![]() The standardized setup consists of a “FLIROnePro” attached to an Android tablet. ![]() The best fit of our method had an R 2 = 0.774. 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. Our approach only needs a single (top view) thermal image of a piglet to automatically estimate the BCT. Additionally, images need to be manually annotated for the regions of interest inside the manufacturer’s software. 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. Thermal imaging technologies offer the opportunity to determine BCT in a non-invasive, stress-free way, potentially reducing the manual effort. Suboptimal BCT might indicate or lead to increased stress or diseases. Body core temperature (BCT) is an important characteristic for the vitality of pigs.
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