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Predictive method for poultry carcass visceral dimensions using 3D point cloud and Genetic Algorithm-based wavelet

Zhengwei Zhu1, Yan Chen1, Lu Cai1

  • 1College of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei, 430048, China.

Poultry Science
|December 4, 2024
PubMed
Summary

This study introduces a 3D point cloud and Genetic Algorithm-Wavelet Neural Network (GA-WNN) model for predicting poultry visceral dimensions. The GA-WNN model accurately forecasts visceral size, aiding in damage-free automated evisceration.

Keywords:
3D point cloudGenetic algorithm-based wavelet neural networkMean absolute percentage errorPoultry visceraRoot mean square error

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Poultry evisceration risks damaging internal organs, reducing product value.
  • Accurate prediction of visceral dimensions is crucial for automated processing.

Purpose of the Study:

  • To develop a predictive model for poultry carcass visceral dimensions.
  • To enhance automated evisceration technology by preventing visceral damage.

Main Methods:

  • Utilized 3D point cloud scanning and reverse engineering software to collect poultry carcass data.
  • Developed a Genetic Algorithm-based Wavelet Neural Network (GA-WNN) model.
  • Employed correlation analysis for input-target determination and K-fold cross-validation.

Main Results:

  • The GA-WNN model demonstrated superior prediction accuracy compared to six other models.
  • Achieved lowest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for most visceral dimension predictions.
  • Verified the model's generalizability across different poultry breeds.

Conclusions:

  • The 3D point cloud and GA-WNN model accurately predicts poultry visceral dimensions.
  • This method provides a theoretical basis for developing non-damaging automated evisceration systems.
  • The approach offers significant advantages in predicting visceral positions, improving processing outcomes.