Abstract
Bio-monitoring of livestock has become a popular topic in scientific research over the past 15 years. With the introduction of the concept of smart farming a necessity has arisen for utilizing modern methods such as infrared thermography (IRT) for evaluating the State-of-Health of farm animals. Tissue imaging via IRT is a complex task, due to thermal pattern changes induced by variances in skin thickness, tissue structure and fat contents, fur coating color, hair length and thickness, skin emissivity, animal stress levels, animal movement, ambient temperature and humidity. The goal of our work is to present an automated novel IoT system, which can address these difficulties, allowing scalability and performing farm-level animal well-being assessment in a non-stressful manner. The developed system implements a ratiometric One-Eyed-Bandit Technique (OEBT), automated thermographic object recognition software and algorithms for applying dynamic corrections to the captured raw thermographic data.
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Rizanov, S., Yakimov, P., Todorov, D. (2023). IoT Infrared Imaging of Livestock Tissues Using a One-Eyed Bandit Technique. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 694. Springer, Singapore. https://doi.org/10.1007/978-981-99-3091-3_22
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DOI: https://doi.org/10.1007/978-981-99-3091-3_22
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