Journal of eISSN: 2377-4312 JDVAR

Dairy, Veterinary & Animal Research
Mini Review
Volume 5 Issue 4 - 2017
Advances in Automatic Detection of Body Condition Score of Cows: A Mini Review
Juan Rodríguez Alvarez3*, Mauricio Arroqui1,3, Pablo Mangudo1,3, Juan Toloza1,3, Daniel Jatip1,3, Juan M Rodríguez2, Alejandro Zunino2, Cristian Mateos2 and Claudio Machado1,3
1Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT), Argentina
2ISISTAN Research Institute - UNCPBA-CONICET, Argentina
3CIVETAN Research Institute - FCV UNCPBA-CONICET-CIC, Argentina
Received: June 02, 2017 | Published: June 26, 2017
*Corresponding author: Juan Rodríguez Alvarez, CIVETAN Research Institute, FCV-UNCPBA-CONICET-CIC, Campus Universitario, Tandil, Buenos Aires, Argentina, Email:
Citation: Alvarez JR, Arroqui M, Mangudo P, Toloza J, Jatip D et al. (2017) Advances in Automatic Detection of Body Condition Score of Cows: A Mini Review. J Dairy Vet Anim Res 5(4): 00149. DOI: 10.15406/jdvar.2017.05.00149

Abstract

BCS is a method to estimate body fat stores and accumulated energy balance of cows. This value influences productivity, reproduction, and health of cows. Therefore, it is important to monitor BCS to achieve a better animal response. In practice, this task is performed by expert scorers mainly visually, and could vary between scorers and be time-consuming. For this reason, several studies have tried to automate BCS by applying image analysis and machine learning techniques. An overview of selected studies is provided in this mini review.

Keywords: Precision livestock; Body condition score; Automatic detection; Image analysis

Abbreviations

BCS: Body Condition Score; ICT: Information and Communication Technology; 3D: Three-Dimensional

Introduction

The BCS system is a means of accurately determining body condition of cows, independent of body weight and frame size [1], using a 5-point scale with 0.25-point increments (with 1 representing emaciated cows and 5 representing obese cows) [2,3]. Extreme values of BCS are related with health risk, low productivity level and impaired pregnancy rate [4-7]. The subjectivity in the judgment of raters can lead to different scores for the same cow under consideration, or inconsistent scores of the same expert, which requires regular repeatability assessments [8]. As a result of the increasing availability of wide range of information and communication technology (ICT), more and higher-quality information to be available is expected in support of daily decision-making [9]. Consequently, there are multiple opportunities for automation and digitalization of livestock farming tasks, and different studies have particularly focused on automation of BCS. This brief review selects the most relevant and recent studies on the topic.

Discussion

Different authors have studied the feasibility of utilizing digital images to determine BCS. In this mini review relevant works later than 2007 and based on cow images from a top view were considered. In the Table 1 main characteristics and results from the selected papers are shown. Developed methods have two stages:

  1. Image analysis techniques to extract relevant characteristics (such as angles, distances and areas between anatomical points; intensity/depth pixels values; cow contour or a representation of it) to differentiate fat reserves levels of cows; Usage of collected characteristics to implement a BCS estimation model.
  2. Mostly, there are two types of models used: regression analysis models (as in [10-16]) and algorithms that measure cow’s body angularity (as in [17-19]) according to the hypothesis that the body shape of a fatter cow is rounder than that of a thin cow. Moreover, three automation levels are described. In the lowest level are [10,12,15], which require to manually identify anatomical points in the images to extract characteristics to develop the estimation models. In the medium level are [11,13,20], where the input images used are manually selected, but the rest of the process is automatic. Finally, in the highest level are [14,16-19], where the process is completely automated. Among the latter studies, only [17,18] carry out real time estimations (i.e. estimation result is showed to the user few seconds after the cow goes under the camera) because image preprocessing techniques (segmentation, normalization, features extraction) used in the other studies are time-consuming. In more recent studies the use of 3D cameras is more frequent. The use of thermal cameras [17,21], although allows an easy segmentation of the entire body of the cow (the warm cow shape highlight above its cold surroundings), are less common probably associated to a their higher costs. In [11] and [20] they used red breed dairy cows because the camera used to acquire the images has operational problems with black pigment cows. The selected studies applied different statistical metrics to estimate BCS visually observed by experts, and the most frequently used indicator was the accuracy of the automatic estimated scores to be within ±0.25 and within ±0.50 increment score of the manual BCS. However, more efficient computing processing methods based on powerful machine learning technique fated to improve BCS accuracy are under testing [22].

Work

Camera

Cow Breed

Dataset Size (# of Images)

Automation level

Real Time

Results

Bewley et al. [10]

2D Digital

Holstein-Fresian

834 (US-BCS), 767 (UK-BCS)

Low

NO

92.79% within 0.25, 100% within 0.5

Krukowski [11]

3D, ToF

SRB

351 (training), 120 (test)

Medium

NO

Test Set: 20% within 0.25, 46% within 0.5

Anglart [20]

3D, ToF

SRB

1329 (10% training, 90% test)

Medium

N/A

R=0.84.
69% within 0.25, 95% within 0.50

Azzaro et al. [12]

2D Digital

Holstein-Fresian

286

Low

NO

ErrorLOOCV=0.31

Halachmi et al. [17]

Termal

Holstein

172

High

YES

R=0.94

Bercovich et al. [13]

2D Digital

Holstein

87 (training), 64 (test)

Medium

NO

Test set: R2=0.64.
Around 50% within 0.25, around 100% within 0.75

Salau et al. [14]

3D, ToF

Fleckvieh

540 (for GLM with all features).
514 (for correlation analysis on individual features)

High

NO

RGLM2=0.7

Hansen et al. [18]

3D, Light Coding (RGB + depth sensor)

Holstein-Fresian

95

High

YES

N/D. Inverse relationship between angularity and BCS.  High repeatability scoring an individual cow (14/15).

Fischer et al. [15]

3D, Light Coding (RGB + depth)

Holstein

57 (training), 25 (test cows), 25 (test stage)

Low

NO

Test Set 1: R=0.89 y RMSE=0.31.
Test Set 2:
R=0.96 y RMSE=0.32

Shelley [19]

3D, Light Coding (RGB + depth sensor)

Holstein

18517

High

NO

71.35% within 0.25, 93.91% within 0.5

Spoliansky et al. [16]

3D, Light Coding (RGB + depth sensor)

N/A

11824

High

NO

R2=0.75.
74% within 0.25, 91% within 0.5

Table 1: General characteristics and results of BCS estimation systems.

2D: Two Dimensional; 3D: Three-Dimensional; ToF: Time-of-Flight; SRB: Swedish Red Breed; GLM: Generalized Linear Model; US-BCS: United State Body Condition Score; UK-BCS: United Kingdom Body Condition Score; R: Correlation Coefficient; R2: Coefficient of Determination; LOOCV: Leave One Out Cross Validation; RMSE: Root Mean Square Error

Conclusion

The literature attempts to automate BCS assessment look promising as a tool for supporting cattle decision-making, in a context where ICT technology is becoming more efficient, productive, and cheaper. Acceptable accuracy within the range of human error have been reported, with room for improvement as more effective computing processing methods became available.

References

  1. EE Wildman, GM Jones, PE Wagner, RL Boman, HF Troutt, et al. (1982) A dairy cow body condition scoring system and its relationship to selected production characteristics. Journal of Dairy Science 65(3): 495-501.
  2. James D Ferguson, David T Galligan, Neal Thomsen (1994) Principal descriptors of body condition score in holstein cows. Journal of Dairy Science 77(9): 2695-2703.
  3. JD Ferguson, G Azzaro, G Licitra (2006) Body condition assessment using digital images. Journal of dairy science 89(10): 3833-3841.
  4. AJ Heinrichs, C Jones, VA Ishler (2017) Body condition scoring as a tool for dairy herd management. Technical report, Penn State College of Agricultural Sciences.
  5. Wayne Kellogg (2010) Body condition scoring with dairy cattle. Technical report, Division of Agriculture, University of Arkansas, p. 1-6.
  6. O Markusfeld, N Galon, E Ezra (1997) Body condition score, health, yield and fertility in dairy cows. The Veterinary Record 141(3): 67-72.
  7. John R Roche, Nicolas Charles Friggens, Jane K Kay, Mark W Fisher, Kevin J Stafford, et al. (2009) Invited review: Body condition score and its association with dairy cow productivity, health, and welfare. J Dairy Sci 92(12): 5769-5801.
  8. E Vasseur, J Gibbons, J Rushen, AM de Passillé (2013) Development and implementation of a training program to ensure high repeatability of body condition scoring of dairy cows. J Dairy Sci 96(7): 4725-4737.
  9. Sander JC Janssen, Cheryl H Porter, Andrew D Moore, Ioannis N Athanasiadis, Ian Foster, et al. (2016) Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology. Agricultural Systems 155: 200-212.
  10. JM Bewley, AM Peacock, O Lewis, RE Boyce, DJ Roberts, et al. (2008) Potential for estimation of body condition scores in dairy cattle from digital images. Journal of Dairy Science 91(9): 3439-3453.
  11. Marilyn Krukowski (2009) Automatic determination of body condition score of dairy cows from 3d images. Master’s thesis. P. 1-89.
  12. Azzaro G, Caccamo M, Ferguson JD, Battiato S, Farinella GM et al. (2011) Objective estimation of body condition score by modeling cow body shape from digital images. Journal of Dairy Science 94(4): 2126-2137.
  13. Bercovich A, Edan Y, Alchanatis V, Moallem U, Parmet Y, et al. (2013) Development of an automatic cow body condition scoring using body shape signature and fourier descriptors. J Dairy Sci 96(12): 8047-8059.
  14. Salau J, Haas JH, Junge W, Bauer U, Harms J, et al. (2014) Feasibility of automated body trait determination using the sr4k time-of-flight camera in cow barns. Springerplus 3: 225.
  15. Amélie Fischer, T Luginbühl, L Delattre, JM Delouard, Philippe Faverdin (2015) Rear shape in 3 dimensions summarized by principal component analysis is a good predictor of body condition score in holstein dairy cows. J Dairy Sci 98(7): 4465-4476.
  16. Roii Spoliansky, Yael Edan, Yisrael Parmet, Ilan Halachmi (2016) Development of automatic body condition scoring using a low-cost 3-dimensional kinect camera. Journal of Dairy Science 99(9): 7714-7725.
  17. I Halachmi, M Klopčič, P Polak, DJ Roberts, JM Bewley (2013) Automatic assessment of dairy cattle body condition score using thermal imaging. Computers and Electronics in Agriculture 99: 35-40.
  18. Mark Hansen, Melvyn Smith, Lyndon Smith, Ian Hales, Duncan Forbes (2015) Non-intrusive automated measurement of dairy cow body condition using 3d video. Proceedings of the Machine Vision of Animals and their Behaviour (MVAB) p. 1.1-1.8.
  19. Anthony N Shelley (2016) Incorporating machine vision in precision dairy farming technologies. PhD thesis, University of Kentucky.
  20. Dorota Anglart (2010) Automatic estimation of body weight and body condition score in dairy cows using 3d imaging technique. Master’s thesis.
  21. I Halachmi, P Polak, DJ Roberts, M Klopcic (2008) Cow body shape and automation of condition scoring. J Dairy Sci 91(11): 4444–4451.
  22. Juan Rodríguez Alvarez, Mauricio Arroqui, Pablo Mangudo, Juan Toloza, Daniel Jatip, et al. (2017) Body condition estimation on cows from 3d images using convolutional neural networks. I International Conference on Agro BigData and Decision Support Systems in Agriculture.
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