An Automatic Weighting System for Wild Animals Based in an Artificial Neural Network: How to Weigh Wild Animals without Causing Stress

et al. 2021

J Wireless Com Network

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.

Section: Discussionmentioning

confidence: 99%

Body weight estimation of yak based on cloud edge computing

et al. 2021

J Wireless Com Network

“…However, errors are incurred in this paper because of the failure in recognizing sufficiently accurate measuring points. If compared with the method developed by Larios [44], the proposed approach here does not require sensors and can be also embedded in portable devices such as a mobile phone. Yaks which are huge and wild cannot be treated like ordinary domestic animals.…”

Section: Discussionmentioning

confidence: 99%

Body Weight Estimation of Yak Based on Cloud Edge Computing

et al. 2020

Preprint

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.

“…However, errors are incurred in this paper because of the failure in recognizing sufficiently accurate measuring points. If compared with the method developed by Larios [28], the proposed approach here does not require sensors and can be also embedded in portable devices such as a mobile phone. Yaks which are huge and wild cannot be treated like ordinary domestic animals.…”

Section: Discussionmentioning

confidence: 99%

Machine Vision-enabled Yak Weight Estimation on Edge Devices

et al. 2020

Preprint

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.