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Multi-Camera-Based Sorting System for Surface Defects of Apples.

Ju-Hwan Lee¹, Hoang-Trong Vo¹, Gyeong-Ju Kwon²

¹Department of ICT Convergence System Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea.

Sensors (Basel, Switzerland)

|April 28, 2023

Summary

This study introduces a novel multi-camera apple sorting system for defect detection. It ensures uniform surface imaging, improving classification accuracy over traditional methods.

Keywords:

CNN classifier apple sorting system knowledge distillation multi-camera

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Area of Science:

Agricultural Engineering
Computer Vision
Machine Learning

Background:

Traditional apple sorting relies on manual labor or single-camera systems.
Existing methods struggle with uniform apple surface capture, leading to misclassification.
Random rotation mechanisms hinder accurate defect detection.

Purpose of the Study:

To develop an efficient and precise multi-camera apple sorting system.
To overcome limitations of single-camera and random rotation methods.
To improve defect detection accuracy and sorting reliability.

Main Methods:

A novel rotation mechanism for individual apples.
Simultaneous image acquisition using three cameras for complete surface coverage.
Convolutional Neural Network (CNN) classifier with knowledge distillation for efficient analysis.
Deployment on embedded hardware for real-time processing.

Main Results:

Achieved 93.83% accuracy in defect classification.
CNN inference speed of 0.069 seconds.
Total sorting time of 2.84 seconds per apple.
Uniform and accurate surface imaging demonstrated.

Conclusions:

The proposed multi-camera system offers a significant improvement for apple defect detection.
Efficient and reliable sorting of apples is achievable with the integrated system.
This technology enhances the precision and speed of automated fruit sorting.