| Title |
Advancements in AI for poultry farming to ensure early detection to tackle fallen bird incidents / |
| Authors |
Nakrošis, Arnas ; Paulauskaite-Taraseviciene, Agne ; Gružauskas, Romas |
| Full Text |
|
| Is Part of |
CEUR workshop proceedings: IVUS 2024: Information society and university studies 2024: proceedings of the 29th international conference on information society and university studies (IVUS 2023) Kaunas, Lithuania, May 17, 2024 / edited by: I. Veitaitė, A. Lopata, T. Krilavičius, M. Woźniak.. Aachen : CEUR-WS. 2024, vol. 3885, p. 39-48.. ISSN 1613-0073 |
| Keywords [eng] |
computer vision ; deep learning ; segmentation ; overlapping images ; poultry |
| Abstract [eng] |
This study explores the application of deep learning architectures for image classification and segmentation in poultry farms with overlapping objects. Early detection of fallen birds is crucial for preventing disease outbreaks and maintaining animal welfare. We investigate the efficacy of various architectures, including U-Net, mU-Net, SegNet, and O-Net, for segmenting live and dead birds within poultry farm real time images. Our experiments, conducted on a dataset of 1805 images with varying lighting, distances, and object numbers, reveal that U-Net achieves the highest Dice coefficient (0.95128) for segmentation accuracy. We further demonstrate the potential of these models for classifying individual birds as alive or dead, with U-Net reaching a classification accuracy of 88.938%. The findings suggest that AI-powered image segmentation holds promise for enhancing poultry farm management by enabling early detection of deceased birds and fostering improved animal health and welfare. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2024 |
| CC license |
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