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Potato Surface Defect Detection Based on Deep Transfer Learning

Author

Listed:
  • Chenglong Wang

    (School of Electronic Information and Electrical Engineering, Huizhou University, Huizhou 516007, China)

  • Zhifeng Xiao

    (School of Engineering, Penn State Erie, The Behrend College, Erie, PA 16563, USA)

Abstract

Food defect detection is crucial for the automation of food production and processing. Potato surface defect detection remains challenging due to the irregular shape of potato individuals and various types of defects. This paper employs deep convolutional neural network (DCNN) models for potato surface defect detection. In particular, we applied transfer learning by fine-tuning a base model through three DCNN models—SSD Inception V2, RFCN ResNet101, and Faster RCNN ResNet101—on a self-developed dataset, and achieved an accuracy of 92.5%, 95.6%, and 98.7%, respectively. RFCN ResNet101 presented the best overall performance in detection speed and accuracy. It was selected as the final model for out-of-sample testing, further demonstrating the model’s ability to generalize.

Suggested Citation

  • Chenglong Wang & Zhifeng Xiao, 2021. "Potato Surface Defect Detection Based on Deep Transfer Learning," Agriculture, MDPI, vol. 11(9), pages 1-18, September.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:9:p:863-:d:632612
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    References listed on IDEAS

    as
    1. Jiali Fang & Ben Jacobsen & Yafeng Qin, 2014. "Predictability of the simple technical trading rules: An out‐of‐sample test," Review of Financial Economics, John Wiley & Sons, vol. 23(1), pages 30-45, January.
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    Cited by:

    1. Abozar Nasirahmadi & Ulrike Wilczek & Oliver Hensel, 2021. "Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models," Agriculture, MDPI, vol. 11(11), pages 1-13, November.
    2. Huishan Li & Lei Shi & Siwen Fang & Fei Yin, 2023. "Real-Time Detection of Apple Leaf Diseases in Natural Scenes Based on YOLOv5," Agriculture, MDPI, vol. 13(4), pages 1-19, April.

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