# Progress of Machine Vision Technologies in Intelligent Dairy Farming
## 2 Application of Machine Vision in Dairy Cow Identification
proposed an improved convolutional neural network (Res_5_2 Net) method for individual dairy cow recognition based on dairy cow facial images [23]. The recognition accuracy can reached 94.53%. [PERSON] et al. proposed a novel unified global and part feature deep network framework (GPN) based on the feature maps from the backbone network ResNet50.This network could framework can capture both the global feature and the local detail to enhance the feature representation discriminability [24]. Finally, the recognition accuracy in rank-5 and rank-10 network depth can reach 95.9% and 97.4%, respectively. [PERSON] et al. also used facial features to identify cow individuals when developing an automatic cow temperature measurement platform [25].They used the three layers of the YOLO V3-tiny recognition algorithm to achieve a recognition accuracy of 90%. The texture information of a cow's muzzle print does not change during its growth. Therefore, many scholars use the cow muzzle print as a feature to identify cows. As early as 2013, [PERSON] et al. used the Scale-Invariant Feature Transform (SIFT) to extract the feature points of the cow's muzzle print image, and coupled the Random Sample Consistency (RANSAC) algorithm with the SIFT output to remove outliers and achieve higher robustness, and obtaineding a recognition accuracy of 93.3% [26]. [PERSON] used the Local Binary Pattern (LBP) to extract features from cow muzzle print images, and classified them using Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms, obtaining a recognition accuracy of 99.5% [27]. In addition, this method can also achieve high-precision recognition when the test images were is rotated at different angles or is partially occluded. [PERSON] et al. also used the SIFT algorithms and Speeded-Up Robust Features (SURFs) to extract cows' muzzle print features, and used Multi-Layer Perceptron (MLP), Random Forest (RF), and Decision Tree (DT) algorithms for image classification [28]. The recognition rates of these three algorithms, respectively, were 69.32%, 74.88%, and 79.60%.Finally, they improved the accuracy to 83.35% through integrated adaptive enhancement methods. [PERSON] et al. used the Weber Local Descriptor (WLD) and LBP to extract cows' muzzle print features, and then used SVM to identify the cow's identity from the fusion features [29] and achieved a recognition accuracy of this method was 96.5%. [PERSON] et al. have also published several articles in recent years that use deep learning to identify cow identity through cow muzzle print features. By improving the deep learning algorithm, the recognition accuracy can reached 98.99% [30]. [PERSON] used a multi-level framework combination method and utilizedthe Inception Resnet V2 model to extract feature values, achieving a recognition accuracy of 98.21% [31]. It is interesting to note that many other scholars have used machine learning algorithms to identify cattle based on their mouth and nose texture images, but the main applications were for cattle information traceability, lost cow retrieval, and insurance claims. Due to the small area of the face and muzzle print images of cows and the irregular head movement of cows, it is difficult to obtain real-time face and muzzle print image data in on-farm conditions. For the automatic, real-time, and continuous on-site identification of cows required in precision breeding, this method still cannot meet the needs of large-scale and automated breeding. A relatively large area of body image information is undoubtedly a good choice for cow identification using machine vision. The body pattern of cows contains rich individual information, such as the white-black pattern of Holstein cows. By extracting the body pattern features of cows, a precise identification of cow identity can be achieved. [PERSON] et al. extracted Holstein cows' body pattern from 528 segments of recorded video information, and then used a deep learning algorithm of FAST + SIFT + FLANN, achieving a recognition rate of 96.72% [32]. [PERSON] et al.