Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Tail biting presents a significant challenge in conventional pig farming, impacting animal welfare and farmers' economic viability. This paper introduces a novel approach for image-based tail posture monitoring, a potential early indicator of tail biting outbreaks. Our two-step tail posture detection approach, consisting of an initial pig detection and a subsequent tail posture detection step, shows significant improvements compared to previous methods. To mitigate ambiguity, our pipeline incorporates an EfficientNetV2 image classification model, filtering out lying pigs in the tail posture monitoring process. When applied to video sequences containing tail biting incidents, our method effectively captures the shift in tail posture from predominantly upright to hanging preceding outbreaks. Our findings offer a promising foundation for an early warning system to aid undocked pig husbandry, improve animal welfare, and provide targeted insights for farmers. The proposed approach demonstrates the potential for real-world applications, fostering proactive interventions to mitigate tail biting.
Recommended Citation
Witte, Jan-Hendrik; Heseker, Philipp; Probst, Jeanette; Traulsen, Imke; Kemper, Nicole; and Gómez, Jorge Marx, "Image-based Tail Posture Monitoring of Pigs" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 2.
https://aisel.aisnet.org/hicss-57/da/smart_farming/2
Image-based Tail Posture Monitoring of Pigs
Hilton Hawaiian Village, Honolulu, Hawaii
Tail biting presents a significant challenge in conventional pig farming, impacting animal welfare and farmers' economic viability. This paper introduces a novel approach for image-based tail posture monitoring, a potential early indicator of tail biting outbreaks. Our two-step tail posture detection approach, consisting of an initial pig detection and a subsequent tail posture detection step, shows significant improvements compared to previous methods. To mitigate ambiguity, our pipeline incorporates an EfficientNetV2 image classification model, filtering out lying pigs in the tail posture monitoring process. When applied to video sequences containing tail biting incidents, our method effectively captures the shift in tail posture from predominantly upright to hanging preceding outbreaks. Our findings offer a promising foundation for an early warning system to aid undocked pig husbandry, improve animal welfare, and provide targeted insights for farmers. The proposed approach demonstrates the potential for real-world applications, fostering proactive interventions to mitigate tail biting.
https://aisel.aisnet.org/hicss-57/da/smart_farming/2