ABSTRACT: Interindividual distances and orientations of laying hens provide quantitative measures to calculate and optimize space allocations for bird flocks. However, these metrics were often measured manually and have not been examined for different stocking densities of laying hens. The objectives of this study were to 1) integrate and develop several deep learning techniques to detect interindividual distances and orientations of laying hens; and 2) examine the 2 metrics under 8 stocking densities via the developed techniques. Laying hens (Jingfen breed, a popular hen breed in China) at 35 wk of age were raised in experimental compartments at 8 different stocking densities of 3,840, 2,880, 2,304, 1,920, 1,646, 1,440, 1,280, and 1,152 cm2•bird−1 (3–10 hens per compartment, respectively), and cameras on the top of the compartments recorded videos for further analysis. The designed deep learning image classifier achieved over 99% accuracy to classify bird's perching status and excluded frames with bird perching to ensure that all birds analyzed were on the same horizontal plane, reducing calculation errors. The YOLOv5m oriented object detection model achieved over 90% precision, recall, and F1 score in detecting birds in compartments and can output bird centroid coordinates and angles, from which interindividual distances and orientations were calculated based on pairs of birds. Laying hens maintained smaller minimum interindividual distances in higher stocking densities. They were in an intersecting relationship with conspecifics for over 90% of the time. The developed integrative deep learning techniques and behavior metrics provide animal-based measurement of space requirement for laying hens.
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