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.
ABSTRACT
Heusler compound nanoparticles with good structural ordering need to be investigated as a potential material class for magneto-thermal applications requiring heat generation in presence of an oscillating magnetic field. Here, we report an important finding of a structural parameter related to the product of the strain and the coherent crystallite size, that can be used to efficiently control the structural ordering and the magnetic property of the Heulser compound nanoparticles. The optimization of this product parameter is found to enhance both the structural ordering and magnetic transition temperature in Co2FeSn Heusler nanoparticles. Furthermore, using magnetic hyperthermia measurements we demonstrate the possibility of heat generation using Heusler compound nanoparticles comparable to that of conventional magnetic nanoparticles. This shall lead to the development of Heulser compounds for similar applications.
