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

Abstract

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. Concentrate, hay, and water were freely available. Calf health parameters were scored daily. The AMF measured milk replacer (MR) intake, number of rewarded visits, number of unrewarded visits, and drinking speed. A calf was considered sick if its fecal score was 3 or 4 and its respiratory score was 2 or 3. Only data from AMF up to 47 d of age were included in the analysis. This cut in the data was made to avoid data from the weaning period. Data were split in 80:20 ratios for training and testing data sets according to the Pareto principle. A minimum sensitivity of 80% was considered an appropriate requirement for the prediction models. Considering all calves in group housing, cross-validation of the test data set showed a sensitivity of 83% and a specificity of 79%, with a positive predictive value and a negative predictive value of 37 and 97%, respectively. The area under the curve of the receiver operating characteristic for the deep CNN model was 0.81 for all group-housed calves. The CNN model yielded sensitivity and specificity of 83 and 71%, respectively (for ad libitum-fed calves), and 82 and 87%, respectively (for restricted-fed calves), with good area under the curve-receiver operating characteristic (0.77 to 0.87), indicating that the CNN models can predict calf disease in both groups with different MR allowances. The permutation feature importance was measured by the decrease in model accuracy, and features (behaviors) were summarized in descending order of their relative importance to the CNN model. Drinking speed and MR intake were the main factors to predict calf disease in calves fed ad libitum. The number of unrewarded visits to the milk feeder and MR intake were the main factors to predict calf disease in restricted-fed calves. Despite the relatively small sample size, the results provide strong evidence that daily feeding behavior data from AMF can be used to identify calves at risk for disease. In conclusion, despite a very good testing performance of the CNN model, the relatively low daily prevalence of calf disease in the present study resulted in a high proportion of false-positive alarms.

Keywords: calf; deep learning; disease; feeding behavior.