Predictive biometrics of hair sheep through digital imaging
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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.
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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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/10.22319/rmcp.v7i1.4152
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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/10.21817/ijet/2017/v9i2/170902089
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