Precision livestock farming, a review of the advances in poultry farming focused on broiler breeding
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ISSN: 2076-8133
E-ISSN: 2312-637X
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Actualidad Tecnológica
Actualidad Tecnológica
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Aug 24, 2022
Published: Feb 3, 2023
Published: Feb 3, 2023
Abstract
Poultry production is one of the industries with the best development within the Panamanian agricultural system, however, for the next few years a growth in food demand caused by population growth is expected. This has led to solutions such as encouraging the emergence of a greater number of producers and boosting intensive animal production. Faced with this problem, a new field of research has emerged called Precision Livestock Farming (PLF), which is defined as the ability to monitor and track in real time the welfare, production, reproduction, environmental impact and health of livestock, using new technologies in artificial intelligence, automation, internet of things and information systems. This article aims to be a review on the fundamentals of precision livestock farming in broiler breeding, gathering current works and their work trends, from bibliographic research bases, with a view to the adoption of this field in future projects within Panama. As a result of this review, it was found that European countries such as Belgium, Netherlands, United Kingdom and Italy have the largest number of researchers and works related to this branch, being projects based on sensors, machine learning, artificial vision and sound analysis the current research trends, it was also found that ethical dilemmas related to animal care and welfare are still being discussed within this field.
Keywords
automation, animal welfare, ethics, precision farming, management, machine learning, broilers, broiler chickens, sensors, artificial visionDownloads
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Juárez, R. (2023). Precision livestock farming, a review of the advances in poultry farming focused on broiler breeding. Prisma Tecnológico, 14(1), 38-48. https://doi.org/10.33412/pri.v14.1.3652
References
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(20) J. M. Bos, B. Bovenkerk, P. H. Feindt, and Y. K. van Dam, “The Quantified Animal: Precision Livestock Farming and the Ethical Implications of Objectification,” Food Ethics 2018 21, vol. 2, no. 1, pp. 77–92, Nov. 2018, doi: 10.1007/S41055-018-00029-X.
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(29) H. Pu, J. Lian, and M. Fan, “Automatic Recognition of Flock Behavior of Chickens with Convolutional Neural Network and Kinect Sensor,” https://doi.org/10.1142/S0218001418500234, vol. 32, no. 7, Mar. 2018, doi: 10.1142/S0218001418500234.
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(32) M. Kashiha, A. Pluk, C. Bahr, E. Vranken, and D. Berckmans, “Development of an early warning system for a broiler house using computer vision,” Biosyst. Eng., vol. 116, no. 1, pp. 36–45, Sep. 2013, doi: 10.1016/J.BIOSYSTEMSENG.2013.06.004.
(33) C. Fang, T. Zhang, H. Zheng, J. Huang, and K. Cuan, “Pose estimation and behavior classification of broiler chickens based on deep neural networks,” Comput. Electron. Agric., vol. 180, p. 105863, Jan. 2021, doi: 10.1016/J.COMPAG.2020.105863.
(34) J. Khairunissa, S. Wahjuni, I. R. H. Soesanto, and W. Wulandari, “Detecting Poultry Movement for Poultry Behavioral Analysis using the Multi-Object Tracking (MOT) Algorithm,” Proc. 8th Int. Conf. Comput. Commun. Eng. ICCCE 2021, pp. 265–268, Jun. 2021, doi: 10.1109/ICCCE50029.2021.9467144.
(35) X. Zhuang, M. Bi, J. Guo, S. Wu, and T. Zhang, “Development of an early warning algorithm to detect sick broilers,” Comput. Electron. Agric., vol. 144, pp. 102–113, Jan. 2018, doi: 10.1016/J.COMPAG.2017.11.032.
(36) C. Fang, J. Huang, K. Cuan, X. Zhuang, and T. Zhang, “Comparative study on poultry target tracking algorithms based on a deep regression network,” Biosyst. Eng., vol. 190, pp. 176–183, Feb. 2020, doi: 10.1016/J.BIOSYSTEMSENG.2019.12.002.
(37) I. Fontana et al., “Sound analysis to model weight of broiler chickens,” Poult. Sci., vol. 96, no. 11, pp. 3938–3943, Nov. 2017, doi: 10.3382/PS/PEX215.
(38) L. Carpentier, E. Vranken, D. Berckmans, J. Paeshuyse, and T. Norton, “Development of sound-based poultry health monitoring tool for automated sneeze detection,” Comput. Electron. Agric., vol. 162, pp. 573–581, Jul. 2019, doi: 10.1016/J.COMPAG.2019.05.013.
(39) N. Jakovljevic, N. Maljkovic, D. Miskovic, P. Knezevic, and V. Delic, “A Broiler Stress Detection System Based on Audio Signal Processing,” 27th Telecommun. Forum, TELFOR 2019, Nov. 2019, doi: 10.1109/TELFOR48224.2019.8971336.
(40) “Vetmeduni : ECPLF.” https://www.vetmeduni.ac.at/ecplf2022/ecplf (accessed Feb. 15, 2022).
(41) “EA-PLF | European Association for Precisione Livestock Farming.” https://www.eaplf.eu/ (accessed Feb. 15, 2022).