Deep Learning-Based Research on Carrot Grading and Sorting System

Abstract

To solve carrot grading problems (low manual efficiency, unquantifiable defects/secondary damage in machinery, gaps in slender carrot (aspect ratio > 4:1) sorting), this study develops a deep learning-based system. Methods: Build CarrotDSTNet (YOLOv8-seg + DeepSORT, optimized via DualConv/SegNeXt) for quality detection; adopt fuzzy comprehensive evaluation for grading; propose CarrotDTNet with an electronic fence for sorting. Results: Detection metrics improved; grading accuracy 94% (0.37 ms); sorting accuracy 97.39%, efficiency 310 roots/min. Contribution: Realizes non-contact, high precision/efficiency sorting, solves traditional issues, and supports carrot industry automation.