An Approach towards a Practicable Assessment of Neonatal Piglet Body Core Temperature Using Automatic Object Detection Based on Thermal Images

Abstract

Body core temperature (BCT) is an important characteristic for the vitality of pigs. Suboptimal BCT might indicate or lead to increased stress or diseases. Thermal imaging technologies offer the opportunity to determine BCT in a non-invasive, stress-free way, potentially reducing the manual effort. The current approaches often use multiple close-up images of different parts of the body to estimate the rectal temperature, which is laborious under practical farming conditions. Additionally, images need to be manually annotated for the regions of interest inside the manufacturer’s software. Our approach only needs a single (top view) thermal image of a piglet to automatically estimate the BCT. We first trained a convolutional neural network for the detection of the relevant areas, followed by a background segmentation using the Otsu algorithm to generate precise mean, median, and max temperatures of each detected area. The best fit of our method had an R2 = 0.774. The standardized setup consists of a “FLIROnePro” attached to an Android tablet. To sum up, this approach could be an appropriate tool for animal monitoring under commercial and research farming conditions.