Machine Vision-enabled Yak Weight Estimation on Edge Devices

Abstract

In the field of stock farming, body dimensions and weight of a yak may reasonably reveal its growing characters, productivity and genetic characteristics. However, it is arduous for the herdsman to manually measure the body and dimension of yaks. Fortunately, with the development of mobile computing and edge devices, it is preferable and possible for the herdsman to estimate the yaks weight and size with handheld devices (e.g., mobile phones). This paper aims at providing machine visual-based yak body height and weight estimate method for edge devices. In our method, to begin with a foreground image of the yak is extracted; and measuring point identification is carried out to identify measuring points of the yak. Then, a ratio of its body dimensions is acquired. Both body dimensions and weight of the yak are acquired through comparison with relevant data. 25 yaks in different age groups were randomly selected from a herd to perform experiments. As indicated by corresponding experimental results, the foreground extraction approach has the potential to generate split images with good boundaries. As for the measuring point identification method selected for the yak, it features preferable accuracy and stability. For example, estimated values of its standing height, body length, chest depth, hipcross height and body weight, average errors between the measured values and them are proven to be 1.95%, 3.11%, 4.91%,3.35% and 7.79% respectively. By contrast to the traditional measuring approaches, the proposed method may improve measurement efficiency and reduce stimulation caused by manual measurement to the yak.