Body Weight Estimation of Yak Based on Cloud Edge Computing

Abstract

In stock farming, the body size parameters and weight of yaks can reasonably reflect the growth and development characteristics, production performance and genetic characteristics of yaks. However, it is difficult for herders to measure the body size and weight of yaks by traditional manual methods. Fortunately, with the development of edge computing, herders can use mobile devices to estimate the yak’s body size and weight. The purpose of this paper is to provide a machine vision-based yak weight estimation method for the edge equipment and establish a yak estimation comprehensive display system based on the user’s use of the edge equipment in order to maximize the convenience of herdsmen’s work. In our method, a set of yak image foreground extraction and measurement point recognition algorithm suitable for edge equipment were developed to obtain yak’s measurement point recognition image, and the ratio between body sizes was transmitted to the cloud server. Then, the body size and weight of yaks were estimated using the data mining method, and the body size estimation data were constantly displayed in the yak estimation comprehensive display system. 25 yaks in different age groups were randomly selected from the herd to perform experiments. The experimental results show that the foreground extraction method can obtain segmentation image with good boundary, and the yak measurement point recognition algorithm has good accuracy and stability. The average error between the estimated values and the actual measured values of body height, oblique length, chest depth, cross height and body weight is 1.95%, 3.11%, 4.91%, 3.35% and 7.79%, respectively. Compared with the traditional manual measurement method, the use of mobile end to estimate the body size and weight of yaks can improve the measurement efficiency, facilitate the herdsmen to breed yaks, reduce the stimulation of manual measurement on yaks, and lay a solid foundation for the fine breeding of yaks in Sanjiangyuan region.

Summer grazing by three livestock species at moderate intensity enhances primary productivity and drives community divergence in a semi‐arid steppe

“…The experiment was conducted over four years, from 2017 to 2020. The grazing sheep and goat were two years old, with their initial body weight determined using a balance (33 kg on average), and the cattle body weight determined using the body measurements method (230 kg on average) (Zhang et al, 2021). A flock of six sheep or goats, or three cattle were placed in each paddock in each rotational grazing.…”

Research on Yak Body Ruler and Weight Measurement Method Based on Deep Learning and Binocular Vision

“…In 2016, 55 Doeschl et al recorded the growth process of pigs by using a visual image analysis 56 system based on a single camera, and established the relationship between pig body 57 ruler parameters and time [13]. In 2021, Sun Zijie used machine vision to estimate yak 58 weight, and adopted traditional image processing methods to obtain yak foreground 59 images [14]. then consider the more detailed information for further judgment.…”

Research on Measurement Method of Yak Body Size and Weight Based on Convolutional Neural Network and Binocular Vision

“…In 56 2016, Doeschl et al recorded the growth process of pigs by using a visual image analysis 57 system based on a single camera, and established the relationship between pig body size 58 parameters and time [13]. In 2021, Sun Zijie used machine vision to estimate yak weight, 59 and adopted traditional image processing methods to obtain yak foreground images [14]. of the target area [16], and then consider the more detailed information for further 88 judgment.…”