# Progress of Machine Vision Technologies in Intelligent Dairy Farming
## 3 Application of Machine Vision in Main Behavior Recognition of Dairy Cows
By studying eight videos of cows' mouths, the result showed that the highest filling rate was 96.76%, the lowest filling rate was 25.36%, and the average true filling rate was 63.91%. Due to the small area of the mouth area of cows, it is still difficult to identify rumination behavior under the influence of illumination and obstructions. Wearable sensors are more suitable for identifying this behavior. Traditional dairy estrus detection mainly depends on manual observation, which is time-consuming and laborious, and the detection efficiency is low. Accurate judgment of cow estrus behavior can improve the pregnancy rate, prolong the lactation period, and enhance the benefits of cow breeding. [PERSON] et al. extracted estrus features of cow videos through the Faster R-CNN and Single Shot Detector models with the Inception V2 [50]. The confidence scores of the SSD algorithm exceeded 90% in visualizing bounding boxes on the single class objects. Faster R-CNN had poor accuracy in identifying objects with color differences between subjects and barn surface area, and it achieved a 50% detection efficiency.However, this system still has certain application value in detecting cow feeding behavior. [PERSON] et al. used a set of discriminative features to detect cattle estrus. The author extracted the positions of key points in the body of cows to identify the behavioral feature of estrous cows [51]. The classification model adopted. Support Vector Machine with the Radial Basis Function, the maximum accuracy of the global model of this method was 90.0%, and the maximum accuracy of the cattle-specific model could rise to 92.0%. Earlier, [PERSON] et al. used machine vision to detect the estrus behavior of cows from different perspectives [52]. They continuously observed the estrus behavior and abnormal daily behavior of cows through machine vision after injecting them with estrus drugs. Random Forest, Linear Discriminant Analysis, and Neural Network were applied to automatically analyze the estrus behavior of 18 standing cows, and the accuracy of the results was between 91% and 100%. When comparing visual observation with progesterone profiles of all 32 cows, the accuracy was 65.6%. [PERSON] et al. used Background Subtraction to extract detection regions, and then utilized the Support Vector Machine (SVM) classifier to identify mounting behavior, obtaining accuracy and omission rates of 98.3% and 6.4%, respectively [53]. [PERSON] et al.proposed an estrus detection approach that tracks and identifies cattle mating postures individually based on video inputs [54]. They used the Mask R-CNN deep learning framework and a lightweight tracking algorithm as a post-processing step to detect mounting behaviors of the dairy cow, achieving a 95.5% detection accuracy in identifying the estrus behaviors of cattle. Finally, [PERSON] reviewed the detection of dairy cows' estrus behavior, especially the application of automated detection techniques in cow estrus. This method embraces multiple technologies and included pressure sensing systems, activity meters, video cameras, recordings of vocalization, as well as measurements of body temperature and mill progesterone concentration. The method of integrating multiple technologies will achieve better results in detection sensitivity and specificity and has enormous research potential in the future [55]. The recognition of walking, lying down, and other behaviors of dairy cows is relatively simple and has a high recognition rate. However, the recognition of rumination and estrus has certain difficulties. Because the rumination behavior is not obvious, it is difficult to recognize in the case of insufficient light or occlusion. Although the estrus and mounting behavior is easy to identify, cows have a latent estrus phenomenon; in this case, the estrus behavior of cows is no different from their daily behavior, and there will be no mounting phenomenon. This situation can lead to missed detection when purely using machine vision as a means of identification.