Potato surface defect detection based on deep transfer learning

Chenglong Wang, Zhifeng Xiao

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish (US)
Article number863
JournalAgriculture (Switzerland)
Issue number9
StatePublished - Sep 2021

All Science Journal Classification (ASJC) codes

  • Food Science
  • Agronomy and Crop Science
  • Plant Science


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