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Non-Destructive Volume Estimation of Oranges for Factory Quality Control Using Computer Vision and Ensemble Machine

Wattanapong Kurdthongmee1, Arsanchai Sukkuea1

  • 1School of Engineering and Technology, Walailak University, 222 Thaibury, Thasala, Nakorn Si Thammarat 80160, Thailand.

Journal of Imaging
|October 28, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a non-destructive method using machine learning and computer vision to accurately predict orange volume. The approach enhances industrial quality control for agricultural products.

Area of Science:

  • Agricultural Technology
  • Computer Vision
  • Machine Learning

Background:

  • Accurate volume estimation is vital for industrial quality control, particularly in the food and agriculture sectors.
  • Traditional methods for volume estimation can be time-consuming and destructive.
  • Developing non-destructive, precise volume prediction techniques is essential for efficient quality assessment.

Purpose of the Study:

  • To develop a comprehensive, non-destructive method for predicting orange volume using machine learning and computer vision.
  • To create a reliable pipeline for estimating fruit dimensions and predicting volume.
  • To enhance industrial quality control processes in agriculture.

Main Methods:

  • Utilized top and side views of oranges with a calibrated marker to estimate four key dimensions.
Keywords:
computer visionensemble learningmachine learningnon-destructive testingquality controlstacking

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  • Engineered features beyond basic geometry, including surface-area-to-volume ratios and shape-based descriptors.
  • Trained and fine-tuned a machine learning model, specifically a Stacking Regressor, on a dataset of 150 oranges.

    • Main Results:

    • The Stacking Regressor model achieved a high R2 score of 0.971, outperforming single-model benchmarks like LightGBM.
    • The method demonstrated robustness against fruit variability by relying on fundamental physical characteristics.
    • The approach is adaptable for various produce types, indicating broad applicability.

    Conclusions:

    • The developed method offers a precise and non-destructive solution for orange volume prediction.
    • This technique supports real-time density calculation for automated defect detection and quality grading in factory settings.
    • The study highlights the potential for advanced computer vision and machine learning in agricultural quality control.