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Smart Agriculture ›› 2022, Vol. 4 ›› Issue (2): 64-76.doi: 10.12133/j.smartag.SA202206003

• Topic--Smart Animal Husbandry Key Technologies and Equipment • Previous Articles     Next Articles

Automatic Acquisition and Target Extraction of Beef Cattle 3D Point Cloud from Complex Environment

LI Jiawei1,3(), MA Weihong2,3(), LI Qifeng2,3, XUE Xianglong2,3, WANG Zhiquan4   

  1. 1.College of Information and Electrical Engineering, China Agricultural University, Beijing, China, 100091
    2.Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China, 100097
    3.National Engineering Research Center for Information Technology in Agriculture, Beijing, China, 100097
    4.Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, T6G2R3, Canada
  • Received:2022-06-02 Online:2022-06-30 Published:2022-08-04
  • corresponding author: MA Weihong E-mail:lijiawei@cau.edu.cn;maweihong01@163.com


Non-contact measurement based on the point cloud acquisition technology is able to alleviate the stress responses among beef cattle while collecting core body dimension data, but the current 3D data collection for beef cattle is usually time-consuming and easily influenced by the environment, which is in fact inapplicable to the actual breeding environment. In order to overcome the difficulty in obtaining the complete beef cattle point clouds, a non-contact phenotype data acquisition equipment was developed with a 3D reconstruction function, which can provide a large amount of standardized 3D quantitative phenotype data for beef cattle breeding and fattening process. The system is made up of a Kinect DK depth camera, an infrared grating trigger, and an Radio Frequency Identification (RFID) trigger, which enables the multi-angle instantaneous acquisition of beef cattle point clouds when the beef cattle pass through the walkway. The point cloud processing algorithm was developed based on the C++ platform and Point Cloud Library (PCL), and 3D reconstruction of beef cattle point clouds was achieved through spatial and outlier point filtering, Random Sample Consensus (RANSAC) shape fitting, point cloud thinning, and perceptual box filtering based on the dimensionality reduction density clustering to effectively filter out the interference, such as noises from the railings close to the beef cattle, without destroying the integrity of the point clouds. In the present work, a total of 124 sets of point clouds were successfully collected from 20 beef cattles on the actual farm using this system, and the target extraction experiments were completed. Notably, the beef cattle passed through the walkway in a natural state without any intervention during the whole data collection process. The experimental results showed that the acquisition success rate of this device was 91.89%. The coordinate system of the collected point cloud was consistent with the real situation and the body dimension reconstruction error was 0.6%. This device can realize the automatic acquisition and 3D reconstruction of beef cattle point cloud data from multiple angles without human intervention, and can automatically extract the target beef cattle point clouds from a complex environment. The point cloud data collected by this system help to restore the body size and shape of beef cattle, thereby provide solid support for the measurement of core parameters such as body height, body width, body oblique length, chest circumference, abdominal circumference, and body weight.

Key words: beef cattle point cloud, 3D reconstruction, point cloud processing, automatic acquisition, target extraction, non-contact measurement

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