A posture-based measurement adjustment method for improving the accuracy of beef cattle body size measurement based on point cloud data

Li, Jiawei; Ma, Weihong; Bai, Qiang; Tulpan, Dan; Gong, Minglun; Sun, Yi; Xue, Xindong; Zhao, Chunjiang; Li, Qifeng

doi:10.1016/j.biosystemseng.2023.04.014

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…To address this, a “standard position” probably needs to be determined in the future, with which it may be possible to reduce the variation between devices. For example, Ling et al (2022) highlighted the importance of a standard position in correctly determining body size measurements in pigs and Li et al (2023) proposed a posture-based measurement adjustment method to improve accuracy measurements based on point-cloud data in beef cattle. In the Morpho3D device, the heads of animals are restrained thanks to the use of a self-locking head gate and therefore always raised, while with the Deffilait3D device, the animal’s head may move freely, which can affect leg position and thus, WH.…”

Section: Discussionmentioning

confidence: 99%

One-shot technology for three-dimensional imaging of large animals: perspectives for ruminant management

Le Cozler,

Dumesny,

Delouard

et al. 2024

Translational Animal Science

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In numerous systems of animal production, there is increasing interest in the use of 3D imaging technology on farms for its ability to easily and safely measure traits of interest on living animals. With this information, it is possible to evaluate multiple morphological indicators of interest, either directly or indirectly, and follow them through time. Several tools for this purpose were developed, but one of their main weaknesses was their sensitivity to light and animal movement, which limited their potential for large-scale application on farms. To address this, a new device, called Deffilait3D and based on depth camera technology, was developed. In tests on 31 Holstein dairy cows and 13 Holstein heifers, the values generated for most measured indicators were highly repeatable and reproducible, with coefficients of variation lower than 4%. A comparison of measurements obtained from both Deffilait3D and previous validated system, called Morpho3D, revealed a high degree of similarity for most selected traits, e.g., less than 0.2% variation for animal volume and 1.2% for chest depth, with the highest degree of difference (8%) noted for animal surface area. Previously published equations used to estimate body weight with the Morpho3D device were equally valid using Deffilait3D. This new device was able to record 3D images regardless of the movement of animals and it is affected only by direct daylight. The on-going step is now to develop methods for automated analysis and extraction from images, which should enable the rapid development of new tools and potentially lead to the large-scale adoption of this type of device on commercial farms.

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Section: Discussionmentioning

confidence: 99%

One-shot technology for three-dimensional imaging of large animals: perspectives for ruminant management

Le Cozler,

Dumesny,

Delouard

et al. 2024

Translational Animal Science

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show abstract

“…Yin et al (2022) [71] introduce a pig posture classification algorithm and use regression models to adjust the measurements in non-standard postures, thereby enhancing the measurement precision. Li et al (2023) [72] devise a Pose Measurement Adjustment (PMA) model for calibrating various poses of cattle, predicting the calculation errors under different postures, and significantly reducing measurement result discrepancies.…”

Section: Body Dimension Measurement Of Livestock Using 2d and 3d Fusi...mentioning

confidence: 99%

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

Ma,

Sun,

et al. 2024

Sensors

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Livestock’s live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.

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“…Okayama et al [21] estimated pig weight using a simple regression model based on "adjusted volume", achieving MAPE and RMSPE of 4.87% and 6.13%, respectively. Li et al [76] used three regression analysis models-stepwise regression, ridge regression, and partial least squares regression-to estimate pig body measurements. They established a stepwise regression model with body length, body height, and shoulder width as independent variables and weight measured on a scale as the dependent variable, achieving an MAE of 2.961 kg.…”

Section: Linear Regression Weight Estimation-based Body Weight Measur...mentioning

confidence: 99%

Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review

Ma,

Qi,

Sun

et al. 2024

Agriculture

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Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement.

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A posture-based measurement adjustment method for improving the accuracy of beef cattle body size measurement based on point cloud data

Cited by 10 publications

References 27 publications

One-shot technology for three-dimensional imaging of large animals: perspectives for ruminant management

One-shot technology for three-dimensional imaging of large animals: perspectives for ruminant management

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

Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review

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