Digital images are becoming more readily available and possibilities for image processing are developing rapidly. This opens the possibility to use digital images to monitor and detect diseases in animals. In this paper we present 1) a generic facial feature scoring system based on seven facial features, 2) manual scores of images of Holstein Frisian heifers during foot-and-mouth disease vaccine efficacy trials and 3) automatic disease scores of the same animals. The automatic scoring system was based on the manual version and trained on annotated images from the manual scoring system. For both systems we found an increase in disease scores three days post infection, followed by a recovery. This temporal pattern matched with observations made by animal caretakers. Importantly, the automatic system was able to discern animals that were protected by the vaccine, and did not develop blisters at the feet, and animals that were not protected. Finally, automatic scores could be used to detect healthy and sick animals with a sensitivity and specificity of 0.94 on the second and third days following infection in an experimental setting. This generic facial feature disease scoring system could be further developed and extended to lactating Holstein Frisian dairy cows, other breeds and other infectious diseases. The system could be applied during animal experiments or, after further development, in a farm setting.
Keywords: Cattle, face, foot-and-mouth disease; Disease detection; Disease monitoring; Image recognition; Images.
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