# Progress of Machine Vision Technologies in Intelligent Dairy Farming
## 4 Application of Machine Vision in Disease Prevention and Treatment of Dairy Cows
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Behavior** & **Characteristic Extraction and Classification Model** & **Result** \\ \hline [PERSON] et al. [56], 2020 & \multirow{2}{*}{Lameness Behavior} & YOLO V3, Long Short-Term Memory & \multirow{2}{*}{98.57\%} \\  & & (LSTM) & \\  & & DSKNN (Distilling data of K-Nearest & \\ [PERSON] et al. [57], 2018 & \multirow{2}{*}{Lameness Behavior} & Neighbor) + LCCCT (Local Cyclic & SVM: 91.11\%, Naive Bayes: \\  & & Center Compensation Tracking & 86.11\%, KNN: 93.89\% \\  & & Model), SVM, Naive Bayes, KNN & \\ [PERSON] et al. [58], 2023 & \multirow{2}{*}{Legs Tracking, Lameness Behavior} & Siamese attention model (Siam-AM), & \multirow{2}{*}{94.73\%} \\  & & Behavior & SVM & \\ [PERSON] et al. [59], 2023 & \multirow{2}{*}{Lameness Behavior} & Temporal aggregation network using & \multirow{2}{*}{98.89\%} \\  & & micromotion features & \\ [PERSON] et al. [60], 2022 & \multirow{2}{*}{Cow Mastitis} & YOLO v5 & \multirow{2}{*}{85.71\%} \\ [PERSON] et al. [61], 2017 & & Linear Regression, Power Regression, & \\  & & Quadratic Regression, and Principal & \\  & & Component Regression & 3.67\% \\ [PERSON] et al. [62], 2017 & \multirow{2}{*}{Cow Mastitis} & Matlab and SPSS Tool Software & 57.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Research on machine vision in cow disease prevention and control. Using the temperature difference between the eyes and udder of cows to identify mastitis is currently a common method for machine vision. [PERSON] et al. proposed a new detection method of dairy cow mastitis based on infrared thermal images [60]. This method combined the improved left and right udder skin surface temperature (USST) with the ocular surface temperature. The improved detection method avoided effectively the effect of external factors. The results showed that the accuracy, specificity, and sensitivity of mastitis detection were 87.62, 84.62, and 96.30%, respectively. However, factors such as changes in the distance between the thermal imager and key parts of dairy cows and obstructions in this method can have a certain impact on the accuracy rate. [PERSON] et al. used another method to study cow mastitis. Namely, the sample milk was dropped onto a pH test paper, and the number of milk somatic cells was calculated based on the color characteristics of the pH test paper [61]. The number of milk somatic cells was used to determine whether the cow has mastitis and the severity of the disease. [PERSON] et al. also used a similar method to obtain cow udder temperature through cow udder thermal images to determine if there is mastitis [62]. However, the accuracy of this method was relatively low, only 57.3%. The above methods are currently the main research methods for non-contact machine vision in diagnosing cow mastitis. From the results of the above research on mastitis, it can be seen that the recognition of mastitis based on thermal infrared images is susceptible to factors such as attachments, shielding, and different measurement distances during non-contact temperature measurement, resulting in certain errors in measurement, and its accuracy needs to be further improved. Machine vision has further applications in precision and large-scale dairy farming, such as cow perinatal behavior recognition, measurement of cow body size parameters, cow health assessment, and evaluation of farm environmental comfort.