Deep learning based classification of sheep behaviour from accelerometer data with imbalance-Reference-Cited by-同舟云学术

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Deep learning based classification of sheep behaviour from accelerometer data with imbalance

Published:2022-04 Issue: Volume: Page:
ISSN:2214-3173
Container-title:Information Processing in Agriculture
language:en
Short-container-title:Information Processing in Agriculture

Author:

Turner Kirk E.,Thompson Andrew,Harris Ian,Ferguson Mark,Sohel Ferdous

Funder

Murdoch University

Meat and Livestock Australia

Publisher

Elsevier BV

Subject

Computer Science Applications,Agronomy and Crop Science,Animal Science and Zoology,Aquatic Science,Forestry

Reference35 articles.

1. A review of livestock monitoring and the need for integrated systems;Frost;Comput Electron Agric,1997

2. Using a three-axis accelerometer to identify and classify sheep behaviour at pasture;Alvarenga;Applied Animal Behaviour Science,2016

3. Behaviour classification of extensively grazed sheep using machine learning;Fogarty;Comput Electron Agric,2020

4. Observing the unwatchable through acceleration logging of animal behavior;Brown;Anim Biotelem,2013

5. Identifying sheep activity from tri-axial acceleration signals using a moving window classification model;Barwick;Remote Sensing,2020

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1. Application of deep learning for livestock behaviour recognition: A systematic literature review;Computers and Electronics in Agriculture;2024-09

2. Comparison of two tri-axial accelerometers for measuring locomotor activity in ewes and lambs;Smart Agricultural Technology;2024-08

3. Behavior classification and spatiotemporal analysis of grazing sheep using deep learning;Computers and Electronics in Agriculture;2024-05

4. Integrating computer vision algorithms and RFID system for identification and tracking of group-housed animals: an example with pigs;Journal of Animal Science;2024

5. Rumination Detection in Sheep: A Systematic Review of Sensor-Based Approaches;Animals;2023-12-05

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