Automated cow body condition scoring using multiple 3D cameras and convolutional neural networks

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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


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