Neural network-assisted body weight prediction of goat kids using morphometric measurements in various growth phases

A  DAS; A  TARAFDAR; A K  ARGANA; A  DEBBARMA; B  SINGH; R S  GODARA; T C  DHARA; N K  DAHIYA; H C  YADAV; D K MANDAL

doi:10.56093/ijans.v95i8.169061

Authors

A DAS ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh- 243 122, India
A TARAFDAR ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh- 243 122, India
A K ARGANA ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh- 243 122, India
A DEBBARMA ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh- 243 122, India
B SINGH ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh- 243 122, India
R S GODARA ICAR-Central Sheep and Wool Research Institute, Avikanagar- 304 501, Rajasthan, India
T C DHARA ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh- 243 122, India
N K DAHIYA Agriculture Education Division, ICAR, Head Quarter, New Delhi, India
H C YADAV ICAR-Indian Veterinary Research Institute, Izatnagar 243 122, Uttar Pradesh, India
D K MANDAL ICAR-National Dairy Research Institute, Eastern Regional Station, Kalyani, West Bengal-741 235, India

https://doi.org/10.56093/ijans.v95i8.169061

Keywords:

Algorithm, Goat kids, Machine learning, Morphometry, Post-weaning, Pre-weaning

Abstract

Body weight (BW) measurement of animals is an essential farm activity to monitor their growth and welfare but weighing equipment is often inaccessible for low-income farmers. This work utilized animal morphometrics to determine BW of Black Bengal goat kids using artificial neural networks. Over four months, 130 observations per growth phase (pre-weaning and post-weaning) were collected for morphometric measurements and BW. Twelve different body morphometric measures were taken, and correlated with BW. Various combinations of training algorithms (LM and GDX) with LOGSIG and TANSIG transfer functions were tested across 5 to 30 hidden layer neurons (HLNs). Results showed higher accuracy for BW prediction using heart girth and height at back (HAB) for the pre-weaning phase. However, the corpus length (CL) and HAB showed better model accuracy for post-weaning and combined growth phase. The selected ANN model showed superior results than the non-linear and linear regression models. ANN may therefore be used to predict BW of goat kids in place of other regression methods.

Downloads

Download data is not yet available.

References

Abraham H, Gizaw S and Urge M. 2018. Identification of breeding objectives for Begait goat in western Tigray, North Ethiopia. Tropical Animal Health and Production 50: 1887–92.

Adhianto K, Harris I, Nugroho P and Putra WPB. 2020. Prediction of body weight through body measurements in Boerawa (Boer × Etawah crossbred) bucks at Tanggamus Regency of Indonesia. Bulgarian Journal of Agricultural Science 20(6): 1273–79.

Akkol S, Akilli A and Cemal I. 2017. Comparison of artificial neural network and multiple linear regression for prediction of live weight in hair goats. Yyu Journal of Agricultural Sciences 27(1): 21–29.

Behzadi MR and Aslaminejad AA. 2010. A comparison of neural network and nonlinear regression predictions of sheep growth. Journal of Animal and Veterinary Advances 9(16): 2128–31.

Cam MA, Olfaz M and Soydan ER. 2010. Possibilities of using morphometric characteristics as a tool for body weight prediction in Turkish Hair Goats (Kilkeci). Asian Journal of Animal and Veterinary Advances 5: 52–59.

DAHD (2019) Department of Animal Husbandry, Dairying and Fisheries, Ministry of Agriculture, Government of India, New Delhi. https://dahd.nic.in/sites/default/filess/Key%20Results%2BAnnexure%2018.10.2019.pdf

Ghotbaldini H, Mohammadabadi M, Nezamabadi-Pour H, Babenko OI, Bushtruk MV and Tkachenko SV. 2019. Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed. Acta Scientiae Veterinariae 41: e45282.

Habib MA, Akhtar A, Bhuiyan AK, Choudhury MP and Afroz MF. 2019. Biometrical relationship between body weight and body measurements of Black Bengal goat (BBG). Current Journal of Applied Science and Technology 35(2): 1–7.

Haldar A, Pal P, Ghosh S and Pan S. 2023. Body weight prediction using recursive partitioning and regression trees (RPART) model in Indian Black Bengal goat breed: A machine learning approach. Indian Journal of Animal Research 57: 1251–57.

Hossain ME. 2021. Performance of Black Bengal goat: a 50-year review. Tropical Animal Health and Production 53: e71.

Ibrahim A, Artama WT, Budisatria IG, Yuniawan R, Atmoko BA and Widayanti R. 2021. Regression model analysis for prediction of body weight from body measurements in female Batur sheep of Banjarnegara District, Indonesia. Biodiversitas Journal of Biological Diversity 22: 2723–30.

Iqbal F, Waheed A and Faraz A. 2022. Comparing the Predictive Ability of Machine Learning Methods in Predicting the Live Body Weight of Beetal Goats of Pakistan. Pakistan Journal of Zoology 54(1): 1–8.

Iqbal M, Javed K and Ahmad N. 2013. Prediction of body weight through body measurements in Beetal goats. Pakistan Journal of Science 65: 458–61.

Jimmy S, David M, Donald KR and Dennis M. 2010. Variability in body morphometric measurements and their application in predicting live body weight of Mubende and Small East African goat breeds in Uganda. Middle-East Journal of Scientific Research 5(2): 98–105.

Khorshidi-Jalali M, Mohammadabadi M, Koshkooieh AE, Barazandeh A and Babenko O. 2019. Comparison of artificial neural network and regression models for prediction of body weight in Raini Cashmere goat. Iranian Journal of Applied Animal Science 3: 453–61.

Mebratie W, Tekuar S, Alemayehu K and Dessie T. 2022. Body weight and linear body measurements of indigenous goat population in Awi Zone, Amhara region, Ethiopia. Acta Agriculturae Scandinavica, Section A – Animal Science 71(1–4): 89–97.

Michael JD, Baruselli PS and Campanile G. 2019. Influence of nutrition, body condition, and metabolic status on reproduction in female beef cattle: A review. Theriogenology 125: 277–284.

Preethi AL, Tarafdar A, Ahmad SF, Panda S, Tamilarasan K, Ruchay A and Gaur GK. 2023. Weight prediction of Landlly pigs from morphometric traits in different age classes using ANN and non-linear regression models. Agriculture 13(2): 362. Rahman AE, Shoukry MM, Mohamed MI, Salman FM and Abedo AA. 2019. Some body measurements as a management tool for Shami goats raised in subtropical areas in Egypt. Bulletin of the National Research Centre 43(1): 1–6.

Raja TV, Ruhil AP and Gandhi RS. 2012. Comparison of connectionist and multiple regression approaches for prediction of body weight of goats. Neural Computing and Applications 21: 119–124.

Rakib MR, Ahmed S, Desha NH, Akther S, Rahman MH, Pasha MM, Dhakal A, Sultana N and Hemayet MA. 2022. Morphometric features and performances of Black Bengal goat in Bangladesh. Tropical Animal Health and Production 54(6): 341.

Roush WB, Dozier WA and Branton SL. 2006. Comparison of Gompertz and neural network models of broiler growth. Poultry Science 85: 794–97.

Ruhil AP, Raja TV and Gandhi RS. 2013. Preliminary study on prediction of body weight from morphometric measurements of goats through ANN models. Journal of the Indian Society of Agricultural Statistics 67: 51–58.

Siddiqui MU, Lateef M, Bashir MK, Bilal MQ, Muhammad G and Mustafa MI. 2015. Estimation of live weight using different body measurements in Sahiwal cattle. Pakistan Journal of Life and Social Sciences 13(1): 1–12.

Singh B, Das A, Bhakat C, Mishra B, Elangbam S, Sinver M, Ambili KS and Tarafdar A. 2025. Prediction of dry matter intake in growing Black Bengal goats using artificial neural networks. Tropical Animal Health and Production 57: 42.

Sun MA, Hossain MA, Islam T, Rahman MM, Hossain MM and Hashem MA. 2020. Different body measurement and body weight prediction of Jamuna Basin sheep in Bangladesh. SAARC Journal of Agriculture 18: 83–196.

Vaidya MM, Kulkarni SS, Dongre VB, Kokate LS, Khandait VN and Kale SB. 2018. Comparative efficacy of three different methods for prediction of live body weight in small ruminants. Indian Journal of Animal Science 88: 602–05.