Abstract
Behavior is a good indicator of animal welfare, especially in challenging environments. However, few studies have investigated how pig behavior changes during heat stress. The current study is a proof-of-concept using Convolutional Neural Network (CNN) models to monitor pig behavior in order to investigate the differences in behavioral response to heat stress of two contrasted breeds: Large White (LW), selected for high performance, and Creole (CR), adapted to tropical conditions. A total of 6 slaughter pigs (3 CR and 3 LW; 22 weeks of age) were monitored from 8:30 to 17:30 during 54 days. Two CNN architectures were used to detect the animal (Yolo v2) and to estimate animal's posture (GoogleNet). Pig postures estimated by the neural network showed that pigs spent more time lying on their side when temperature increased. When comparing the two breeds, as temperature increases, CR pigs spent more time lying on their side than LW pigs, suggesting that they use this posture to increase thermoregulation and dissipate heat more efficiently. This study demonstrates that neural network models are an efficient tool to monitor animal behavior in an automated way, which could be particularly relevant to characterize breed adaptation to challenging environments.
Highlights
With the global increase of ambient temperature worldwide, Heat Stress (HS) is becoming a major concern for production (Renaudeau et al, 2012) and welfare (Johnson, 2018) in the pig industry
We found that the 3 CR pigs spent more time lying on their side than the 3 Large White (LW) pigs
When comparing the behavior of the two breeds in response to HS, i.e. changes in time spent in each posture in response to the rise of ambient temperature, we found that CR pigs adopted the lateral lying posture preferentially from a temperature of 29◦C, compared to 35◦C for LW
Summary
With the global increase of ambient temperature worldwide, Heat Stress (HS) is becoming a major concern for production (Renaudeau et al, 2012) and welfare (Johnson, 2018) in the pig industry. HS impacts on the economic viability, costing for instance $2 billion annually in the USA swine industry (St-Pierre, 2003) Due to their low number of sweat glands, pig thermoregulation relies mostly on heat dissipation through sensible heat loss and respiratory evaporation (Renaudeau et al, 2007). The use of accelerometers and GPS have been initially preferred to cover indoor and outdoor housing conditions, but computer vision starts offering a reliable solution to track animals and automatically record fine behavioral features, such as postural changes (Zheng et al, 2018; Leonard et al, 2019; Nasirahmadi et al, 2019; Yang et al, 2020) or aggression among group mates (Viazzi et al, 2014; Chen et al, 2020).
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