Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device

Hansen, Mark F.; Smith, Melvyn L.; Smith, Lyndon; Jabbar, Khalid Abdul; Forbes, David C.

doi:10.1016/j.compind.2018.02.011

Cited by 124 publications

(42 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Machine vision is already making an early impact in animal monitoring, e.g. for weight estimation, body condition monitoring [36] and illness detection [37] in pigs, cattle and poultry. Individual animal identification, e.g.…”

Section: Robotic Visionmentioning

confidence: 99%

Agricultural Robotics: The Future of Robotic Agriculture

et al. 2018

View full text Add to dashboard Cite

Section: Robotic Visionmentioning

confidence: 99%

Agricultural Robotics: The Future of Robotic Agriculture

et al. 2018

View full text Add to dashboard Cite

“…However, to be able to compare our BCS classification performance to those of other studies, we have to calculate the MAE of our full model as well. Our full model yielded an MAE of 0.15 BCS units, which is less than the MAE of 0.28 BCS units reported by Fischer et al (2015), the 0.26 BCS units reported by Spoliansky et al (2016), and the 0.21 BCS units reported by Hansen et al (2018). These error differences may be due to the differences in the body regions used to extract features.…”

Section: Improvement Of Automated Bcs Classificationmentioning

confidence: 58%

Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions

Song

Bokkers

Mourik

et al. 2019

Journal of Dairy Science

View full text Add to dashboard Cite

Machine vision technology has been used in automated body condition score (BCS) classification of dairy cows. The current vision-based classifications use information acquired from a limited number of body regions of the cow. Our study aimed to improve automated BCS classification by including multiple body condition-related features extracted from 3 viewpoints in 8 body regions. The data set of this study included 44 lactating cows with their BCS evenly distributed over the scale of BCS from 1.5 to 4.5 units. The body images of these cows were recorded over 2 consecutive days using 3-dimensional cameras positioned to view the cow from the top, right side, and rear. Each image was automatically processed to identify anatomical landmarks on the body surface. Around these anatomical landmarks, the bony prominences and body surface depressions were quantified to describe 8 body condition-related features. A manual BCS of each cow was independently assigned by 2 trained assessors using the same predefined protocol. With the extracted features as inputs and manual BCS as the reference, we built a nearest-neighbor classification model to classify BCS and obtained an overall classification sensitivity of 0.72 using a 10-fold cross-validation. We conclude that the sensitivity of automated BCS classification has been improved by expanding the selection of body condition-related features extracted from multiple body regions.

show abstract

“…That system also introduced variations in BCS trends during the calving and lactation intervals using values for monthly mean height. In [ 14 ], a low-cost monitoring system was proposed for unobtrusively and regularly monitoring BCS, lameness, and weight using 3D imaging technology. In the paper described in [ 14 ], a new approach for assessing BCS based on a rolling ball algorithm was validated by achieving repeatability within ±0.25 BCS.…”

Section: Related Workmentioning

confidence: 99%

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

Zin

Seint

Tin

et al. 2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device

Cited by 124 publications

References 35 publications

Agricultural Robotics: The Future of Robotic Agriculture

Agricultural Robotics: The Future of Robotic Agriculture

Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions

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

Contact Info

Product

Resources

About