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Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data.

Yao Liu1, Jie Zhou1, Yifan Bian1

  • 1College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China.

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Summary

This study introduces a non-contact method for estimating pig weight and body dimensions using 3D point cloud data. The developed model accurately measures pigs, improving efficiency and animal welfare in farming.

Keywords:
RGB informationbody measurementsconvolutional neural networkmulti-head attentionweight estimation

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Animal Science

Background:

  • Manual measurement of pig weight and body dimensions presents challenges including herding difficulties, stress induction in animals, and zoonotic disease control.
  • Accurate assessment of pig growth and development is vital for effective management in global animal husbandry.

Purpose of the Study:

  • To develop and validate a non-contact model for estimating pig weight and body dimensions using 3D point cloud data.
  • To address the limitations of traditional manual measurement methods in pig farming.

Main Methods:

  • Acquisition of 3D point cloud data from pig backs using a depth camera mounted above a weighbridge.
  • Application of point cloud filtering, denoising, and K-means clustering for pig back segmentation.
  • Development of a multi-head attention convolutional neural network (MACNN) incorporating RGB information for weight prediction and body dimension measurement.

Main Results:

  • The non-contact weight estimation model achieved an average absolute error of 11.552 kg and a root mean square error of 11.181 kg.
  • Incorporating RGB data into the MACNN model reduced the RMSE by 2.469 kg and MAPE by 0.8%.
  • Average relative errors for shoulder width, abdominal width, and hip width measurements were 3.144%, 3.798%, and 3.820%, respectively.

Conclusions:

  • The developed MACNN model demonstrates accuracy and reliability for non-contact pig weight estimation and body dimension measurement.
  • The integration of RGB information enhances the performance of the weight prediction model.
  • This non-contact approach offers a promising alternative to manual measurements, improving efficiency and animal welfare in pig farming.