Deep-learning classification of teat-end conditions in Holstein cattle

Abstract

As a means of preventing mastitis, deep learning for classifying teat-end conditions in dairy cows has not yet been optimized. By using 1426 digital images of dairy cow udders, the extent of teat-end hyperkeratosis was assessed using a four-point scale. Several deep-learning networks based on the transfer learning approach have been used to evaluate the conditions of the teat ends displayed in the digital images. The images of the teat ends were partitioned into training (70 %) and validation datasets (15 %); afterwards, the network was evaluated based on the remaining test dataset (15 %). The results demonstrated that eight different ImageNet models consistently achieved high accuracy (80.3–86.6 %). The areas under the receiver operating characteristic curves for the normal, smooth, rough, and very rough classification scores in the test data set ranged from 0.825 to 0.999. Thus, improved accuracy in image-based classification of teat tissue conditions in dairy cattle using deep learning requires more training images. This method could help farmers reduce the risks of intramammary infections, decrease the use of antimicrobials, and better manage costs associated with mastitis detection and treatment.