Intelligent Perception-Based Cattle Lameness Detection and Behaviour Recognition: A Review.

Animals (Basel)

Australian Centre for Field Robotics (ACFR), Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia.

Published: October 2021

AI Article Synopsis

  • The demand for animal-sourced protein is rising due to a growing global population, but traditional farming methods face challenges like climate change and socioeconomic issues.
  • Recent advancements in technology, such as smart sensors and deep learning, are being applied to improve livestock farming practices, particularly in cattle behaviour recognition and health monitoring.
  • The review analyzes over 100 studies on cattle lameness and behavior detection, anticipating future developments in intelligent monitoring through standardization and integration with IoT and deep learning, while also addressing key challenges and opportunities in this research area.

Article Abstract

The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8614286PMC
http://dx.doi.org/10.3390/ani11113033DOI Listing

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