Fast Quality Detection of <i>Astragalus</i> Slices Using FA-SD-YOLO
Quality inspection is a pivotal component in the intelligent sorting of <i>Astragalus membranaceus</i> (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...
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MDPI AG
2024-11-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/14/12/2194 |
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| author | Fan Zhao Jiawei Zhang Qiang Liu Chen Liang Song Zhang Mingbao Li |
| author_facet | Fan Zhao Jiawei Zhang Qiang Liu Chen Liang Song Zhang Mingbao Li |
| author_sort | Fan Zhao |
| collection | DOAJ |
| description | Quality inspection is a pivotal component in the intelligent sorting of <i>Astragalus membranaceus</i> (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 <i>Astragalus membranaceus</i> slices, providing reliable technical support for intelligent sorting machinery in the processing of this important medicinal herb. |
| format | Article |
| id | doaj-art-bb29e786a3264dea8d8cae0cd4ac4e53 |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-bb29e786a3264dea8d8cae0cd4ac4e532025-08-20T02:00:55ZengMDPI AGAgriculture2077-04722024-11-011412219410.3390/agriculture14122194Fast Quality Detection of <i>Astragalus</i> Slices Using FA-SD-YOLOFan Zhao0Jiawei Zhang1Qiang Liu2Chen Liang3Song Zhang4Mingbao Li5College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaQuality inspection is a pivotal component in the intelligent sorting of <i>Astragalus membranaceus</i> (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 <i>Astragalus membranaceus</i> slices, providing reliable technical support for intelligent sorting machinery in the processing of this important medicinal herb.https://www.mdpi.com/2077-0472/14/12/2194<i>Astragalus</i> slicesFA-SD-YOLOquality detection |
| spellingShingle | Fan Zhao Jiawei Zhang Qiang Liu Chen Liang Song Zhang Mingbao Li Fast Quality Detection of <i>Astragalus</i> Slices Using FA-SD-YOLO Agriculture <i>Astragalus</i> slices FA-SD-YOLO quality detection |
| title | Fast Quality Detection of <i>Astragalus</i> Slices Using FA-SD-YOLO |
| title_full | Fast Quality Detection of <i>Astragalus</i> Slices Using FA-SD-YOLO |
| title_fullStr | Fast Quality Detection of <i>Astragalus</i> Slices Using FA-SD-YOLO |
| title_full_unstemmed | Fast Quality Detection of <i>Astragalus</i> Slices Using FA-SD-YOLO |
| title_short | Fast Quality Detection of <i>Astragalus</i> Slices Using FA-SD-YOLO |
| title_sort | fast quality detection of i astragalus i slices using fa sd yolo |
| topic | <i>Astragalus</i> slices FA-SD-YOLO quality detection |
| url | https://www.mdpi.com/2077-0472/14/12/2194 |
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