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A computer vision approach to improving cattle digestive health by the monitoring of faecal samples

Atkinson, Gary A.; Smith, Lyndon N.; Smith, Melvyn L.; Reynolds, Christopher K.; Humphries, David J.; Moorby, Jon M.; Leemans, David K.; Kingston-Smith, Alison H.

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Authors

Lyndon Smith Lyndon.Smith@uwe.ac.uk
Professor in Computer Simulation and Machine

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Melvyn Smith Melvyn.Smith@uwe.ac.uk
Research Centre Director Vision Lab/Prof

Christopher K. Reynolds

David J. Humphries

Jon M. Moorby

David K. Leemans

Alison H. Kingston-Smith



Abstract

The digestive health of cows is one of the primary factors that determine their well-being and productivity. Under- and over-feeding are both commonplace in the beef and dairy industry; leading to welfare issues, negative environmental impacts, and economic losses. Unfortunately, digestive health is difficult for farmers to routinely monitor in large farms due to many factors including the need to transport faecal samples to a laboratory for compositional analysis. This paper describes a novel means for monitoring digestive health via a low-cost and easy to use imaging device based on computer vision. The method involves the rapid capture of multiple visible and near-infrared images of faecal samples. A novel three-dimensional analysis algorithm is then applied to objectively score the condition of the sample based on its geometrical features. While there is no universal ground truth for comparison of results, the order of scores matched a qualitative human prediction very closely. The algorithm is also able to detect the presence of undigested fibres and corn kernels using a deep learning approach. Detection rates for corn and fibre in image regions were of the order 90%. These results indicate the potential to develop this system for on-farm, real time monitoring of the digestive health of individual animals, allowing early intervention to effectively adjust feeding strategy.

Citation

Atkinson, G. A., Smith, L. N., Smith, M. L., Reynolds, C. K., Humphries, D. J., Moorby, J. M., …Kingston-Smith, A. H. (2020). A computer vision approach to improving cattle digestive health by the monitoring of faecal samples. Scientific Reports, 10, https://doi.org/10.1038/s41598-020-74511-0

Journal Article Type Article
Acceptance Date Sep 23, 2020
Online Publication Date Oct 16, 2020
Publication Date Oct 16, 2020
Deposit Date Oct 8, 2020
Publicly Available Date Oct 8, 2020
Journal Scientific Reports
Electronic ISSN 2045-2322
Publisher Nature Research (part of Springer Nature)
Peer Reviewed Peer Reviewed
Volume 10
Article Number 17557
DOI https://doi.org/10.1038/s41598-020-74511-0
Keywords Cattle digestive health; animal welfare; faecal consistency; computer vision; 3D imaging; convolutional neural network
Public URL https://uwe-repository.worktribe.com/output/6762753

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