Predictive biometrics of hair sheep through digital imaging

Main Article Content

Alfonso J. Chay-Canul
Jorge Tapia González
Jorge Canul-Solís
Fernando Casanova-Lugo
Ángel T. Piñeiro-Vázquez
Rodrigo Portillo-Salgado
Ricardo García-Herrera
Einar Vargas-Bello-Pérez

Abstract

Direct collection of biometric measurements (BM) from sheep is an expensive and stressful procedure for animals; instead, indirect and novel methods have recently been used. The objective of this study was to use digital image analysis (DIA) to predict biometric measurements of Pelibuey sheep as a non-invasive approach under on-farm conditions. Withers height (WH), body length (BL), body diagonal length (BDL), and rib depth (RD) were predicted in Pelibuey ewes using DIA. Images were taken from the left flank of 65 nonpregnant and nonlactating Pelibuey ewes using a digital camera and analyzed by DIA. The BM determined from both in vivo and by DIA presented positive and moderate (P < 0.05) correlation coefficients (r) of 0.43, 0.66, 0.73, and 0.75 for BL, BDL, WH, and RD, respectively. Regression equations from BM by DIA had determination coefficients (r2) of 0.19, 0.44, 0.54, and 0.56 for BL, BDL, WH, and RD, respectively. The equations developed were from low to moderate precision (r2 = 0.18 to 55), moderate to high accuracy with a bias correction factor (Cb > 0.69), and low to moderate reproducibility index (> 0.30). Overall, the use of DIA was able to predict the BM in Pelibuey ewes with low to moderate precision and accuracy. Factors affecting the accuracy and precision of this relationship should be further investigated.

Keywords:
body measurements image analysis linear regression equations image-processing tropical conditions Body weight

Article Details

Author Biography

Einar Vargas-Bello-Pérez, School of Agriculture. Policy and Development New Agriculture Building. Earley Gate Whiteknights Road, Berkshire, United Kingdom

Universidad Autónoma de Chihuahua. Facultad de Zootecnia y Ecología. Chihuahua, México

References

Carabús A, Gispert M, Font-i-Furnols M. Imaging technologies to study the composition of live pigs: a review. Spanish Journal of Agricultural Research. 2016;14(3):e06R01. doi: 10.5424/sjar/2016143-8439.

Wu T, Gaw N, Xu Y, Li J, Wang L, Fu Y, Silva A, et al. Quantitative imaging system for cancer diagnosis and treatment planning: an interdisciplinary approach. Informs Tutorials in Operations Research. 2017;153–177. doi: 10.1287/educ.2017.0173.

Bertram CA, Klopfleisch R. The Pathologist 2.0: an update on digital pathology in Veterinary Medicine. Veterinary Pathology. 2017;54(5):756–766. doi: 10.1177/0300985817709888.

Argawal S, Chand S. Digital image forensic: a brief review. Forensic Research & Criminology International Journal. 2017;5(4):00161. doi: 10.15406/frcij.2017.05.00161.

Craigie CR, Navajas EA, Purchas RW, Maltin CA, Bünger L, Hoskin SO, Ross DW, Morris ST, Roehe R. A review of the development and use of video image analysis (VIA) for beef carcass evaluation as an alternative to the current EUROP system and other subjective systems. Meat Science. 2012;92(4):307–318. doi: 10.1016/j.meatsci.2012.05.028.

Cruz-Fernández M, Luque-Cobija MJ, Cervera ML, Morales-Rubio A, de la Guardia M. Smartphone determination of fat in cured meat products. Microchemical Journal. 2017;132:8–14. doi: 10.1016/j.microc.2016.12.020.

Nir O, Parmet Y, Werner D, Adin G, Halachmi I. 3D Computer-vision system for automatically estimating heifer height and body mass. Biosystems Engineering. 2018;173:4–10. doi: 10.1016/j.biosystemseng.2017.11.014.

Gomes RA, Monteiro GR, Assis GJF, Busato KC, Ladeira MM, Chizzotti ML. Technical note: Estimating body weight and body composition of beef cattle trough digital image analysis. Journal of Animal Science. 2016;94(12):5414–5422. doi: 10.2527/jas.2016-0797.

McPhee MJ, Walmsley BJ, Skinner B, Littler B, Siddell JP, Cafe LM, Wilkins JF, Oddy VH, Alempijevic A. Live animal assessments of rump fat and muscle score in Angus cows and steers using 3-dimensional imaging. Journal of Animal Science. 2017;95(4):1847–1857. doi: 10.2527/jas.2016.1292.

Cominotte A, Fernandes AFA, Dorea JRR, Rosa GJM, Ladeira MM, van Cleef EHCB, Pereira GL, Baldassini WA, Machado Neto OR. Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases. Livestock Science. 2020;232:103904. doi: 10.1016/j.livsci.2019.103904.

Miller GA, Hyslop JJ, Barclay D, Edwards A, Thomson W, Duthie CA. Using 3D imaging and machine learning to predict liveweight and carcass characteristics of live finishing beef cattle. Frontiers in Sustainable Food Systems. 2019;3:30. doi: 10.3389/fsufs.2019.00030.

Martins BM, Mendes ALC, Silva LF, Moreira TR, Costa JHC, Rotta PP, Chizzotti ML, Marcondes MI. Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements. Livestock Science. 2020;236:104054. doi: 10.1016/j.livsci.2020.104054.

Liu D, He D, Norton T. Automatic estimation of dairy cattle body condition score from depth image using ensemble model. Biosystems Engineering. 2020;194:16–27. doi: 10.1016/j.biosystemseng.2020.03.011.

Weber VAM, Weber FL, Gomes RC, Junior ASO, Menezes GV, Abreu UGP, Belete NAS, Pistori H. Prediction of Girolando cattle weight by means of body measurements extracted from images. Revista Brasileira de Zootecnia. 2020;49:e20190110. doi: 10.37496/rbz4920190110.

Na Zhang AL, Pei Wu B, Xin Hua Jiang C, Chuan Zhong Xuan D, Yan Hua Ma E, Yong An Zhang F. Development and validation of a visual image analysis for monitoring the body size of sheep. Journal of Applied Animal Research. 2018;46(1):1004–1015. doi: 10.1080/09712119.2018.1450257.

Carabús A, Gispert M, Brun A, Rodríguez P, Font-i-Furnols M. In vivo computed tomography evaluation of the composition of the carcass and various cuts of growing pigs of three commercial crossbreeds. Livestock Science. 2014;170:181–192. doi: 10.1016/j.livsci.2014.10.005.

Yan Q, Ding L, Wei H, Wang X, Jiang C, Degen A. Body weight estimation of yaks using body measurements from image analysis. Measurement. 2019;140:76–80. doi: 10.1016/j.measurement.2019.03.021.

Khojastehkey M, Kalantar Neyestanaki M, Roudbari Z, Sadeghipanah H, Javaheri H, Aghashahi AR. Feasibility of body weight estimation of Kalkoohi camels using digital image processing. Iranian Journal of Applied Animal Science. 2020;10(2):333–340.

Mollah BR, Hasan A, Salam A, Ali A. Digital image analysis to estimate the live weight of broiler. Computers and Electronics in Agriculture. 2010;72(1):48–52. doi: 10.1016/j.compag.2010.02.002.

Mortensen AK, Lisouski P, Ahrendt P. Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture. 2016;123:319–326. doi: 10.1016/j.compag.2016.03.011.

Wishart H, Morgan-Davies C, Stott AW, Wilson A, Waterhouse T. Liveweight loss associated with handling and weighing of grazing sheep. Small Ruminant Research. 2017;153:163–170. doi: 10.1016/j.smallrumres.2017.06.013.

Morota G, Ventura RV, Silva FF, Koyama M, Fernando SC. Big data analytics and precision animal agriculture symposium: machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science. 2018;96(4):1540–1550. doi: 10.1093/jas/sky014.

Kongsro J. Estimation of pig weight using a Microsoft Kinect prototype imaging system. Computers and Electronics in Agriculture. 2014;109:32–35. doi: 10.1016/j.compag.2014.08.008.

Chay-Canul AJ, García-Herrera RA, Salazar-Cuytún R, Ojeda-Robertos NF, Cruz-Hernández A, Fonseca MA, Canul-Solís JR. Development and evaluation of equations to predict body weight of Pelibuey ewes using heart girth. Revista Mexicana de Ciencias Pecuarias. 2019;10(3):767–777. doi: 10.22319/rmcp.v10i3.4911.

Bautista-Díaz E, Salazar-Cuytun R, Chay-Canul AJ, García-Herrera RA, Piñeiro-Vázquez AT, Magaña-Monforte JG, Tedeschi LO, Cruz-Hernández A, Gómez-Vázquez A. Determination of carcass traits in Pelibuey ewes using biometric measurements. Small Ruminant Research. 2017;147:115–119. doi: 10.1016/j.smallrumres.2016.12.037.

Canul-Solis J, Angeles-Hernandez JC, García-Herrera RA, del Razo-Rodríguez OE, Lee Rangel HA, Piñeiro-Vázquez AT, Casanova-Lugo F, Rosales-Nieto CA, Chay-Canul AJ. Estimation of body weight in hair ewes using an indirect measurement method. Tropical Animal Health and Production. 2020;52:2341–2347. doi: 10.1007/s11250-020-02232-7.

O’ Leary N, Leso L, Buckley F, Kenneally J, McSweeney D, Shalloo L. Validation of an automated body condition scoring system using 3D imaging. Agriculture. 2020;10(6):246. doi: 10.3390/agriculture10060246.

Ozkaya S, Bozkurt Y. The relationship of parameters of body measures and body weight by using digital image analysis in pre-slaughter cattle. Archives Animal Breeding. 2008;51(2):120–128. doi: 10.5194/aab-51-120-2008.

Tasdemir S, Urkmez A, Inal S. Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis. Computers and Electronics in Agriculture. 2011;76(2):189–197. doi: 10.1016/j.compag.2011.02.001.

Ozkaya S. Accuracy of body measurements using digital image analysis in female Holstein calves. Animal Production Science. 2012;52(10):917–920. doi: 10.1071/AN12006.

Chay-Canul AJ, Magaña-Monforte JG, Chizzotti ML, Piñeiro-Vázquez ÁT, Canul-Solís JR, Ayala-Burgos AJ, Ku-Vera JC, Tedeschi LO. Energy requirements of hair sheep in the tropical regions of Latin America. Review. Revista Mexicana de Ciencias Pecuarias. 2016;7(1):105–125.

AFRC. Energy and protein requirements of ruminants. Wallingford, UK: Agricultural and Food Research Council, CAB International; 1993. 159 pp.

Wongsriworaphon A, Arnonkijpanich B, Pathumnakul S. An approach based on digital image analysis to estimate the live weights of pigs in farm environments. Computers and Electronics in Agriculture. 2015;115:26–33. doi: 10.1016/j.compag.2015.05.004.

Tedeschi LO. Assessment of the adequacy of mathematical models. Agricultural Systems. 2006;89(2-3):225–247. doi: 10.1016/j.agsy.2005.11.004.

Cochran WG, Cox GM. Experimental Design. New York, US: John Wiley and Sons; 1957. 615 pp.

Loague K, Green RE. Statistical and graphical methods for evaluating solute transport models: overview and application. Journal of Contaminant Hydrology. 1991;7(1-2):51–73. doi: 10.1016/0169-7722(91)90038-3.

Mayer DG, Butler DG. Statistical validation. Ecological Modelling. 1993;68(1-2):21–32. doi: 10.1016/0304-3800(93)90105-2.

Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45(1):255–268. doi: 10.2307/2532051.

Wang Y, Yang W, Winter P, Walker L. Walk-through weighing of pigs using machine vision and an artificial neural network. Biosystems Engineering. 2008;100(1):117–125. doi: 10.1016/j.biosystemseng.2007.08.008.

Lasfeto DB, DaudLetik M. A measuring weight model of Timor's beef cattle based on image. International Journal of Engineering and Technology. 2017;9(2):677–688. doi: 10.21817/ijet/2017/v9i2/170902089.