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Deep-Learning-Based Wireless Visual Sensor System for Shiitake Mushroom Sorting.

Junwen Deng1, Yuhang Liu1, Xinqing Xiao1

  • 1College of Engineering, China Agricultural University, Beijing 100083, China.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
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A new deep-learning wireless visual sensor system sorts shiitake mushrooms with high accuracy. This technology enhances mushroom quality sorting, boosting industry profits and efficiency.

Area of Science:

  • Agricultural Technology
  • Computer Vision
  • Machine Learning

Background:

  • Shiitake mushrooms are globally significant edible fungi with high nutritional and medicinal value.
  • Variations in growing environments create diverse surface textures, impacting market prices and necessitating quality sorting.
  • Efficient sorting is crucial for maximizing economic returns in the mushroom industry.

Purpose of the Study:

  • To develop a deep-learning-based wireless visual sensor system for automated shiitake mushroom sorting.
  • To leverage advanced AI models and data augmentation for high-accuracy classification.
  • To create a practical system for real-time quality assessment and sorting.

Main Methods:

  • A wireless visual sensor system was designed, integrating image collection via visual sensors and data transmission via Wi-Fi modules.
Keywords:
deep learningmushroom sortingwireless sensor

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  • The Vision Transformer model was employed for image analysis and classification.
  • Data augmentation techniques including Random Erasing, RandAugment, and Label Smoothing were applied to a small dataset.

    • Main Results:

    • The trained deep learning model achieved a near-perfect accuracy rate of 99.2% during training.
    • The developed system demonstrated a high practical sorting accuracy of 98.53% for shiitake mushrooms.
    • The system processed each image in an average of 8.7 milliseconds, indicating efficient operation.

    Conclusions:

    • The developed deep-learning wireless visual sensor system effectively sorts shiitake mushrooms with stable and high accuracy.
    • The system's efficiency and accuracy suggest its potential for broader applications in visual feature-based sorting tasks.
    • Future enhancements could include integrating binocular vision and multisensor technology for superior accuracy and minor feature identification.