Online defect detection and automatic grading of carrots using computer vision combined with deep learning methods

Abstract

The demand for smart automatic system in postharvest technology, particularly in the postharvest of carrot production is high. In this paper, an automatic carrot grading system was developed based on computer vision and deep learning, which can automatically inspect surface quality of carrots and grade washed carrots. Specifically, based on ShuffleNet and transfer learning, a lightweight deep learning model (CDDNet) was constructed to detect surface defects of carrots. Carrot grading methods were also proposed based on minimum bounding rectangle (MBR) fitting and convex polygon approximation. Experimental results showed that the detection accuracy of the proposed CDDNet was 99.82% for binary classification (normal and defective) and 93.01% for multi-class classification (normal, bad spot, abnormity, fibrous root), and demonstrated good performance both in time efficiency and detection accuracy. The grading accuracy of MBR fitting and convex polygon approximation was 92.8% and 95.1% respectively. This research provides a practical method for online defect detection and carrot grading, and has great application potential in commercial packing lines.