dc.contributor.author |
Summerfield, Gary I.
|
|
dc.contributor.author |
De Freitas, Allan
|
|
dc.contributor.author |
Van Marle-Koster, Este
|
|
dc.contributor.author |
Myburgh, Hermanus Carel
|
|
dc.date.accessioned |
2024-07-18T12:43:08Z |
|
dc.date.available |
2024-07-18T12:43:08Z |
|
dc.date.issued |
2023-11 |
|
dc.description |
DATA AVAILABITY STATEMENT: The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy concerns. |
en_US |
dc.description.abstract |
Body condition scoring is an objective scoring method used to evaluate the health of a cow by determining the amount of subcutaneous fat in a cow. Automated body condition scoring is becoming vital to large commercial dairy farms as it helps farmers score their cows more often and more consistently compared to manual scoring. A common approach to automated body condition scoring is to utilise a CNN-based model trained with data from a depth camera. The approaches presented in this paper make use of three depth cameras placed at different positions near the rear of a cow to train three independent CNNs. Ensemble modelling is used to combine the estimations of the three individual CNN models. The paper aims to test the performance impact of using ensemble modelling with the data from three separate depth cameras. The paper also looks at which of these three cameras and combinations thereof provide a good balance between computational cost and performance. The results of this study show that utilising the data from three depth cameras to train three separate models merged through ensemble modelling yields significantly improved automated body condition scoring accuracy compared to a single-depth camera and CNN model approach. This paper also explored the real-world performance of these models on embedded platforms by comparing the computational cost to the performance of the various models. |
en_US |
dc.description.department |
Animal and Wildlife Sciences |
en_US |
dc.description.department |
Electrical, Electronic and Computer Engineering |
en_US |
dc.description.sdg |
SDG-02:Zero Hunger |
en_US |
dc.description.sdg |
SDG-09: Industry, innovation and infrastructure |
en_US |
dc.description.sponsorship |
The Milk South Africa (MilkSA). |
en_US |
dc.description.uri |
https://www.mdpi.com/journal/sensors |
en_US |
dc.identifier.citation |
Summerfield, G.I.;
De Freitas, A.; van Marle-Koster, E.;
Myburgh, H.C. Automated Cow
Body Condition Scoring Using
Multiple 3D Cameras and
Convolutional Neural Networks.
Sensors 2023, 23, 9051. https://doi.org/10.3390/s23229051. |
en_US |
dc.identifier.issn |
1424-8220 (online) |
|
dc.identifier.other |
10.3390/s23229051 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/97104 |
|
dc.language.iso |
en |
en_US |
dc.publisher |
MDPI |
en_US |
dc.rights |
© 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license. |
en_US |
dc.subject |
Automated cow body condition scoring |
en_US |
dc.subject |
Computer vision |
en_US |
dc.subject |
Ensemble modelling |
en_US |
dc.subject |
Sensor fusion |
en_US |
dc.subject |
Precision livestock |
en_US |
dc.subject |
Data augmentation |
en_US |
dc.subject |
Convolutional neural network (CNN) |
en_US |
dc.subject |
SDG-02: Zero hunger |
en_US |
dc.subject |
SDG-09: Industry, innovation and infrastructure |
en_US |
dc.title |
Automated cow body condition scoring using multiple 3D cameras and convolutional neural networks |
en_US |
dc.type |
Article |
en_US |