Objective scoring of footpad dermatitis in broiler chickens using image segmentation and a deep learning approach: camera-based scoring system
14
Issued Date
2022-01-01
Resource Type
ISSN
00071668
eISSN
14661799
Scopus ID
2-s2.0-85122878671
Pubmed ID
34870524
Journal Title
British Poultry Science
Volume
63
Issue
4
Start Page
427
End Page
433
Rights Holder(s)
SCOPUS
Bibliographic Citation
British Poultry Science Vol.63 No.4 (2022) , 427-433
Suggested Citation
Kaewtapee C., Thepparak S., Rakangthong C., Bunchasak C., Supratak A. Objective scoring of footpad dermatitis in broiler chickens using image segmentation and a deep learning approach: camera-based scoring system. British Poultry Science Vol.63 No.4 (2022) , 427-433. 433. doi:10.1080/00071668.2021.2013439 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/83393
Title
Objective scoring of footpad dermatitis in broiler chickens using image segmentation and a deep learning approach: camera-based scoring system
Author's Affiliation
Other Contributor(s)
Abstract
1. Footpad dermatitis (FPD) can be used as an important indicator of animal welfare and for economic evaluation; however, human scoring is subjective, biased and labour intensive. This paper proposes a novel deep learning approach that can automatically determine the severity of FPD based on images of chicken’s feet. 2. This approach first determined the areas of the FPD lesion, normal parts of each foot and the background, using a deep segmentation model. The proportion of the FPD for the chicken’s two feet was calculated by dividing the number of FPD pixels by the number of feet pixels. The proportion was then categorised using a five-point score for FPD. The approach was evaluated from 244 images of the left and right footpads using five-fold cross-validation. These images were collected at a commercial slaughter plant and scored by trained observers. 3. The result showed that this approach achieved an overall accuracy and a macro F1-score of 0.82. The per-class F1-scores from all FPD scores (scores 0 to 4) were similar (0.85, 0.80, 0,80, 0,80, and 0.87, respectively), which demonstrated that this approach performed equally well for all classes of scores. 4. The results suggested that image segmentation and a deep learning approach can be used to automate the process of scoring FPD based on chicken foot images, which can help to minimise the subjective bias inherent in manual scoring.