Potato Surface Defect Detection Based on Deep Transfer Learning

Chenglong Wang and Zhifeng Xiao
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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

Agriculture, 2021, vol. 11, issue 9, 1-18

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.

Keywords: potato surface defect detection; deep convolutional neural networks; SSD; faster RCNN; object detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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