# Progress of Machine Vision Technologies in Intelligent Dairy Farming
## 4 Application of Machine Vision in Disease Prevention and Treatment of Dairy Cows  
In addition to its extensive application in the motion behavior recognition of dairy cows, machine vision also has prominent research results in disease prevention and the early warning of dairy cows. Table 3 shows the main research results of machine vision in recent yearsin the field of frequent cow diseases such as claudication and mastitis. Lameeness is one of the common diseases in dairy cattle, which seriously affects the health of cows and can lead to a decline in milk production and reproductive capacity. The early-onset symptoms of lameness in dairy cows are not obvious from the perspective of artificial observation. When discovered later, it will make treatment more difficult, and some can only be eliminated and slaughtered, causing significant economic losses to farmers. Therefore, research on the detection of lameness in dairy cows has always been a hot topic in dairy industry research. Applying machine vision technology to the detection of lameness in dairy cows can achieve early detection and treatment, reduce cow pain, and increase cow welfare and breeding economic benefits. [PERSON] et al. detected the leg characteristics of cows in each frame of a video through YOLO V3, and used various classification algorithms to identify them. After comprehensively comparing the accuracy, true positive and false positive rates, and the algorithm's demand for hardware resources, they concluded that the comprehensive performance of the Long Short-Term Memory (LSTM) model was best [56]. [PERSON] et al. proposed a lameness detection method of dairy cows with the fusion of LCCCT (local circulation center compensation tracking) and DSKNN (distilling data of k-nearest neighbor) [57]. Then, the SVM, Naive Bayes, and KNN classification algorithms were used to perform classification and detection experiments on cow lameness, and the accuracy rates were 91.11%, 86.11%, and 93.89% respectively. The result proved that the KNN classification algorithm can achieve better detection results. [PERSON] et al. proposed a Siamese Attention Model (Siam-AM) fusion attention mechanism, which monitors cow legs to obtain the motion tracking of cows in large-scale farms [58]. The Support Vector Machine (SVM) classifier was trained to classify the lameness behavior of cows. The Siam-AM algorithm had a lameness detection accuracy of 94.73%. In order to identify the early claudication behavior of dairy cows, [PERSON] et al. introduced the micro-motion feature as a feature value into the spatiotemporal attention mechanism, which can detect the claudication of dairy cows earlier and provide evidence for the early treatment of claudication [59]. The above scholars used machine vision to identify the lameness behavior of cows from different perspectives, providing strong technical support for the early detection and treatment of cow lameness. Dairy cows are prone to mastitis during long-term and mass milk production. Therefore, the detection and treatment of mastitis have become key concerns in dairy farming. Replacing traditional artificial physical and chemical testing methods, contactless and efficient machine vision methods have also been used in the detection of mastitis in cows in recent years.