Potato Surface Defect Detection Based on Deep Transfer Learning
Problem
Potato surface defect detection is challenging due to irregular potato shapes and various defect types, and is crucial for automating food production and processing.
Why it matters
Automated defect detection supports quality control and automation in food production.
What the AI did
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.
Results
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.
Impact
Demonstrated high-accuracy automated potato defect detection with stated out-of-sample generalization.
Record
| 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 |
Importance rating
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.
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