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A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles

With the ever-increasing importance of dairy and meat production, precision livestock farming (PLF) using advanced information technologies is emerging to improve farming production systems. The latest automation, connectivity, and artificial intelligence developments open new horizons to monitor li...

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Main Authors: Mamehgol Yousefi, D. B., Mohd Rafie, A. S., Al-Haddad, S. A. R., Syaril Azrad
Format: Article
Published: IEEE 2022
Online Access:http://psasir.upm.edu.my/id/eprint/100453/
https://ieeexplore.ieee.org/abstract/document/9844698
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spelling my.upm.eprints.1004532023-12-14T04:10:16Z http://psasir.upm.edu.my/id/eprint/100453/ A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles Mamehgol Yousefi, D. B. Mohd Rafie, A. S. Al-Haddad, S. A. R. Syaril Azrad With the ever-increasing importance of dairy and meat production, precision livestock farming (PLF) using advanced information technologies is emerging to improve farming production systems. The latest automation, connectivity, and artificial intelligence developments open new horizons to monitor livestock in the pasture, controlled environments, and open environments. Due to the significance of livestock detection and tracking, this systematic review extracts and summarizes the existing deep learning (DL) techniques in PLF using unmanned aerial vehicles (UAV). In the context of livestock recognition studies, UAVs are receiving growing attention due to their flexible data acquisition and operation in different conditions. This review examines the implemented DL architectures and scrutinizes the broadly exploited evaluation metrics, attributes, and databases. The classification of most UAV livestock monitoring systems using DL techniques is in three categories: detection, classification, and localization. Correspondingly, this paper discusses the future benefits and drawbacks of these DL-based PLF approaches using UAV imagery. Additionally, this paper describes alternative methods used to mitigate issues in PLF. The aim of this work is to provide insights into the most relevant studies on the development of UAV-based PLF systems focused on deep neural network-based techniques. IEEE 2022 Article PeerReviewed Mamehgol Yousefi, D. B. and Mohd Rafie, A. S. and Al-Haddad, S. A. R. and Syaril Azrad (2022) A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles. IEEE Access, 10. 80071 - 80091. ISSN 2169-3536 https://ieeexplore.ieee.org/abstract/document/9844698 10.1109/ACCESS.2022.3194507
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description With the ever-increasing importance of dairy and meat production, precision livestock farming (PLF) using advanced information technologies is emerging to improve farming production systems. The latest automation, connectivity, and artificial intelligence developments open new horizons to monitor livestock in the pasture, controlled environments, and open environments. Due to the significance of livestock detection and tracking, this systematic review extracts and summarizes the existing deep learning (DL) techniques in PLF using unmanned aerial vehicles (UAV). In the context of livestock recognition studies, UAVs are receiving growing attention due to their flexible data acquisition and operation in different conditions. This review examines the implemented DL architectures and scrutinizes the broadly exploited evaluation metrics, attributes, and databases. The classification of most UAV livestock monitoring systems using DL techniques is in three categories: detection, classification, and localization. Correspondingly, this paper discusses the future benefits and drawbacks of these DL-based PLF approaches using UAV imagery. Additionally, this paper describes alternative methods used to mitigate issues in PLF. The aim of this work is to provide insights into the most relevant studies on the development of UAV-based PLF systems focused on deep neural network-based techniques.
format Article
author Mamehgol Yousefi, D. B.
Mohd Rafie, A. S.
Al-Haddad, S. A. R.
Syaril Azrad
spellingShingle Mamehgol Yousefi, D. B.
Mohd Rafie, A. S.
Al-Haddad, S. A. R.
Syaril Azrad
A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles
author_facet Mamehgol Yousefi, D. B.
Mohd Rafie, A. S.
Al-Haddad, S. A. R.
Syaril Azrad
author_sort Mamehgol Yousefi, D. B.
title A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles
title_short A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles
title_full A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles
title_fullStr A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles
title_full_unstemmed A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles
title_sort systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles
publisher IEEE
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/100453/
https://ieeexplore.ieee.org/abstract/document/9844698
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score 13.211869

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