3D Computer-vision system for automatically estimating heifer height and body mass

Nir, Oron; Parmet, Yisrael; Werner, Daniel; Adin, Gaby; Halachmi, I.

doi:10.1016/j.biosystemseng.2017.11.014

Cited by 92 publications

(28 citation statements)

References 20 publications

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“…Owing to the relatively low-flying altitudes and high-resolution imaging, the quadcopter presents the potential capabilities of quick monitoring of large areas and detailed aerial imagery of livestock (Windrim et al 2019) to manage the livestock, providing the benefits of low cost, time efficiency and convenient operation. The aim is to build an accurate, fast and reliable livestock classification system, which plays a vital part in an autonomous robotic system for livestock management (Van Hertem et al 2018); it is a key element for automated livestock monitoring such as the individual behaviour activities, housing welfare and grazing estimation of grassland (Nasirahmadi, Edwards, and Sturm 2017;Nir et al 2018). In this study, Mask R-CNN deep learning network is adopted which we modify and fine-tune on our own training data to detect and classify cattle and sheep.…”

Section: Discussionmentioning

confidence: 99%

Livestock classification and counting in quadcopter aerial images using Mask R-CNN

Wang

Falzon

et al. 2020

International Journal of Remote Sensing

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Quadcopters equipped with machine learning vision systems are bound to become an essential technique for precision agriculture applications in pastures in the near future. This paper presents a low-cost approach for livestock counting jointly with classification and semantic segmentation which provide the potential of biometrics and welfare monitoring in animals in real time. The method used in the paper adopts the state-of-the-art deep-learning technique known as Mask R-CNN for feature extraction and training in the images captured by quadcopters. Key parameters such as IoU (Intersection over Union) threshold, the quantity of the training data and the effect the proposed system performs on various densities have been evaluated to optimize the model. A real pasture surveillance dataset is used to evaluate the proposed method and experimental results show that our proposed system can accurately classify the livestock with an accuracy of 96% and estimate the number of cattle and sheep to within 92% of the visual ground truth, presenting competitive advantages of the approach feasible for monitoring the livestock. ARTICLE HISTORY

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Section: Discussionmentioning

confidence: 99%

Livestock classification and counting in quadcopter aerial images using Mask R-CNN

Wang

Falzon

et al. 2020

International Journal of Remote Sensing

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“…Moreover, there might be interactions between teat coordinate traits that might be better indicators of udder health, longevity, or AMS efficiency. Also, research about the use of imaging technologies for determining body conformation is ongoing (Fischer et al, 2015;Nir et al, 2017;Vlček et al, 2017). This could open up possibilities to automatically measure not only teat coordinates, but also other conformation traits, such as body height, BCS, and claw angle.…”

Section: Additional Potential Of Teat Coordinatesmentioning

confidence: 99%

Genetic analysis of udder conformation traits derived from automatic milking system recording in dairy cows

Poppe

Mulder

Ducro

et al. 2019

Journal of Dairy Science

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Cartesian teat coordinates measured by automatic milking systems (AMS) provide new opportunities to record udder conformation traits and to study changes in udder conformation genetically and phenotypically within and between parities. The objective of this study was to estimate heritabilities and repeatabilities of AMS-based udder conformation traits within parities, to estimate genetic correlations between parities for AMS-based udder conformation traits, and to estimate genetic correlations between AMS-based udder conformation traits and classifier-based udder conformation traits, longevity, and udder health. Data from 70 herds, including 12,663 first-parity cows, 10,206 second-parity cows, and 7,627 third-parity cows, were analyzed using univariate and bivariate mixed animal models. Heritabilities of the AMS udder conformation traits were large (0.37-0.67) and genetic correlations between the AMS udder conformation traits and classifier-based traits were strong (>0.91). Repeatabilities within parities were large as well (0.89-0.97), indicating that a single record on udder conformation per lactation reflects udder conformation well. Genetic correlations of AMS udder conformation traits between parities were strong (0.88-1.00) and were stronger than the permanent environmental correlations. This shows that udder conformation changes over parities, but this change is mostly due to nongenetic factors. Based on these results, the current herd classification system, where cows are scored on udder conformation once in first parity, is sufficient. The AMS udder conformation traits as defined in this study have limited value as replacement for classifier-based udder conformation traits because they have smaller genetic correlations with functional traits than classifier-based traits. In summary, udder conformation hardly changes genetically between parities and is highly repeatable within parities. Udder conformation traits based on AMS need fine-tuning before they can replace classifier-based traits, and AMS teat coordinates probably contain additional information about udder health that is yet to be explored.

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“…Globally, animal size has been widely investigated and various analytical methods have been adopted to appraise animal morphology and traits. They provide guidance for animal breed selection and animal production [3][4][5]. However, traditional measuring methods require high workload, and raise stricter requirements for standing postures of animal.…”

Section: Introductionmentioning

confidence: 99%

Body Weight Estimation of Yak Based on Cloud Edge Computing

Zhang

Sun

Zhang

et al. 2020

Preprint

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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.

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3D Computer-vision system for automatically estimating heifer height and body mass

Cited by 92 publications

References 20 publications

Livestock classification and counting in quadcopter aerial images using Mask R-CNN

Livestock classification and counting in quadcopter aerial images using Mask R-CNN

Genetic analysis of udder conformation traits derived from automatic milking system recording in dairy cows

Body Weight Estimation of Yak Based on Cloud Edge Computing

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