Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning-based Automatic Monitoring of Pigs Physico-temporal Activities at Different Greenhouse Gas Concentrations

Version 1 : Received: 20 October 2021 / Approved: 21 October 2021 / Online: 21 October 2021 (23:06:30 CEST)

A peer-reviewed article of this Preprint also exists.

Bhujel, A.; Arulmozhi, E.; Moon, B.-E.; Kim, H.-T. Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations. Animals 2021, 11, 3089. Bhujel, A.; Arulmozhi, E.; Moon, B.-E.; Kim, H.-T. Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations. Animals 2021, 11, 3089.

Journal reference: Animals 2021, 11, 3089
DOI: 10.3390/ani11113089

Abstract

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 short-term pigs' physical activities in a 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 Fast-er 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, coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results showed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs also 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 monitoring and tracking pigs' physical activities non-invasively.

Keywords

YOLOv4; Faster RCNN; Deep-SORT; pig posture detection; object tracking; greenhouse gas; animal welfare

Subject

LIFE SCIENCES, Other

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