Deep convolutional neural networks for the detection of diarrhea and respiratory disease in preweaning dairy calves using data from automated milk feeders.

The objective of the current study was to develop a predictive model for calf disease detection in the preweaning period using data from automated milk feeders (AMF). A deep convolutional neural network (CNN) architecture for the detection of respiratory disease and diarrhea in dairy calves was developed. German Holstein calves were fed milk replacer either ad libitum (up to 25 L/d; n = 32) or restrictively (6 L/d; n = 32) via AMF from 10 ± 3 d of life on.