A review on computer vision systems in monitoring of poultry: A welfare perspective

• Main Authors: Cedric Okinda, Innocent Nyalala, Tchalla Korohou, Celestine Okinda, Jintao Wang, Tracy Achieng, Patrick Wamalwa, Tai Mang, Mingxia Shen
• Format: Article
• Language: English
• Published: KeAi Communications Co., Ltd. 2020-01-01
• Series: Artificial Intelligence in Agriculture
• Subjects: Computer vision; Deep learning; Machine learning; Monitoring; Poultry; Welfare
• Online Access: http://www.sciencedirect.com/science/article/pii/S2589721720300258

Monitoring of poultry welfare-related bio-processes and bio-responses is vital in welfare assessment and management of welfare-related factors. With the current development in information technologies, computer vision has become a promising tool in the real-time automation of poultry monitoring systems due to its non-intrusive and non-invasive properties, and its ability to present a wide range of information. Hence, it can be applied to monitor several bio-processes and bio-responses. This review summarizes the current advances in poultry monitoring techniques based on computer vision systems, i.e., conventional machine learning-based and deep learning-based systems. A detailed presentation on the machine learning-based system was presented, i.e., pre-processing, segmentation, feature extraction, feature selection, and dimension reduction, and modeling. Similarly, deep learning approaches in poultry monitoring were also presented. Lastly, the challenges and possible solutions presented by researches in poultry monitoring, such as variable illumination conditions, occlusion problems, and lack of augmented and labeled poultry datasets, were discussed.