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Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network.

Zihao Liu1,2, Jingyi Hua2,3, Hongxiang Xue1,2

  • 1College of Engineering, Nanjing Agricultural University, Nanjing 210031, China.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

This study presents a novel hybrid 3D point cloud denoising method for accurate pig weight estimation. The approach improves feeding management and animal welfare by reducing manual weighing stress and labor costs.

Keywords:
3D sensorconvolutional neural networkpig weight estimationpoint cloud segmentation

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Animal Science

Background:

  • Accurate pig weight measurement is vital for optimizing growth, health, and feeding strategies.
  • Conventional manual weighing is inefficient, time-consuming, and stressful for pigs.
  • Existing 2D and 3D methods for weight estimation have limitations in equipment complexity and data processing.

Purpose of the Study:

  • To develop a precise and efficient pig weight estimation model using a hybrid 3D point cloud denoising approach.
  • To mitigate weight estimation bias and enhance feature extraction accuracy.
  • To provide a non-invasive and accurate method for real-time pig weight monitoring.

Main Methods:

  • A hybrid 3D point cloud denoising technique combining statistical filtering and DBSCAN clustering.
  • Utilizing the convex hull technique to isolate the pig's back for refined data.
  • Employing voxel down-sampling for improved real-time processing efficiency.
  • Integrating pig back parameters with a Convolutional Neural Network (CNN) for weight prediction.

Main Results:

  • The proposed model achieved a Mean Absolute Error (MAE) of 12.45 kg, Mean Absolute Percent Error (MAPE) of 5.36%, and Root Mean Square Error (RMSE) of 12.91 kg.
  • The method demonstrated simplified equipment configuration and reduced data processing complexity compared to existing techniques.
  • Accuracy of weight estimation was maintained without compromising efficiency.

Conclusions:

  • The hybrid 3D point cloud denoising approach offers an effective solution for precise pig weight estimation.
  • This method supports scientific feeding management, reduces labor costs, and enhances pig welfare.
  • The proposed technique provides a valuable tool for precision livestock farming and intelligent animal husbandry.