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Open AccessLetter

Body Condition Score Estimation Based on Regression Analysis Using a 3D Camera

1
Graduate School of Engineering, University of Miyazaki, 1 Chome-1 Gakuenkibanadainishi, Miyazaki 889-2192, Japan
2
Center for Animal Disease Control, University of Miyazaki, 1 Chome-1 Gakuenkibanadainishi, Miyazaki 889-2192, Japan
3
Field Science Center, Faculty of Agriculture, University of Miyazaki, 1 Chome-1 Gakuenkibanadainishi, Miyazaki 889-2192, Japan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3705; https://doi.org/10.3390/s20133705
Received: 29 May 2020 / Revised: 22 June 2020 / Accepted: 29 June 2020 / Published: 2 July 2020
(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications)
The Body Condition Score (BCS) for cows indicates their energy reserves, the scoring for which ranges from very thin to overweight. These measurements are especially useful during calving, as well as early lactation. Achieving a correct BCS helps avoid calving difficulties, losses and other health problems. Although BCS can be rated by experts, it is time-consuming and often inconsistent when performed by different experts. Therefore, the aim of our system is to develop a computerized system to reduce inconsistencies and to provide a time-saving solution. In our proposed system, the automatic body condition scoring system is introduced by using a 3D camera, image processing techniques and regression models. The experimental data were collected on a rotary parlor milking station on a large-scale dairy farm in Japan. The system includes an application platform for automatic image selection as a primary step, which was developed for smart monitoring of individual cows on large-scale farms. Moreover, two analytical models are proposed in two regions of interest (ROI) by extracting 3D surface roughness parameters. By applying the extracted parameters in mathematical equations, the BCS is automatically evaluated based on measurements of model accuracy, with one of the two models achieving a mean absolute percentage error (MAPE) of 3.9%, and a mean absolute error (MAE) of 0.13. View Full-Text
Keywords: body condition score; 3D surface roughness parameters; rotary parlor; 3D camera; regression analysis body condition score; 3D surface roughness parameters; rotary parlor; 3D camera; regression analysis
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MDPI and ACS Style

Zin, T.T.; Seint, P.T.; Tin, P.; Horii, Y.; Kobayashi, I. Body Condition Score Estimation Based on Regression Analysis Using a 3D Camera. Sensors 2020, 20, 3705. https://doi.org/10.3390/s20133705

AMA Style

Zin TT, Seint PT, Tin P, Horii Y, Kobayashi I. Body Condition Score Estimation Based on Regression Analysis Using a 3D Camera. Sensors. 2020; 20(13):3705. https://doi.org/10.3390/s20133705

Chicago/Turabian Style

Zin, Thi T.; Seint, Pann T.; Tin, Pyke; Horii, Yoichiro; Kobayashi, Ikuo. 2020. "Body Condition Score Estimation Based on Regression Analysis Using a 3D Camera" Sensors 20, no. 13: 3705. https://doi.org/10.3390/s20133705

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