Fast Quality Detection of Astragalus Slices Using FA-SD-YOLO

Fan Zhao, Jiawei Zhang, Qiang Liu, Chen Liang, Song Zhang and Mingbao Li ()
Additional contact information
Fan Zhao: College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
Jiawei Zhang: College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
Qiang Liu: College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
Chen Liang: College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
Song Zhang: College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
Mingbao Li: College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China

Agriculture, 2024, vol. 14, issue 12, 1-19

Abstract: Quality inspection is a pivotal component in the intelligent sorting of Astragalus membranaceus (Huangqi), a medicinal plant of significant pharmacological importance. To improve the precision and efficiency of assessing the quality of Astragalus slices, we present the FA-SD-YOLO model, an innovative advancement over the YOLOv8n architecture. This model introduces several novel modifications to enhance feature extraction and fusion while reducing computational complexity. The FA-SD-YOLO model replaces the conventional C2f module with the C2F-F module, developed using the FasterNet architecture, and substitutes the SPPF module with the Adaptive Inverted Fusion (AIFI) module. These changes markedly enhance the model’s feature fusion capabilities. Additionally, the integration of the SD module into the detection head optimizes parameter efficiency while improving detection performance. Performance evaluation highlights the superiority of the FA-SD-YOLO model. It achieves accuracy and recall rates of 88.6% and 89.6%, outperforming the YOLOv8n model by 1.8% and 1.3%, respectively. The model’s F1 score reaches 89.1%, and the mean average precision (mAP) improves to 93.2%, reflecting increases of 1.6% and 2.4% over YOLOv8n. These enhancements are accompanied by significant reductions in model size and computational cost: the parameter count is reduced to 1.58 million (a 47.3% reduction), and the FLOPS drops to 4.6 G (a 43.2% reduction). When compared with other state-of-the-art models, including YOLOv5s, YOLOv6s, YOLOv9t, and YOLOv11n, the FA-SD-YOLO model demonstrates superior performance across key metrics such as accuracy, F1 score, mAP, and FLOPS. Notably, it achieves a remarkable recognition speed of 13.8 ms per image, underscoring its efficiency and suitability for real-time applications. The FA-SD-YOLO model represents a robust and effective solution for the quality inspection of Astragalus membranaceus slices, providing reliable technical support for intelligent sorting machinery in the processing of this important medicinal herb.

Keywords: Astragalus slices; FA-SD-YOLO; quality 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: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/12/2194/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/12/2194/ (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:14:y:2024:i:12:p:2194-:d:1534174

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 ().