Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.09259 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911941466783744 |
|---|---|
| author | Chelotti, José Martinez-Rau, Luciano Ferrero, Mariano Vignolo, Leandro Galli, Julio Planisich, Alejandra Rufiner, H. Leonardo Giovanini, Leonardo |
| author_facet | Chelotti, José Martinez-Rau, Luciano Ferrero, Mariano Vignolo, Leandro Galli, Julio Planisich, Alejandra Rufiner, H. Leonardo Giovanini, Leonardo |
| contents | Livestock feeding behaviour is an influential research area for those involved in animal husbandry and agriculture. In recent years, there has been a growing interest in automated systems for monitoring the behaviour of ruminants. Despite the developments accomplished in the last decade, there is still much to do and learn about the methods for measuring and analysing livestock feeding behaviour. Automated monitoring systems mainly use motion, acoustic, and image sensors to collect animal behavioural data. The performance evaluation of existing methods is a complex task and direct comparisons between studies are difficult. Several factors prevent a direct comparison, starting from the diversity of data and performance metrics used in the experiments. To the best of our knowledge, this work represents the first tutorial-style review on the analysis of the feeding behaviour of ruminants, emphasising the relationship between sensing methodologies, signal processing, and computational intelligence methods. It assesses the main sensing methodologies (i.e. based on movement, sound, images/videos, and pressure) and the main techniques to measure and analyse the signals associated with feeding behaviour, evaluating their use in different settings and situations. It also highlights the potentiality of automated monitoring systems to provide valuable information that improves our understanding of livestock feeding behaviour. The relevance of these systems is increasingly important due to their impact on production systems and research. Finally, the paper closes by discussing future challenges and opportunities in livestock feeding behaviour monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_09259 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Livestock feeding behaviour: A review on automated systems for ruminant monitoring Chelotti, José Martinez-Rau, Luciano Ferrero, Mariano Vignolo, Leandro Galli, Julio Planisich, Alejandra Rufiner, H. Leonardo Giovanini, Leonardo Signal Processing Machine Learning Sound Audio and Speech Processing Image and Video Processing Livestock feeding behaviour is an influential research area for those involved in animal husbandry and agriculture. In recent years, there has been a growing interest in automated systems for monitoring the behaviour of ruminants. Despite the developments accomplished in the last decade, there is still much to do and learn about the methods for measuring and analysing livestock feeding behaviour. Automated monitoring systems mainly use motion, acoustic, and image sensors to collect animal behavioural data. The performance evaluation of existing methods is a complex task and direct comparisons between studies are difficult. Several factors prevent a direct comparison, starting from the diversity of data and performance metrics used in the experiments. To the best of our knowledge, this work represents the first tutorial-style review on the analysis of the feeding behaviour of ruminants, emphasising the relationship between sensing methodologies, signal processing, and computational intelligence methods. It assesses the main sensing methodologies (i.e. based on movement, sound, images/videos, and pressure) and the main techniques to measure and analyse the signals associated with feeding behaviour, evaluating their use in different settings and situations. It also highlights the potentiality of automated monitoring systems to provide valuable information that improves our understanding of livestock feeding behaviour. The relevance of these systems is increasingly important due to their impact on production systems and research. Finally, the paper closes by discussing future challenges and opportunities in livestock feeding behaviour monitoring. |
| title | Livestock feeding behaviour: A review on automated systems for ruminant monitoring |
| topic | Signal Processing Machine Learning Sound Audio and Speech Processing Image and Video Processing |
| url | https://arxiv.org/abs/2312.09259 |