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논문 기본 정보

자료유형
학술저널
저자정보
Md Nasim Reza (Chungnam National University) Md Razob Ali (Department of Agricultural Machinery Engineering, Chungnam National University) Samsuzzaman (Department of Agricultural Machinery Engineering, Chungnam National University) Md Shaha Nur Kabir (Hajee Mohammad Danesh Science and Technology University) Md Rejaul Karim (Chungnam National University) Shahriar Ahmed (Chungnam National University (충남대학교)) Hyunjin Kyoung (Chungnam National University) 김국환 (농촌진흥청 국립농업과학원) Sun-Ok Chung (Chungnam National University)
저널정보
한국축산학회(구 한국동물자원과학회) 한국축산학회지 Journal of Animal Science and Technology Vol.66 No.1
발행연도
2024.1
수록면
31 - 56 (26page)

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Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

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