# Progress of Machine Vision Technologies in Intelligent Dairy Farming
## 2 Application of Machine Vision in Dairy Cow Identification  
Dairy cow identity information can be used to establish individual profiles in the cow management information system and is also a prerequisite for conducting research on other aspects of dairy cows farming. Traditional cow identification is a manual labeling method for cows, such as wearing ear tags and collars. Later, it was developed into RFID (Radio-Frequency Identification) electronic tags [19; 20]. RFID electronic tag technology is currently mature. Based on the mature application of machine learning in face recognition, many scholars have begun to recognize cow identities using cow facial information, muzzle print information, and body contour features, and these studies have achieved certain results. Table 1 shows the current main research results of dairy cow identification based on machine vision and deep learning technology. [PERSON] et al. published a paper titled 'Face recognition of cattle: can it be done? [21]. The paper reviewed identity recognition methods based on cow facial features, proving the feasibility of cow identity recognition based on facial information. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Identify Features** & **Recognition Methods** & **Accuracy** \\ \hline [PERSON] et al. [22], 2022 & Cow facial features & Integrated light-weight Retina Face-mobilenet with Additive & 91.3\% \\  & & Angular Margin Loss (ArcFace) & \\ \hline [PERSON] et al. [23], 2022 & Cow facial features & Improved Convolutional Neural Network (ResNet) & 94.5\% \\ \hline [PERSON] et al. [24], 2022 & Cow facial features & a novel unified global and part feature deep network & 97.4\% \\  & & (GPN)-framework-based ResNet50 & 93.3\% \\ \hline [PERSON] et al. [25], 2022 & Cow facial features & YOLO V3-Tiny Deep Learning Algorithm & 90.0\% \\ \hline [PERSON] al. [26], 2013 & Cow muzzle print features & SIFT (Scale-Invariant Feature Transform), RANSAC (Random Sample Consensus) algorithm & 93.3\% \\ \hline [PERSON] et al. [27], 2014 & Cow muzzle print features & LBP (Local Binary Pattern), Naive Bayes, SVM, and KNN & 99.5\% \\ \hline [PERSON] et al. [28], 2022 & Cow muzzle print features & SIFT (Scale-Invariant Feature Transform), SURF (Speeded-up Robust Features), MLP(Multilayer Perceptron Network), RF (Random Forest), and DT (Decision tree) & 83.35\% \\ \hline [PERSON] et al. [29], 2020 & Cow muzzle print features & WLD (Weber Local Descriptor), SVM (Support Vector Machine) & 96.5\% \\ \hline [PERSON] et al. [30], 2018 & Cow muzzle print features & CNN (Convolution Neural Network) and DBN (Deep Belief Network) & 98.99\% \\ \hline [PERSON] et al. [31], 2022 & Cow muzzle print features & InceptionResnetV2 + MLP (Multilayer Perceptron Network) & 98.21\% \\ \hline [PERSON] et al. [32], 2019 & Cow body features & FAST (Features from accelerated segment test) + SIFT (Scale-invariantfeature transform)+FLANN(Fast Library for Approximate Nearest neighbors) & 96.72\% \\ \hline [PERSON] et al. [33], 2022 & Cow body features & Combination of Receptive Fields (CORF) + Convolution Neural Network (ConvNet) + linear SVM & 99.64\% \\ \hline [PERSON] et al. [34], 2019 & Cow body features & InceptionV3 + LSTM (Long Short-Term Memory) & 91\% \\ \hline [PERSON] et al. [35], 2022 & Cow body features & CNN (Convolution Neural Network) + LRM (Linear Regression Model) & 93.7\% \\ \hline [PERSON] et al. [36], 2022 & Cow body features & YOLO (You Only Look Once) + CNNs (ConvolutionalNeural Networks) + SVM (Support Vector Machine) & 98.36\% \\ \hline \end{tabular}
\end{table} 
Table 1: Research of dairy cow identification based on machine vision. [PERSON] et al. integrated Additive Angular Margin Loss (ArcFace) into lightweight Retina Face-mobilenet, and created a novel cow facial recognition framework called CattleFaceNet [22]. This framework had a recognition accuracy of 91.3% and a fast recognition speed, capable of recognizing 24 images per second. According to the idea of a residual network, [PERSON] et al.