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.