# Livestock feeding behaviour: A review on automated systems for ruminant monitoring

arXiv: https://arxiv.org/abs/2312.09259 · HTML: https://mlsanity.com/paper/2312.09259/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 235 references that are in the map.
- First posted on arXiv 3 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [A review of domain adaptation without target labels](https://mlsanity.com/paper/1901.05335/) (2019)
- [Communication-Efficient Edge AI: Algorithms and Systems](https://mlsanity.com/paper/2002.09668/) (2020)
- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [A Systematic Review of Machine Learning Techniques for Cattle Identification: Datasets, Methods and Future Directions](https://mlsanity.com/paper/2210.09215/) (2022)
- [Video-based cattle identification and action recognition](https://mlsanity.com/paper/2110.07103/) (2021)
- [A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem](https://mlsanity.com/paper/2012.11870/) (2020)
- [Dairy Cow rumination detection: A deep learning approach](https://mlsanity.com/paper/2101.10445/) (2021)

## Built on by (most cited first, 1 of 1)

- [A multi-head deep fusion model for recognition of cattle foraging events using sound and movement signals](https://mlsanity.com/paper/2505.10198/) (2025)

## Abstract (arXiv)

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.