Research advances in the automatic detection technology for mastitis of dairy cows

Cow mastitis is one of the most serious diseases in the healthy development of dairy farming, due to the high incidence and wide range of characteristics. Mastitis in dairy cows can also reduce milk production and quality, leading to human health and herd turnover costs. Dairy cows with clinical mastitis vary greatly in the abnormal changes in their udders and milk. In the case of subclinical mastitis, there is no outstanding appearance in the udder and milk, where the economic loss is more severe than before. Physical and chemical property testing has been widely used to collect the milk for the diagnosis of mastitis in dairy cows at present. However, the testing environment and the long testing period cannot fully meet the demand for the rapid and real-time diagnosis of mastitis in dairy cows. Fortunately, advanced electronic information technology and equipment have been widely used in the field of agriculture in recent years. The automatic detection of dairy cow mastitis has achieved better research for the high requirements. In this review, the current research progress was introduced into the automatic detection of dairy cow mastitis, according to the different types of sensors in the data acquisition. Three aspects were also included: the visual sensor, the automatic milking system (AMS), and the rest sensors. Firstly, the automatic detection of dairy cow mastitis with the vision sensor was divided into the detection with the udder thermal infrared and the milk images. Automatic detection was achieved in mastitis in dairy cows without damage and stress. Especially, the mastitis detection with the thermal infrared images shared the animal welfare, but the detection accuracy needed to be improved during this time. Mastitis detection with the milk images was achieved with high accuracy during lactation somatic cell count (SCC) detection. But it was still lacking in the test trials on the accuracy of mastitis detection in dairy cows. Secondly, the AMS was often used to collect milk information in the automatic detection of dairy cow mastitis. The local data or manually recorded individual information of dairy cows were then combined to construct the detection model of dairy cow mastitis using machine learning classification. The automation and efficiency of mastitis detection were greatly improved, as well as the accuracy of detection. However, the AMS was easy to cause injury to the dairy cow's udder during milking, even to the animal welfare and the high cost. Finally, a sensor or multi-sensor system was developed using the rest sensors. A mastitis detection model was constructed using machine learning. The milk or udder data was also obtained to verify, according to the mastitis characteristics and the changes in milk physical and chemical properties. The detection can be expected to fully meet the harsh needs of the rapid and accurate detection of mastitis in dairy cows, due to the low cost and simple operation. Different types of sensors were used to detect mastitis in dairy cows, where the accuracy was quite different. As such, a critical review was proposed on the current progress of accuracy, real-time, and sufficient automation in the detection of mastitis in dairy cows. The future trend was also given to important technical support for future research on the automatic detection of mastitis in dairy cows.