| Title |
Advancements in AI for poultry farming to ensure early detection to tackle fallen bird incidents / |
| Authors |
Nakrošis, Arnas ; Paulauskaitė-Tarasevičienė, Agnė ; Gružauskas, Romas |
| DOI |
10.15388/Proceedings.2024.44 |
| Full Text |
|
| Is Part of |
IVUS2024: 29th international conference "Information society and university studies", Vilnius University, Kaunas Faculty, Kaunas, Lithuania, May 17th, 2024: abstracts.. Vilnius : Vilniaus universiteto leidykla. 2024, p. 11 |
| 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 |
Vilnius : Vilniaus universiteto leidykla |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2024 |
| CC license |
|
