Estimation of Body Weight Using Body Volume Determined from Three-Dimensional Images for Korean Cattle

Dong Hwa Jang; Chulsoo Kim; Yong Hyeon Kim

doi:10.12791/KSBEC.2021.30.4.393

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2021 Vol.30, Issue 4 Preview Page Next Page

Original Articles

Estimation of Body Weight Using Body Volume Determined from Three-Dimensional Images for Korean Cattle 한우의 3차원 영상에서 결정된 몸통 체적을 이용한 체중 추정

31 October 2021. pp. 393-400

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Abstract

Body weight of livestock is a crucial indicator for assessing feed requirements and nutritional status. This study was performed to estimate the body weight of Korean cattle (Hanwoo) using body volume determined from three-dimensional (3-D) image. A TOF camera with a resolution of 640×480 pixels, a frame rate of 44 fps and a field of view of 47°(H)×37°(V) was used to capture the 3-D images for Hanwoo. A grid image of the body was obtained through preprocessing such as separating the body from background and removing outliers from the obtained 3-D image. The body volume was determined by numerical integration using depth information to individual grid. The coefficient of determination for a linear regression model of body weight and body volume for calibration dataset was 0.8725. On the other hand, the coefficient of determination was 0.9083 in a multiple regression model for estimating body weight, in which the age of Hanwoo was added to the body volume as an explanatory variable. Mean absolute percentage error and root mean square error in the multiple regression model to estimate the body weight for validation dataset were 8.2% and 24.5kg, respectively. The performance of the regression model for weight estimation was improved and the effort required for estimating body weight could be reduced as the body volume of Hanwoo was used. From these results obtained, it was concluded that the body volume determined from 3-D of Hanwoo could be used as an effective variable for estimating body weight.

Keywords

body traits

depth information

Hanwoo

regression mode

TOF camera

가축의 체중은 사료 요구량과 영양 상태를 평가하는 데 필요한 주요 지표에 해당한다. 본 연구는 한우의 3-D 영상으로부터 몸통 체적을 산출한 후 체중을 추정하고자 시도되었다. 한우의 3-D 영상 획득에 640×480 픽셀의 해상도, 44fps의 프레임속도 및 47°(H)×37°(V)의 화각을 갖는 TOF 카메라가 사용되었다. 획득된 3-D 영상에서 배경과 몸통 분리, 이상치 제거 등의 전처리 과정을 거쳐서 몸통에 대한 격자 영상을 얻었다. 또한 각각의 격자에 깊이 정보를 적용한 수치적분으로 몸통 체적을 결정하였다. Calibration dataset에서 체중과 몸통체적의 선형회귀에 대한 결정계수는 0.8725로 나타났다. 한편 몸통 체적에 월령을 설명 변수로 추가한 체중 추정의 중회귀 모형에서 결정계수는 0.9083으로 나타났다. Validation dataset에서 중회귀 모형을 이용한 체중 추정의 MAPE와 RMSE는 각각 8.2%, 24.5kg으로 나타났다. 결과적으로 체중 추정을 위한 회귀 모형의 성능이 개선되고, 체중 추정에 소요되는 노력이 절감됨을 고려한다면 3-D 영상에서 결정된 몸통 체적이 한우의 체중 추정에 유효한 변수로 사용될 것이다.

키워드

TOF 카메라

깊이 정보

체형 특성

한우

회귀 모형

References

Brandl N., and E. Jorgensen 1996, Determination of live weight of pigs from dimensions measured using image analysis. Comput Electron Agric 15:57-72. doi:10.1016/0168-1699(96)00003-8 10.1016/0168-1699(96)00003-8

Garrido M., D.S. Paraforos, D. Reiser, M.V. Arellano, H.W. Griepentrog, and C. Valero 2015, 3D maize plant reconstruction based on georeferenced overlapping LiDAR point clouds. Remote Sens 7:17077-17096. doi:10.3390/rs71215870 10.3390/rs71215870

Gionbelli M.P., M.S. Duarte, S.C. Valadares Filho, E. Detmann, M.L. Chizzotti, F.C. Rodrigues, D. Zanetti, T.R.S. Gionbelli, and M.G. Machado 2015, Achieving body weight adjustments for feeding status and pregnant or non-pregnant condition in beef cows. PLoS ONE 10:e0112111. doi:10.1371/journal.pone.0112111 10.1371/journal.pone.011211125793770PMC4368534

Haile-Mariam M., O. Gonzalez-Recio, and J.E. Pryce 2014, Prediction of liveweight of cows from type traits and its relationship with production and fitness traits. J Dairy Sci 97:3173-3189. doi:10.3168/jds.2013-7516 10.3168/jds.2013-751624630649

Hansen M.F., M.L. Smith, L.N. Smith, K.A. Jabbar, and D. Forbes 2018, Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device. Comput Ind 98:14-22. doi:10.1016/j.compind.2018.02.011 10.1016/j.compind.2018.02.01129997403PMC6034442

Heinrichs A.J., G.W. Rogers, and J.B. Cooper 1992, Predicting body weight and wither height in Holstein heifers using body measurements. J Dairy Sci 75:3576-3581. doi:10.3168/ jds.S0022-0302(92)78134-X 10.3168/jds.S0022-0302(92)78134-X

Huang L., S. Li, A. Zhu, X. Fan, C. Zhang, and H. Wang 2018, Non-contact body measurement for Qinchuan cattle with LiDAR sensor. Sens 18:3014-30304. doi:10.3390/s18093014 10.3390/s1809301430205607PMC6164280

Jang D.H., C.S. Kim, Y.G. Ko, and Y.H. Kim 2020, Estimation of body weight for Korean cattle using three-dimensional image. J Biosyst Eng 45:325-332 doi:10.1007/s42853-020-00073-8 10.1007/s42853-020-00073-8

Jang D.H., H.T. Kim, and Y.H. Kim 2019, Estimation of the dimensions of horticultural products and the mean plant height of plug seedlings using three-dimensional images. Protected Hort Plant Fac 28:358-365. (in Korean) doi:10.12791/KSBEC.2019.28.4.358 10.12791/KSBEC.2019.28.4.358

Kashiha M., C. Bahr, S. Ott, C.P.H. Moons, T.A. Niewold, F.O. Odberg, and D. Berckmans 2014, Automatic weight estimation of individual pigs using image analysis. Comput Electron Agric 107:38-44. doi:10.1016/j.compag.2014.06.003 10.1016/j.compag.2014.06.003

Kawasue K., T. Ikeda, T. Tokunaga, and H. Harada 2013, Three-dimensional shape measurement system for black cattle using KINECT sensor. Int J Circuits, Syst Signal Process 7:222-230.

Kramer O. 2013, K-Nearest Neighbors. In Dimensionality reduction with unsupervised nearest neighbors, Springer, Berlin, Germany, pp.13-22. doi:10.1007/978-3-642-38652-7_2 10.1007/978-3-642-38652-7_2

Lee M.Y., and B.K. Ohh 1985, Relation and estimation heritabilities for body weight and body measurements of Korean cattle (Hanwoo). Kor J of Anim Sci 27:691-695. (in Korean)

Ozkaya S., and Y. Bozkurt 2008, The relationship of parameters of body measures and body weight by using digital image analysis in pre-slaughter cattle. Arch Anim Breed 51:120-128. doi:10.5194/aab-51-120-2008 10.5194/aab-51-120-2008

Pezzuolo A., M. Guarino, L. Sartori, and F. Marinello 2018, A feasibility study on the use of a structured light depth-camera for three-dimensional body measurements of dairy cows in free-stall barns. Sens 18:673-687. doi:10.3390/s18020673 10.3390/s1802067329495290PMC5856155

Rusu R.B., Z.C. Marton, N. Blodow, M. Dolha, and M. Beetz 2008, Towards 3D point loud based object maps for household environments. Robot Auton Syst 56:927-941. doi:10.1016/j.robot.2008.08.005 10.1016/j.robot.2008.08.005

Seo K.W., H.T. Kim, D.W. Lee, Y.C. Yoon, and D.Y. Choi 2011, Image processing algorithm for weight estimation of dairy cattle. J Biosyst Eng 36:48-57. (in Korean) doi:10.5307/JBE.2011.36.1.48 10.5307/JBE.2011.36.1.48

Song X., E.A.M. Bokkers, P.P.J. van der Tol, P.W.G. Groot Koerkamp, and S. van Mourik 2018, Automated body weight prediction of dairy cows using 3-dimensional vision. J Dairy Sci 101:4448-4459. doi:10.3168/jds.2017-13094 10.3168/jds.2017-1309429477535

Spoliansky R., Y. Edan, Y. Parmet, and I. Halachmi 2016, Development of automatic body condition scoring using a low-cost 3-dimensional Kinect camera. J Dairy Sci 99: 7714-7725. doi:10.3168/jds.2015-10607 10.3168/jds.2015-1060727320661

Tasdemir S., A. Urkmez, and S. Inal 2011, Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis. Comput Electron Agrice 76:189-197. doi:10.1016/j.compag.2011.02.001 10.1016/j.compag.2011.02.001

Vázquez-Arellano M., H.W. Griepentrog, D. Reiser, and D.S. Paraforos 2016, 3-D imaging systems for agricultural applications - A review. Sens 16:618-641. doi:10.3390/s16050618 10.3390/s1605061827136560PMC4883309

Viazzi S., C. Bahr, T. van Hertem, A. Schlageter-Tello, C.E.B. Romanini, I. Halachmi, C. Lokhorst, and D. Berckmans 2014, Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows. Comput Electron Agric 100:139-147. doi:10.1016/j.compag.2013.11.005 10.1016/j.compag.2013.11.005

Wu J., R. Tillett, N. McFarlane, X. Ju, J.P. Siebert, and P. Schofield 2004, Extracting the three-dimensional shape of live pigs using stereo photogrammetry. Comput Electron Agric 44:203-222. doi:10.1016/j.compag.2004.05.003 10.1016/j.compag.2004.05.003

Information

Publisher :The Korean Society for Bio-Environment Control
Publisher(Ko) :(사)한국생물환경조절학회
Journal Title :Journal of Bio-Environment Control
Journal Title(Ko) :생물환경조절학회지
Volume : 30
No :4
Pages :393-400
Received Date : 2021-08-09
Revised Date : 2021-10-07
Accepted Date : 2021-10-25
DOI :https://doi.org/10.12791/KSBEC.2021.30.4.393

Journal of Bio-Environment Control ISSN:1229-4675(Print) 2765-3641(Online) 생물환경조절학회지

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