Potato Surface Defect Detection Based on Deep Transfer Learning

Chenglong Wang, Zhifeng Xiao

Agriculture 2021 article cited by 51

importance 4/10 solved Industrial Vision Systems and Defect Detection food processing stage: research

Potato surface defect detection is challenging due to irregular potato shapes and various defect types, and is crucial for automating food production and processing.

Automated defect detection supports quality control and automation in food production.

Applied deep transfer learning by fine-tuning three DCNN models (SSD Inception V2, RFCN ResNet101, Faster RCNN ResNet101) on a self-developed potato defect dataset, then ran out-of-sample testing with the selected model.

Detection accuracies of 92.5% (SSD Inception V2), 95.6% (RFCN ResNet101) and 98.7% (Faster RCNN ResNet101); RFCN ResNet101 selected as final model after out-of-sample testing.

Demonstrated high-accuracy automated potato defect detection with stated out-of-sample generalization.

Venue Agriculture
Published 2021
Work type article
Authors Chenglong Wang, Zhifeng Xiao
Citations 51 (OpenAlex, at import time)
References 62
Model or method SSD Inception V2, RFCN ResNet101, Faster RCNN ResNet101 (transfer learning)
Organization not stated in source
Primary source https://doi.org/10.3390/agriculture11090863
Open access gold
Keywords defect detection, transfer learning, deep convolutional neural networks, potato, food quality, DCNN
4 / 10

real but narrower advance

Automated potato surface defect detection supports quality control and automation in food production; real but narrow advance; no scale stated in abstract.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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