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AI-Driven Firmness Prediction of Kiwifruit Using Image-Based Vibration Response Analysis.

Seyedeh Fatemeh Nouri1, Saman Abdanan Mehdizadeh1, Yiannis Ampatzidis2

  • 1Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz 63417-73637, Iran.

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|September 13, 2025
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Summary

This study developed a non-destructive system using computer vision and machine learning to assess kiwifruit firmness. The method accurately predicts firmness by analyzing vibration-induced surface displacement, offering a reliable alternative for quality control.

Keywords:
artificial neural networkdamping coefficientmachine visionnatural frequencyvibration analysis

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Area of Science:

  • Agricultural Engineering
  • Biophysics
  • Computer Vision

Background:

  • Non-destructive fruit firmness assessment is vital for postharvest quality control.
  • Traditional methods can be destructive and time-consuming.
  • Objective evaluation of fruit ripeness is crucial for supply chain management.

Purpose of the Study:

  • To develop and validate an image-based vibration analysis system for non-destructive kiwifruit firmness evaluation.
  • To utilize computer vision and machine learning for accurate firmness prediction.
  • To establish a reliable alternative to conventional firmness testing methods.

Main Methods:

  • 120 kiwifruits were subjected to controlled vibration (200-300 Hz).
  • A digital camera captured surface displacement over 20 seconds.
  • Image processing extracted damping coefficient and natural frequency.
  • A neural network model predicted firmness using these dynamic features.

Main Results:

  • Firmer kiwifruits showed higher natural frequencies and lower damping coefficients.
  • Softer, ripened fruits exhibited lower natural frequencies and higher damping.
  • The neural network achieved high accuracy (R²=0.9951, RMSE=0.0185) in predicting firmness.

Conclusions:

  • The image-based vibration analysis system is feasible for non-destructive firmness evaluation.
  • This method provides a reliable and efficient alternative for fruit quality assessment.
  • The system has potential for real-time implementation in automated grading and quality control.