A Real-Time Sorting Robot System for Panax Notoginseng Taproots Equipped with an Improved Deeplabv3+ Model

Fujie Zhang, Yuhao Lin, Yinlong Zhu, Lixia Li (), Xiuming Cui and Yongping Gao
Additional contact information
Fujie Zhang: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Yuhao Lin: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Yinlong Zhu: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Lixia Li: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Xiuming Cui: Yunnan Key Laboratory of Sustainable Utilization of Panax Notoginseng, Kunming University of Science and Technology, Kunming 650500, China
Yongping Gao: Yixintang Pharmaceutical Group Ltd., Kunming 650500, China

Agriculture, 2022, vol. 12, issue 8, 1-18

Abstract: The classification of the taproots of Panax notoginseng is conducive to improving the economic added value of its products. In this study, a real-time sorting robot system for Panax notoginseng taproots was developed based on the improved DeepLabv3+ model. The system is equipped with the improved DeepLabv3+ classification model for different grades of Panax notoginseng taproots. The model uses Xception as the taproot feature extraction network of Panax notoginseng. In the residual structure of the Xception network, a group normalization layer with deep separable convolution is adopted. Meanwhile, the global maximum pooling method is added in the Atrous Spatial Pyramid Pooling (ASPP) part to retain more texture information, and multiple shallow effective feature layers are designed to overlap in the decoding part to minimize the loss of features and improve the segmentation accuracy of Panax notoginseng taproots of all grades. The model test results show that the Xception-DeepLabv3+ model performs better than VGG16-U-Net and ResNet50-PSPNet models, with a Mean Pixel Accuracy ( MPA ) and a Mean Intersection over Union ( MIoU ) of 78.98% and 88.98% on the test set, respectively. The improved I-Xce-DeepLabv3+ model achieves an average detection time of 0.22 s, an MPA of 85.72%, and an MIoU of 90.32%, and it outperforms Xce-U-Net, Xce-PSPNet, and Xce-DeepLabv3+ models. The system control software was developed as a multi-threaded system to design a system grading strategy, which solves the problem that the identification signal is not synchronized with the grading signal. The system test results show that the average sorting accuracy of the system is 77% and the average false detection rate is 21.97% when the conveyor belt running speed is 1.55 m/s. The separation efficiency for a single-channel system is 200–300 kg/h, which can replace the manual work of three workers. The proposed method meets the requirements of current Panax notoginseng processing enterprises and provides technical support for the intelligent separation of Panax notoginseng taproots.

Keywords: Panax notoginseng taproot; improved Deeplabv3+ model; real-time sorting robot system; multithreading technology (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: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/8/1271/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/8/1271/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:8:p:1271-:d:893515

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().