Image analysis using machine learning (ML) algorithms could provide a measure of animal welfare by measuring comfort behaviours and undesired behaviours. Using a PLF technique based on images, the present study aimed to test a machine learning tool for measuring the number of hens on the ground and identifying the number of dust‐bathing hens in an experimental aviary. In addition, two YOLO (You Only Look Once) models were compared. YOLOv4‐tiny needed about 4.26 h to train for 6000 epochs, compared to about 23.2 h for the full models of YOLOv4. In validation, the performance of the two models in terms of precision, recall, harmonic mean of precision and recall, and mean average precision (mAP) did not differ, while the value of frame per second was lower in YOLOv4 compared to the tiny version (31.35 vs. 208.5). The mAP stands at about 94% for the classification of hens on the floor, while the classification of dust‐bathing hens was poor (28.2% in the YOLOv4‐tiny compared to 31.6% in YOLOv4). In conclusion, ML successfully identified laying hens on the floor, whereas other PLF tools must be tested for the classification of dustbathing hens.

Measuring Comfort Behaviours in Laying Hens Using Deep-Learning Tools

Sozzi M.
Writing – Original Draft Preparation
;
Pillan G.
Investigation
;
Ciarelli C.
Investigation
;
Marinello F.
Conceptualization
;
Pirrone F.
Investigation
;
Bordignon F.
Writing – Original Draft Preparation
;
Xiccato G.
Writing – Review & Editing
;
Trocino A.
Writing – Review & Editing
2023

Abstract

Image analysis using machine learning (ML) algorithms could provide a measure of animal welfare by measuring comfort behaviours and undesired behaviours. Using a PLF technique based on images, the present study aimed to test a machine learning tool for measuring the number of hens on the ground and identifying the number of dust‐bathing hens in an experimental aviary. In addition, two YOLO (You Only Look Once) models were compared. YOLOv4‐tiny needed about 4.26 h to train for 6000 epochs, compared to about 23.2 h for the full models of YOLOv4. In validation, the performance of the two models in terms of precision, recall, harmonic mean of precision and recall, and mean average precision (mAP) did not differ, while the value of frame per second was lower in YOLOv4 compared to the tiny version (31.35 vs. 208.5). The mAP stands at about 94% for the classification of hens on the floor, while the classification of dust‐bathing hens was poor (28.2% in the YOLOv4‐tiny compared to 31.6% in YOLOv4). In conclusion, ML successfully identified laying hens on the floor, whereas other PLF tools must be tested for the classification of dustbathing hens.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3464407
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