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Providing perches in cage-free (CF) housing offers significant benefits for laying hens, such as improved leg muscle development, bone health, reduced abdominal fat, and decreased fear and aggression. A precise detection method is essential to ensure that hens engage in perching behavior from an early age, as manual observation is often labor-intensive and sometimes inaccurate. The objectives of this study were to (1) develop and test a deep learning model for detecting perching behavior; and (2) evaluate the optimal model's performance on detecting perching behavior of laying hens of different ages. In this study, recent deep learning models, that is, YOLOv8s-PB, YOLOv8x-PB, YOLOv7-PB, and YOLOv7x-PB, were developed, trained and compared in detecting perching behavior in 4 CF rooms (200 hens/room). Perch height was up to 1.8 m from the litter floor and situated 1.5 m below the cameras. A total of 3,000 images were used, with each image featuring at least 1 hen perching. The models' detection accuracies and their performance across different age groups of hens were compared using 1-way ANOVA at a 5% significance level. The results showed that the YOLOv8x-PB model outperform all other models used, achieving the precision of 94.80%, recall of 95.10%, and mean average precision (mAP@0.50) of 97.60%. While all models proved over 94% detection precision. With optimal model, PB detection precision was highest (97.40%) for peaking phase followed by prelay (95.20%), grower (94.80%), developer (94.70%) and layers (92.70%) phases while the lowest detection precision (88.80%) was for starter phase. Detection performance was somewhat reduced by the overlapping of birds during perching and occlusion. Overall, the YOLOv8x-PB model was the most optimal in detecting perching behavior, proposing a valuable tool for CF producers to monitor the perching activities of laying hens automatically., Competing Interests: DISCLOSURES The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

As global demand for poultry products, environmental sustainability, and health consciousness rises with time, the poultry industry faces both substantial challenges and new opportunities. Therefore, this review paper provides a comprehensive overview of sustainable poultry farming, focusing on integrating genetic improvements, alternative feed, precision technologies, waste management, and biotechnological innovations. Together, these strategies aim to minimize ecological footprints, uphold ethical standards, improve economic feasibility, and enhance industry resilience. In addition, this review paper explores various sustainable strategies, including eco-conscious organic farming practices and innovative feed sources like insect-based proteins, single-cell proteins, algal supplements, and food waste utilization. It also addresses barriers to adoption, such as technical challenges, financial constraints, knowledge gaps, and policy frameworks, which are crucial for advancing the poultry industry. This paper examined organic poultry farming in detail, noting several benefits like reduced pesticide use and improved animal welfare. Additionally, it discusses optimizing feed efficiency, an alternate energy source (solar photovoltaic/thermal), effective waste management, and the importance of poultry welfare. Transformative strategies, such as holistic farming systems and integrated approaches, are proposed to improve resource use and nutrient cycling and promote climate-smart agricultural practices. The review underscores the need for a structured roadmap, education, and extension services through digital platforms and participatory learning to promote sustainable poultry farming for future generations. It emphasizes the need for collaboration and knowledge exchange among stakeholders and the crucial role of researchers, policymakers, and industry professionals in shaping a future where sustainable poultry practices lead the industry, committed to ethical and resilient poultry production., Competing Interests: DISCLOSURES The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Dustbathing (DB) is a functionally important maintenance behavior in birds that clean plumage, realigns feather structures, removes feather lipids, which helps to remove parasites and prevents feathers from becoming too oily. Among different natural behaviors birds perform in cage-free (CF) housing, DB is one of the important behavior related to bird welfare. Earlier studies have identified DB behavior using manual method such as counting number of DB bouts, and duration of DB bouts from video recordings. The manual detection of DB behavior is time-consuming, sometimes prone to errors, and have limitations. Therefore, an automated precision monitoring method is needed to detect DB behavior in laying hens from an early age in CF housing environment. The objectives of this study were to (1) develop and test a deep learning model for detecting DB behavior and find out the optimal model; and (2) assess the performance of the optimal model in detecting DB behavior at different growing phases. In this study, deep learning models, i.e., YOLOv7-DB, YOLOv7x-DB, YOLOv8s-DB and YOLOv8x-DB, networks, were developed, trained, and compared in tracking DB behavior in 4 CF rooms each with 200 hens (W-36 Hy-Line). Results indicate that the YOLOv8x-DB model outperform all other models on detecting DB behavior with a precision of 93.4%, recall of 91.20%, and mean average precision (mAP@0.50) of 93.70%. All models performed with over 90% detection precision; however, model performance was affected by equipment like drinking lines, perches, and feeders. Based on the optimal model (YOLOv8x-DB), DB detection precision was highest during grower phase (precision of 96.80%, recall of 97.10%, mAP@0.50 of 98.60%, and mAP@0.50-0.95 of 79.10% followed by prelay, layers, developer, and peaking phases. This study provides a reference for poultry and egg producers that DB behavior can be detected automatically with precision of at least 89% or more using optimal model at any growing phase of laying hens., Competing Interests: DISCLOSURES The authors declare that they have no known competing financial or personal interest that could have impact the outcome of the study reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Chickens' behaviors and activities are important information for managing animal health and welfare in commercial poultry houses. In this study, convolutional neural networks (CNN) models were developed to monitor the chicken activity index. A dataset consisting of 1,500 top-view images was utilized to construct tracking models, with 900 images allocated for training, 300 for validation, and 300 for testing. Six different CNN models were developed, based on YOLOv5, YOLOv8, ByteTrack, DeepSORT, and StrongSORT. The final results demonstrated that the combination of YOLOv8 and DeepSORT exhibited the highest performance, achieving a multiobject tracking accuracy (MOTA) of 94%. Further application of the optimal model could facilitate the detection of abnormal behaviors such as smothering and piling, and enabled the quantification of flock activity into 3 levels (low, medium, and high) to evaluate footpad health states in the flock. This research underscores the application of deep learning in monitoring poultry activity index for assessing animal health and welfare., Competing Interests: DISCLOSURES The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)