Thermography for Disease Detection in Livestock

Background: Infra-red thermography (IRT) offers potential opportunities as a tool for disease detection in livestock. Despite considerable research in this area, there are no common standards or protocols for managing IRT parameters in animal disease detection research. Objective. We investigate parameters that are essential to the development of IRT as a tool for disease detection and make recommendations for their use based on the literature found and the veterinary thermography guidelines from the American Academy of Thermology. Materials and Methods: We analyzed a defined set of 109 articles concerned with the use of IRT in livestock related to disease and from these articles, parameters for accurate IRT were identified and sorted into the fields of camera-, animal- or environment-related categories to assess the practices of each article in reporting parameters. Results: This review highlights the inconsistencies in practice across peer-reviewed articles and demonstrates that some important parameters are completely unreported while others are incorrectly captured and/or under-represented in the literature. Further to this, our review highlights the lack of measured emissivity values for live animals in multiple species, which are essential for accurate temperature measurements. Discussion and Conclusion: We present guidelines for the standards of parameters that should be used and reported in future experiments and discuss potential opportunities and challenges associated with using IRT for disease detection: (i) disease types; (ii) absolute versus relative temperature estimations; and (iii) individual vs. herd measurements. We also contemplate the necessary components of an on-farm IRT system and processing/storage requirements for thermal imagery. Perspectives: An IRT system to continuously monitor housed livestock as a group or individuals may be the natural next step, with due consideration given to farm layout, animal identification and specific diseases that are important to detect.