Your browser doesn't support javascript.
loading
Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review.
Reza, Md Nasim; Ali, Md Razob; Kabir, Md Shaha Nur; Karim, Md Rejaul; Ahmed, Shahriar; Kyoung, Hyunjin; Kim, Gookhwan; Chung, Sun-Ok.
Afiliación
  • Reza MN; Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Korea.
  • Ali MR; Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Korea.
  • Samsuzzaman; Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Korea.
  • Kabir MSN; Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Korea.
  • Karim MR; Department of Agricultural Industrial Engineering, Faculty of Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh.
  • Ahmed S; Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Korea.
  • Kyoung H; Farm Machinery and Post-harvest Processing Engineering Division, Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh.
  • Kim G; Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Korea.
  • Chung SO; Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea.
J Anim Sci Technol ; 66(1): 31-56, 2024 Jan.
Article en En | MEDLINE | ID: mdl-38618025
ABSTRACT
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.
Palabras clave

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: J Anim Sci Technol Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: J Anim Sci Technol Año: 2024 Tipo del documento: Article