Thermoregulation in Malayan sun bears (Helarctos malayanus) and its consequences for in situ conservation

Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review

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.

Thermal Imaging to Assess the Health Status in Wildlife Animals under Human Care: Limitations and Perspectives

Promoting animal welfare in wildlife species under human care requires the implementation of techniques for continuously monitoring their health. Infrared thermography is a non-invasive tool that uses the radiation emitted from the skin of animals to assess their thermal state. However, there are no established thermal windows in wildlife species because factors such as the thickness or color of the skin, type/length of coat, or presence of fur can influence the readings taken to obtain objective, sensitive values. Therefore, this review aims to discuss the usefulness and application of the ocular, nasal, thoracic, abdominal, and podal anatomical regions as thermal windows for evaluating zoo animals' thermal response and health status. A literature search of the Web of Science, Science Direct, and PubMed databases was performed to identify relevant studies that used IRT with wild species as a complementary diagnostic tool. Implementing IRT in zoos or conservation centers could also serve as a method for determining and monitoring optimal habitat designs to meet the needs of specific animals. In addition, we analyze the limitations of using IRT with various wildlife species under human care to understand better the differences among animals and the factors that must be considered when using infrared thermography.

Understanding Species–Habitat Associations: A Case Study with the World’s Bears

Habitat modeling is one of the most common practices in ecology today, aimed at understanding complex associations between species and an array of environmental, bioclimatic, and anthropogenic factors. This review of studies of seven species of terrestrial bears (Ursidae) occupying four continents examines how habitat models have been employed, and the functionality of their predictions for management and conservation. Bear occurrence data have been obtained at the population level, as presence points (e.g., sign surveys or camera trapping), or as locations of individual radio-collared animals. Radio-collars provide greater insights into how bears interact with their environment and variability within populations; they are more commonly used in North America and Europe than in South America and Asia. Salient problematic issues apparent from this review included: biases in presence data; predictor variables being poor surrogates of actual behavioral drivers; predictor variables applied at a biologically inappropriate scale; and over-use of data repositories that tend to detach investigators from the species. In several cases, multiple models in the same area yielded different predictions; new presence data occurred outside the range of predicted suitable habitat; and future range projections, based on where bears presently exist, underestimated their adaptability. Findings here are likely relevant to other taxa.