# Progress of Machine Vision Technologies in Intelligent Dairy Farming
## 1 Introduction  
Milk is an important source of protein in human diets, and its demand is expanding year by year. World milk production in 2022 reached around 930 million tons, up by 0.6 percent from 2021 [1]. The resulting scale of dairy cows farming is also increasing rapidly. By the end of 2022, the number of dairy cows in the world reached 140 million. Figure 1 shows the number of dairy cows in the world's major dairy farming countries in 2022 [2]. The changes in the scale of dairy cows farms have also led to changes in feeding methods. With the development of information technology, dairy cow feeding methods are also moving toward large-scale and intelligent development. Intelligent breeding can improve breeding efficiency, reduce breeding costs and manpower investment, and benefit the welfare of cows. Therefore, it is also the main development direction of cow breeding in the future. In the process of intelligent breeding, the automatic acquisition of dairy cow behavior is a prerequisite for achieving intelligence. Back in the 1960s, [PERSON] et al. began using the pendulum principle and the mechanical device of a clock to record the grazing time and frequency of cattle and sheep [3]. By around 2000, research on monitoring cow behavior using mature sensor technology had become extremely common, and nearly 10,000 related articles could be obtained using similar subject headings [4, 5, 6, 7, 8, 9]. As a result, the intelligent era of dairy cow farming has gradually begun. For a long time, using various touch sensors to obtain cows' motion behavior, physiological indicators, body size parameters, and so on has been the main means of the intelligent breeding of dairy cows. [PERSON] et al. used triaxial acceleration sensors to monitor the resting, feeding, and ruminating behaviors of dairy cows [10]. [PERSON] et al. used wearable motion sensors to identify the lameness of cattle [11]. [PERSON] and [PERSON] et al. used a resistive sensor implanted in the cow's obstetric canal to monitor the estrus behavior of dairy cows [6; 12]. [PERSON] and [PERSON] et al. used pressure sensors to calculate the frequency of breaths of cattle [7; 13]. [PERSON] monitored the feeding and rumination of dairy cows by recording their mandibular acoustic signals [14]. [PERSON] et al. also used an animal activity monitor sensor to score the body condition of dairy cows and milk yield [15]. On the other hand, while using sensors to obtain physiological and physical data from dairy cows, this direct touch method (such as the wearing method shown in Figure 1) can cause stress reactions in dairy cows, which is not conducive to their animal welfare. According to research, the welfare status of dairy cows is closely related to their health status, milk yield, and milk quality [16; 17; 18]. The traditional method of obtaining cow growth data using sensors is facing challenges, and the use of sensors also has the following drawbacks. * High cost In order to obtain information about each dairy cow, it is necessary to wear sensors for each cow. As the number of farmed cows increases, the overall cost of the sensors increases. * Easily damaged Due to the presence of corrosive substances such as cow excreta and food residues in the breeding environment, sensors can be easily damaged. In addition, when cows experience stress reactions, they can also rub against the wearing parts, increasing the probability of sensor damage. * Waste of human resources The battery life of the small and portable sensors is poor, requiring manual periodic replacement of the battery. When some devices are not working properly, they also require manual removal and maintenance, which requires significant human resource costs throughout the sensor's use process. * Single function Each sensor can only measure a desired parameter.