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Apple Grading Based on Multi-Dimensional View Processing and Deep Learning.

Wei Ji1, Juncheng Wang1, Bo Xu1

  • 1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.

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Summary
This summary is machine-generated.

This study introduces an AI-powered apple grading system using YOLOv5s for defect detection and quality assessment. The model achieves high accuracy, offering efficient and accurate fruit grading.

Keywords:
Swin TransformerYolov5sapple gradingmulti-dimensional view

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Area of Science:

  • Computer Vision
  • Agricultural Technology
  • Machine Learning

Background:

  • Automated apple quality grading is crucial for the fruit industry.
  • Existing methods often lack accuracy and efficiency in defect detection and grading.

Purpose of the Study:

  • To develop an advanced apple quality grading system utilizing multi-dimensional view information processing.
  • To enhance the accuracy and speed of apple defect detection and quality assessment.

Main Methods:

  • Image preprocessing using the Retinex algorithm.
  • An improved YOLOv5s network incorporating ODConv, GSConv, and VoVGSCSP for defect and stem detection.
  • Integration of Swin Transformer with Resnet18 backbone for enhanced grading accuracy.

Main Results:

  • The defect and stem recognition model achieved 96.56% accuracy with a low loss of 0.03.
  • The quality grading model reached an average accuracy of 94.46% with a loss of 0.05.
  • The system demonstrated efficient processing at 32 frames/s with small model parameters (6.78M and 3.78M).

Conclusions:

  • The proposed multi-dimensional approach significantly improves apple quality grading accuracy and efficiency.
  • The developed AI model shows strong potential for practical application in the apple industry.
  • This research contributes to advancing automated agricultural quality control systems.