Extracting body surface dimensions from top-view images of pigs

Authors

  • Mingzhou Lu 1. College of Engineering/Jiangsu Province Engineering Lab for Modern Facility Agriculture Technology & Equipment, Nanjing Agricultural University, Nanjing 210031, China; 2. M3-BIORES- Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium
  • Tomas Norton M3-BIORES- Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium
  • Ali Youssef M3-BIORES- Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium
  • Nemanja Radojkovic M3-BIORES- Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium
  • Alberto Peña Fernández M3-BIORES- Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium
  • Daniel Berckmans M3-BIORES- Measure, Model & Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, B-3001 Leuven, Belgium

DOI:

https://doi.org/10.25165/ijabe.v11i5.4054

Keywords:

body surface dimension, image analysis, skeleton, triangulated network, ellipse fitting

Abstract

Continuous live weight and carcass traits estimation are important for the pig production and breeding industry. It is widely known that top-view images of a pig’s body (excluding its head and neck) reveal surface dimension parameters, which are correlated with live weight and carcass traits. However, because a pig is not constrained when an image is captured, the body does not always have a straight posture. This creates a big challenge when extracting the body surface dimension parameters, and consequently the live weight and carcass traits estimation has a high level of uncertainty. The primary goal of this study is to propose an algorithm to automatically extract pig body surface dimension parameters, with a better accuracy, from top-view pig images. Firstly, the backbone line of a pig was extracted. Secondly, lengths of line segments perpendicular to the backbone line were calculated, and then feature points on the pig’s contour line were extracted based on the lengths variation of the perpendicular line segments. Thirdly, the head and neck of the pig were removed from the pig’s contour by an ellipse. Finally, four length and one area parameters were calculated. The proposed algorithm was implemented in Matlab® (R2012b) and applied to 126 depth images of pigs. Taking the results of the manual labeling tool as the gold standard, the length and area parameters could be obtained by the proposed algorithm with an accuracy of 97.71% (SE=1.64%) and 97.06% (SE=1.82%), respectively. These parameters can be used to improve pig live weight and carcass traits estimation accuracy in the future work. Keywords: body surface dimension, image analysis, skeleton, triangulated network, ellipse fitting DOI: 10.25165/j.ijabe.20181105.4054 Citation: Lu M Z, Norton T, Youssef A, Radojkovic N, Fernández A P, Berckmans D. Extracting body surface dimensions from top-view images of pigs. Int J Agric & Biol Eng, 2018; 11(5): 182–191.

References

Jørgen K. Estimation of pig weight using a Microsoft Kinect prototype imaging system. Computers and Electronics in Agriculture, 2014; 109(11): 32–35.

Schofield C P, Marchant J A, White R P, Brand N, Wilson M. Monitoring pig growth using a prototype imaging system. Journal of Agricultural Engineering Research, 1999; 72(3): 205–210.

Wang Y S, Yang W, Walker L T, Rababah T M. Enhancing the accuracy of area extraction in machine vision-based pig weighing through edge detection. International Journal of Agricultural and Biological Engineering, 2008; 1(1): 37–42.

Brown C. Who matters? The changing market: Perspective from multiple retailers. In the appliance of pig science. British Society of Animal Science, 2004; 31: 19–22.

Shi C, Teng G H, Li Z. An approach of pig weight estimation using binocular stereo system based on LabVIEW. Computers and Electronics in Agriculture, 2016; 129(1): 37–43.

Doeschl A B, Green D M, Whittemore C T, Schofield C P, Fisher A V, Knap P W. The relationship between the body shape of living pigs and their carcass morphology and composition. Animal Science, 2004; 79: 73–83.

White R P, Schofield C P, Green D M, Parsons D J, Whittemore C T. The effectiveness of a visual image analysis (VIA) system for monitoring the performance of growing/finishing pigs. Animal Science, 2004; 78(3): 409–418.

Collewet G, Bogner P, Allen P, Busk H, Dobrowolski A, Olsen E, et al. Determination of the lean meat percentage of pig carcasses using magnetic resonance imaging. Meat Science, 2005; 70(4): 563–572.

Kusec G, Baulain U, Kallweit E, Glodek P. Influence of MHS genotype and feeding regime on allometric and temporal growth of pigs assessed by magnetic resonance imaging. Livestock Science, 2007; 110: 89–100.

Schinkel A P, Einstein M E, Jungst S, Booher C, Newman S. Evaluation of different mixed model nonlinear functions to describe the body weight growth of pigs of different sire and dam lines. Prof. Anim. Sci., 2009; 25: 307–324.

Kashiha M, Bahr C, Ott S, Moons C P H, Niewold T A, Ödberg F O, et al. Automatic weight estimation of individual pigs using image analysis. Computers and Electronics in Agriculture, 2014; 107: 38–44.

Marchant J A, Schofield C P, White R P. Pig growth and conformation monitoring using image analysis. J. Anim. Sci., 1999; 68: 141–150.

Fisher A V, Green D M, Whittemore C T, Wood J D, Schofield C P. Growth of carcass components and its relation with conformation in pigs of three types. Meat Sci., 2003; 65: 639–650.

Green D M, Brotherstone S, Schofield C P. Food intake and live growth performance of pigs measured automatically and continuously from 25 to 115 kg live weight. J. Sci. Food Agric., 2003; 83: 1150–1155.

Brandl N, Jørgensen E. Determination of live weight of pigs from dimensions measured using image analysis. Computers and Electronics in Agriculture, 1996; 15: 57–72.

Whittemore C T, Schofield C P. A case for size and shape scaling for understanding nutrient use in breeding sows and growing pigs. Livestock Prod. Sci., 2000; 65: 203–208.

Banhazi T M, Tscharke M, Ferdous W M, Saunders C, Lee S H. Using image analysis and statistical modelling to achieve improved pig weight predictions. Biennial Conference of the Australian Society for Engineering in Agriculture (SEAg), 2009; pp.13–16.

Apirachai W, Banchar A, Supachai P. An approach based on digital image analysis to estimate the live weights of pigs in farm environments. Computers and Electronics in Agriculture, 2015; 115(7): 26–33.

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: 189–197.

Mollah Md B R, Hasan Md A, Salam Md A, Ali Md A. Digital image analysis to estimate the live weight of broiler. Computers and Electronics in Agriculture, 2010; 72(1): 48–52.

Liu T H, Teng G H, Fu W S, Li Z. Extraction algorithms and applications of pig body size measurement points based on computer vision. Transactions of the CSAE, 2013; 29(2): 161–168. (in Chinese)

Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems Man and Cybernetics, 1979; 9(1): 62–66.

Haralick R M, Shapiro L G. Computer and Robot Vision, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1992.

Halif R, Flusser J. Numerically stable direct least squares fitting of ellipses. Proc. Sixth Int’l Conf. Computer Graphics and Visualization, 1998; pp.125–132.

Lu M Z, Xiong Y J, Li K Q, Liu L S, Yan L, Ding Y Q, et al. An automatic splitting method for the adhesive piglets’ gray scale image based on the ellipse shape feature. Computers and Electronics in Agriculture, 2016; 120(1): 53–62.

Barber C B, Dobkin D P, Huhdanpaa H T. The Quickhull Algorithm for Convex Hulls. ACM Trans. on Mathematical Software, 1996; 22(4), 469–483.

Kashiha M, Bahr C, Haredasht S A, Ott S, Moons C P H, Niewold T A, et al. The automatic monitoring of pigs water use by cameras. Computers and Electronics in Agriculture, 2013; 90: 164–169.

Nasirahmadi A, Hensel O, Edwards S A, Sturm B. Automatic detection of mounting behaviors among pigs using image analysis. Computers and Electronics in Agriculture, 2016; 124: 295–302.

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Published

2018-09-29

How to Cite

Lu, M., Norton, T., Youssef, A., Radojkovic, N., Fernández, A. P., & Berckmans, D. (2018). Extracting body surface dimensions from top-view images of pigs. International Journal of Agricultural and Biological Engineering, 11(5), 182–191. https://doi.org/10.25165/ijabe.v11i5.4054

Issue

Vol. 11 No. 5 (2018): IJABE

Section

Information Technology, Sensors and Control Systems

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