Interindividual distances and orientations of laying hens under 8 stocking densities measured by integrative deep learning techniques

Li, Guoming; Li, Baoming; Shi, Zhengxiang; Lu, Guoyu; Chai, Lilong; Rasheed, Khaled M.; Regmi, Prafulla; Banakar, Ahmad

doi:10.1016/j.psj.2023.103076

Cited by 4 publications

(3 citation statements)

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“…Hierarchy and dominance play fundamental roles in the allocation and access to resources, such as food, water, environmental enrichments, resting spaces, and even the choice of reproductive partners, directly impacting the reproduction and survival of the species [ 24 ]. Furthermore, they are also responsible for the influence that some birds have over others, directly impacting the interaction and social behavior of the birds [ 25 ].…”

Section: Behavioral Dynamics In Herding and Group Composition Analysismentioning

confidence: 99%

Laying Hens: Why Smothering and Not Surviving?—A Literature Review

Mazocco,

de Castro Júnior,

Silveira

et al. 2024

Animals

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The proliferation of rearing systems providing opportunities for birds to engage in natural behaviors can trigger behavioral repertoires that when not manageable compromise animal welfare and the economic viability of the flock. Smothering in laying hens has long been perceived as “natural” or the result of hysteria among birds in the flock. However, the current literature has recognized smothering as an abnormal outcome with the potential to result in significant losses in cage-free poultry systems. Recent studies have specifically aimed to categorize the organization of smothering behavior and highlight its potential causes and consequences. In this study, literature review and bibliographic mapping, drawing on published articles and engagement with poultry farmers through extension and rural technical assistance, were employed. The findings indicate that smothering is a behavior triggered by factors related to the environment in which the laying hens are kept. This study concludes that there is a critical need for more rigorous and detailed research to elucidate the nuances of avian behavioral physiology and assess the impact of production systems on animal welfare and the economic impacts on the flock. This research contributes to a deeper understanding of bird behavior in high-production environments and provides practical insights for the poultry industry.

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Section: Behavioral Dynamics In Herding and Group Composition Analysismentioning

confidence: 99%

Laying Hens: Why Smothering and Not Surviving?—A Literature Review

Mazocco,

de Castro Júnior,

Silveira

et al. 2024

Animals

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“…Human monitoring of animal behavior can be exhaustive and imprecise [5][6][7]. In this field, computer vision has gained space for using non-invasive sensors and ensuring good accuracy in analyzes [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Computer vision systems based on deep learning techniques make it possible to automatically discover data representations more easily, enabling the extraction of location and distance measurements [7]. The automatic detection of chickens housed on shavings bedding using computer vision cannot be considered a trivial task, as in the monitored area, there are variations in brightness, shadows, concealment by equipment, and the distortion of the camera lens itself [17].…”

Section: Introductionmentioning

confidence: 99%

Chicken Tracking and Individual Bird Activity Monitoring Using the BoT-SORT Algorithm

Siriani,

Miranda,

Mehdizadeh

et al. 2023

AgriEngineering

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The analysis of chicken movement on the farm has several applications in evaluating the well-being and health of birds. Low locomotion may be associated with locomotor problems, and undesirable bird movement patterns may be related to environmental discomfort or fear. Our objective was to test the BoT-SORT object tracking architecture embedded in Yolo v8 to monitor the movement of cage-free chickens and extract measures to classify running, exploring, and resting behaviors, the latter of which includes all other behaviors that do not involve displacement. We trained a new model with a dataset of 3623 images obtained with a camera installed on the ceiling (top images) from an experiment with layers raised cage-free in small-scale aviaries and housed in groups of 20 individuals. The model presented a mAP of 98.5%, being efficient in detecting and tracking the chickens in the video. From the tracking, it was possible to record the movements and directions of individual birds, and we later classified the movement. The results obtained for a group of 20 chickens demonstrated that approximately 84% of the time, the birds remained resting, 10% of the time exploring, and 6% of the time running. The BoT-SORT algorithm was efficient in maintaining the identification of the chickens, and our tracking algorithm was efficient in classifying the movement, allowing us to quantify the time of each movement class. Our algorithm and the measurements we extract to classify bird movements can be used to assess the welfare and health of chickens and contribute to establishing standards for comparisons between individuals and groups raised in different environmental conditions.

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