Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations

Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs’ health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs’ health an...

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Autores principales:	Anil Bhujel, Elanchezhian Arulmozhi, Byeong-Eun Moon, Hyeon-Tae Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	YOLOv4 Faster R-CNN Deep-SORT pig posture detection object tracking greenhouse gas Veterinary medicine SF600-1100 Zoology QL1-991
Acceso en línea:	https://doaj.org/article/18c0a8c7c95b4c039eb5effc713ae410
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id	oai:doaj.org-article:18c0a8c7c95b4c039eb5effc713ae410
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spelling	oai:doaj.org-article:18c0a8c7c95b4c039eb5effc713ae4102021-11-25T16:14:57ZDeep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations10.3390/ani111130892076-2615https://doaj.org/article/18c0a8c7c95b4c039eb5effc713ae4102021-10-01T00:00:00Zhttps://www.mdpi.com/2076-2615/11/11/3089https://doaj.org/toc/2076-2615Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs’ health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs’ health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect pigs’ short-term physical activities in the compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00–08:00, 13:00–14:00, and 20:00–21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Faster R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, was coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results revealed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs shortened their sternal-lying posture, increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models’ efficacy in the monitoring and tracking of pigs’ physical activities non-invasively.Anil BhujelElanchezhian ArulmozhiByeong-Eun MoonHyeon-Tae KimMDPI AGarticleYOLOv4Faster R-CNNDeep-SORTpig posture detectionobject trackinggreenhouse gasVeterinary medicineSF600-1100ZoologyQL1-991ENAnimals, Vol 11, Iss 3089, p 3089 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	YOLOv4 Faster R-CNN Deep-SORT pig posture detection object tracking greenhouse gas Veterinary medicine SF600-1100 Zoology QL1-991
spellingShingle	YOLOv4 Faster R-CNN Deep-SORT pig posture detection object tracking greenhouse gas Veterinary medicine SF600-1100 Zoology QL1-991 Anil Bhujel Elanchezhian Arulmozhi Byeong-Eun Moon Hyeon-Tae Kim Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations
description	Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs’ health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs’ health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect pigs’ short-term physical activities in the compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00–08:00, 13:00–14:00, and 20:00–21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Faster R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, was coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results revealed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs shortened their sternal-lying posture, increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models’ efficacy in the monitoring and tracking of pigs’ physical activities non-invasively.
format	article
author	Anil Bhujel Elanchezhian Arulmozhi Byeong-Eun Moon Hyeon-Tae Kim
author_facet	Anil Bhujel Elanchezhian Arulmozhi Byeong-Eun Moon Hyeon-Tae Kim
author_sort	Anil Bhujel
title	Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations
title_short	Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations
title_full	Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations
title_fullStr	Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations
title_full_unstemmed	Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations
title_sort	deep-learning-based automatic monitoring of pigs’ physico-temporal activities at different greenhouse gas concentrations
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/18c0a8c7c95b4c039eb5effc713ae410
work_keys_str_mv	AT anilbhujel deeplearningbasedautomaticmonitoringofpigsphysicotemporalactivitiesatdifferentgreenhousegasconcentrations AT elanchezhianarulmozhi deeplearningbasedautomaticmonitoringofpigsphysicotemporalactivitiesatdifferentgreenhousegasconcentrations AT byeongeunmoon deeplearningbasedautomaticmonitoringofpigsphysicotemporalactivitiesatdifferentgreenhousegasconcentrations AT hyeontaekim deeplearningbasedautomaticmonitoringofpigsphysicotemporalactivitiesatdifferentgreenhousegasconcentrations
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Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations

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