FEgg3D: A Non-Contact and Dynamic Measuring Device for Egg Shape Parameters and Weight Based on a Self-Designed Laser Scanner

Yuhua Zhu, Daoyi Song, Xintong Wu, Junyan Bu, Sheng Luo, Hongying Wang and Liangju Wang ()
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Yuhua Zhu: College of Engineering, China Agricultural University, Beijing 100083, China
Daoyi Song: College of Engineering, China Agricultural University, Beijing 100083, China
Xintong Wu: College of Engineering, China Agricultural University, Beijing 100083, China
Junyan Bu: College of Engineering, China Agricultural University, Beijing 100083, China
Sheng Luo: College of Engineering, China Agricultural University, Beijing 100083, China
Hongying Wang: College of Engineering, China Agricultural University, Beijing 100083, China
Liangju Wang: College of Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 8, 1-26

Abstract: In large-scale poultry farming, real-time online measurement of egg weight and shape parameters remains a challenge. To address this, we developed FEgg3D, a non-contact dynamic measuring device based on a self-designed laser scanner. The device employed a subset of the point cloud generated to predict the shape parameters and weight of eggs using machine learning algorithms. Different colors and sizes of eggs on various backgrounds were scanned using FEgg3D mounted on a gantry system. Our results demonstrated the following: (1) The Support Vector Regression (SVR) model was optimal for major axis length estimation, with an R 2 of 0.932 using six laser lines and eight points per line. (2) The Gaussian Process Regression (GPR) model excelled in minor axis length estimation, achieving an R 2 of 0.974 with six laser lines and 16 points per line. (3) SVR was optimal for volume estimation, attaining an R 2 of 0.962 with six laser lines and 16 points per line. (4) GPR showed superior performance in weight prediction, with an R 2 of 0.964 using five laser lines and 16 points per line. Including density features significantly improved accuracy to an R 2 of 0.978. This approach paves the way for advanced online egg measurement in commercial settings.

Keywords: laser scanner; machine learning; dynamic; egg parameters; egg weight (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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