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Precise 2D vision solutions for estimating avocado physical characteristics.

Hieu M Tran1,2, Tuan M Le1,2, Ke Wang2

  • 1School of Science, Engineering and Technology, RMIT University Vietnam, Ho Chi Minh City, Viet Nam.

Scientific Reports
|October 9, 2025
PubMed
Summary

The Frustum method accurately estimates avocado mass using geometry, outperforming regression models. This offers cost-effective solutions for agricultural industries needing precise grading and packaging.

Keywords:
Agricultural EngineeringAvocado Mass PredictionCross-Sectional AnalysisFood Processing TechniquesGeometry-Based MethodsRegression Models

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Area of Science:

  • Agricultural Engineering
  • Food Engineering
  • Computer Vision

Background:

  • Accurate estimation of physical properties like mass is crucial for efficient agricultural and food processing operations.
  • Current methods for avocado mass estimation may lack precision or require extensive data and complex computations.
  • Automated systems for grading, weighing, and packaging require reliable predictive models.

Purpose of the Study:

  • To systematically review and compare geometry-based and regression model-based techniques for avocado mass estimation.
  • To evaluate the accuracy and reliability of different methods using manually collected cross-sectional data.
  • To identify optimal modeling approaches for effective and cost-efficient agricultural and food engineering applications.

Main Methods:

  • Systematic review and comparison of geometry-based (Frustum method) and regression-based techniques (Ridge, LASSO, Elastic Net, Linear Regression, MLP Regressor, Gradient Boosting Regressor).
  • Utilized manually collected cross-sectional avocado data for model training and validation.
  • Employed hyper-parameter optimization and K-fold cross-validation for regression models to ensure reliability and minimize overfitting.

Main Results:

  • The Frustum method demonstrated superior performance, achieving a Root Mean Square Percentage Error (RMSPE) of 4.24% and Mean Absolute Percentage Error (MAPE) of 3.43% at 20 slices.
  • Regression models, particularly Ridge Regression, showed strong performance with an average RMSPE of 4.30% and MAPE of 3.52% across 5 folds at 15 slices.
  • Models accurately estimated avocado dimensions (width and length) with errors below 1.53% and model-fit parameters exceeding 99%.

Conclusions:

  • The Frustum method is a robust and reliable technique for precise avocado mass estimation, requiring neither large datasets nor complex computations.
  • Regression models offer competitive and stable alternatives, with Ridge Regression being a notable performer.
  • These findings support the implementation of automation technologies in precision agriculture and intelligent food processing, including robotic harvesting and grading.