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. Twenty-five 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.

Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey

“…While this method effectively extracts the cattle's contours, variations in the cattle's standing posture can still impact the measurement results. Zhang et al (2021) [56] introduced a data-mining-based approach to estimate the body size of yaks. The results showed that this method was convenient and efficient, offering an effective means to measure the body size of yaks.…”

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.…”

Intelligent weight prediction of cows based on semantic segmentation and back propagation neural network

“…Moreover, the presented area can also be influenced by the distance between the camera equipment and the cows. Although the measurement of body size makes use of key areas or body parts like body width and height, heart girth, hip width, and height and thus achieves high accuracy, expensive equipment needs to be equipped at different aspects and angles accordingly, which is not applicable to housing farms (Qiao et al, 2019 ; Du et al, 2021 ; Zhang et al, 2021 ; Dang et al, 2022 ). Instead, the method integrating area and height takes advantage of three-dimensional size information of cows and becomes an accurate and reliable measurement, which is also consistent with the favorite indicators of experienced farmers for weight estimation artificially.…”