# Progress of Machine Vision Technologies in Intelligent Dairy Farming
## 3 Application of Machine Vision in Main Behavior Recognition of Dairy Cows
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Behavior** & **Recognition Methods** & **Results** \\ \hline \multirow{3}{*}{[PERSON] et al. [40], 2022} & \multirow{3}{*}{Lying, standing, walking} & Rank eXpansion Network 3D (Rexnet 3D), Resnet101, Mobilev2, Mobilev3, Shufflev2, C3D and S3D & lying and walking: 97.5\%, standing: 90\% \\ \hline \multirow{3}{*}{[PERSON] et al. [41], 2020} & Entering, leaving, turning, resting, feeding, and drinking & Gaussian mixture model, ViBe, and the new integrated background model (combine & Entering: 94.38\%, leaving: 92.86\%, staying: 96.85\%, turning: 93.51\%, feeding: inter-frame difference methods) & 92.86\%, staying: 96.85\%, turning: 93.51\%, feeding: 79.69\%, drinking: 81.73\%. \\ \hline \multirow{3}{*}{[PERSON] et al. [42], 2021} & drinking, ruminating, walking, standing and lying & VGG16 feature extraction & Average precision, recall, specificity, and accuracy were long short-term memory) classification model & 97.87\% \\ \hline \multirow{3}{*}{[PERSON] et al. [43], 2020} & \multirow{3}{*}{Lying, standing, walking, drinking, and feeding} & EfficientNet for feature extraction, BiFPN (bidirectional feature pyramid network) for feature fusion, C3D, VGG16-LSTM, ResNet50-LSTM, and DenseNet169-LSTM for behavior recognition & 97.87\% \\ \hline \multirow{3}{*}{[PERSON] et al. [44], 2021} & \multirow{3}{*}{Drinking, grazing} & Cascade R-CNN, Temporal Segment Networks (TSNs) & drinking: 84.4\%, grazing: 94.4\% \\ \hline \multirow{3}{*}{[PERSON] et al. [45], 2023} & \multirow{3}{*}{Lying, standing, walking, drinking, and feeding} & ECA (Efficient Channel Attention) for channel information filtering, E3D (Efficient 3D CNN) algorithm for the spatiotemporal information processing of the video & Precision, recall, parameters, and FLOPs of the E3D were 98.17\%, 97.08\%, 2.35 M, and information processing of the video & 0.98 G, respectively \\ \hline [PERSON] et al. [46], 2020 & \multirow{3}{*}{Rumination behavior of dairy cows} & VGG16, VGG19, and ResNet152V & average accuracy: 95\%, recall: 98\%, precision: 98\% \\ \hline [PERSON] et al. [47], 2022 & \multirow{3}{*}{Rumination behavior of dairy cows} & YOLO algorithm combined with the Kernelized Correlation Filter (KCF) & 91.87\% \\ \hline [PERSON] et al. [48], 2019 & \multirow{3}{*}{Rumination behavior of dairy cows} & \multirow{3}{*}{optical flow method} & Highest accuracy was 87.80\%, average accuracy was 76.46\% \\ \hline [PERSON] et al. [49], 2022 & \multirow{3}{*}{Rumination behavior of dairy cows} & Horn-Schunck optical flow method & Highest filling rate was 96.76\%, the lowest filling rate was 25.36\%. \\ \hline [PERSON] et al. [50], 2020 & \multirow{3}{*}{Estrus behavior of dairy cows} & SSD (Single Shot Detector) + Inception V2, Faster R-CNN & 90\% \\ \hline [PERSON] et al. [51], 2019 & \multirow{3}{*}{Estrus behavior of dairy cows} & SVM (Support Vector Machine) & 90\% \\ \hline \multirow{3}{*}{[PERSON] et al. [52], 2015} & \multirow{3}{*}{Estrus behavior of dairy cows} & Random Forest, Linear & \multirow{3}{*}{65.6\%} \\  & & Discriminant Analysis, and & \\ \cline{1-1}  & & Neural Network & \\ \hline [PERSON] et al. [53], 2019 & \multirow{3}{*}{Estrus behavior of dairy cows} & Background Subtraction with & \multirow{3}{*}{98.3\%} \\  & & Color and Texture Features & \\ \cline{1-1}  & & (BSCFT), SVM & \\ \hline [PERSON] et al. [54], 2022 & \multirow{3}{*}{Estrus behavior of dairy cows} & Mask R-CNN, Kalman filter and & \multirow{3}{*}{95.5\%} \\ \cline{1-1}  & & Hungarian algorithm & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Research on Machine Vision in Motion Behavior Recognition of Dairy Cows. Basic motion behaviors of dairy cows contain abundant health information. Timely and accurate identification of cow motion behavior is helpful for precise breeding and disease prevention of cows. In order to accurately and effectively recognize the basic movement behavior of cows, [PERSON] et al.