AI-based Prediction and Detection of Early-onset of Digital Dermatitis in Dairy Cows using Infrared Thermography

This study applied deep learning-based computer vision techniques using infrared thermography (IRT) data for the early detection and prediction of Digital Dermatitis (DD) in dairy cows, a common foot disease that negatively impacts animal welfare, milk production, and fertility. The researchers investigated the role of various inputs, including thermal images of cow feet, statistical color features extracted from IRT images, and manually registered temperature values. Their models achieved an accuracy of above 81% for DD detection on ‘day 0’ (the first appearance of clinical signs) and above 70% accuracy for predicting DD two days prior to clinical signs. The findings indicate that combining IRT images with AI-based predictors shows significant potential for developing future real-time automated tools for monitoring DD in dairy cows, which could lead to improved DD management, more rapid treatments, enhanced animal wellbeing, and reduced negative effects on lactation and reproductive performance. While the study demonstrated promising results, it noted limitations such as a suboptimal number of cows, suggesting that an increased number of monitored animals would likely lead to higher performance for both detection and prediction models.