmar M. 2016. Udder and teat measurements and their relation with milk production in crossbred cows. International Journal of Science, Environment and Technology 5: 3048–54.

Platt J. 1999. Fast training of svms using sequential minimal optimization. (Eds) Scholkopf B, Burges C and Smola A. Advances in Kernel Methods—Support Vector Learning. MIT Press, Cambridge, 185–208.

Prasad R M V, Sudhakar K, Rao R E, Gupta R B and Mahender M. 2010. Studies on the udder and teat morphology and their relationship with milk yield in Murrah buffaloes. Livestock Research for Rural Development 22(1).

Ray D E, Halbach T J and Armstrong D V. 1992. Season and lactation number effects on milk production and reproduction of dairy cattle in Arizona. Journal of Dairy Science 75: 2976– 83. DOI: https://doi.org/10.3168/jds.S0022-0302(92)78061-8

Shahinfar S, Mehrabani-Yeganeh H, Lucas C, Kalhor A, Kazemian M and Weigel K A. 2012. Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems. Computational and Mathematical Methods in Medicine 4: 127–30. DOI: https://doi.org/10.1155/2012/127130

Shahinfar S, Page D, Guenther J, Cabrera V, Fricke P and Weigel K. 2013. Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms. Journal of Dairy Science 97: 731–42. DOI: https://doi.org/10.3168/jds.2013-6693

Sharma A K, Sharma R K and Kasana H S. 2007. Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modelling. Applied Soft Computing 7: 1112–20. DOI: https://doi.org/10.1016/j.asoc.2006.07.002

Sikka P, Nath A, Paul S S, Andonissamy J, Mishra D C, Rao A R, Balhara A K, Chaturvedi K K, Yadav K K and Sunesh. 2020. Inferring relationship of blood metabolic changes and average daily gain with feed conversion efficiency in Murrah heifers: Machine learning approach. Frontier Veterinary Science 7: 518. DOI: https://doi.org/10.3389/fvets.2020.00518

Singh M, Lathwal S S, Singh Y, Mohanty T K, Ruhil A P and Singh N. 2015. Prediction of lameness based on the percent body weight distribution to individual limbs of Karan Fries cows. Indian Journal of Animal Research 49(3): 392–98. DOI: https://doi.org/10.5958/0976-0555.2015.00144.2

Skansi S. 2018. Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence. Springer, Cham, Switzerland. DOI: https://doi.org/10.1007/978-3-319-73004-2

SPSS Inc. 2011. Statistical Package for Social Sciences Study. SPSS for Windows, Version 20. Chicago SPSS Inc.

Taneja M, Byabazaire J, Jalodia N, Davy A, Olariu C and Malone P. 2020. Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle. Computers and Electronics in Agriculture 171: 105286. DOI: https://doi.org/10.1016/j.compag.2020.105286

Tilki M, Inal S, Çolak M and Garip M. 2005. Relationships between milk yield and udder measurements in Brown Swiss cows. Turkish Journal of Veterinary and Animal Sciences 29: 75–81.

Vapnik V. 2000. The Nature of Statistical Learning Theory. Springer-Verlag, New York. DOI: https://doi.org/10.1007/978-1-4757-3264-1

Waikato Environment for Knowledge Analysis (weka). https:// www.cs.waikato.ac. nz/ml/weka.