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Surface Defect Detection System for Carrot Combine Harvest Based on Multi-Stage Knowledge Distillation.

Wenqi Zhou1, Chao Song1, Kai Song1

  • 1College of Engineering, Northeast Agricultural University, Harbin 150030, China.

Foods (Basel, Switzerland)
|February 25, 2023
PubMed
Summary

This study introduces a lightweight AI network (mobile-slimv5s) for real-time carrot defect detection during harvesting. It achieves 90.7% accuracy, improving food safety and smart agriculture.

Keywords:
carrotmulti-stage knowledge distillation networknetwork lightweightsurface defect detection and sorting

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Carrot quality and safety are enhanced by surface defect detection before market entry.
  • Real-time defect detection during combine harvesting is crucial for efficiency.

Purpose of the Study:

  • To develop an efficient lightweight network for carrot surface defect detection during combine harvesting.
  • To adapt the network to image blur caused by harvester vibrations using knowledge distillation.

Main Methods:

  • Proposed an improved knowledge distillation network (mobile-slimv5s) using YOLOv5s as the teacher and a pruned MobileNetV2 as the student.
  • Trained the networks on standard and motion-blurred datasets to handle vibration-induced blur.
  • Utilized multi-stage feature connections with weighted guidance for knowledge transfer.

Main Results:

  • The optimal mobile-slimv5s network achieved a model size of 5.37 MB.
  • Achieved 90.7% accuracy with specific training parameters (LR=0.0001, BS=64, Dropout=0.65).
  • Demonstrated superior performance compared to other algorithms.

Conclusions:

  • The mobile-slimv5s network enables simultaneous carrot harvesting and defect detection in field environments.
  • This research provides a foundation for applying knowledge distillation in smart agriculture for crop sorting.
  • The study significantly improves in-field crop sorting accuracy and contributes to agricultural automation.