Enhancing Dense-Scene Millet Appearance Quality Inspection Based on YOLO11s with Overlap-Partitioning Strategy for Procurement
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| Publicat a: | Agronomy vol. 15, no. 6 (2025), p. 1284 |
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| Autor principal: | |
| Altres autors: | , , , , , , |
| Publicat: |
MDPI AG
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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|---|---|---|---|
| 001 | 3223865162 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2073-4395 | ||
| 024 | 7 | |a 10.3390/agronomy15061284 |2 doi | |
| 035 | |a 3223865162 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231332 |2 nlm | ||
| 100 | 1 | |a He Leilei |u College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Chinawx130@nwafu.edu.cn (X.W.); | |
| 245 | 1 | |a Enhancing Dense-Scene Millet Appearance Quality Inspection Based on YOLO11s with Overlap-Partitioning Strategy for Procurement | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Accurate millet appearance quality assessment is critical for fair procurement pricing. Traditional manual inspection is time-consuming and subjective, necessitating an automated solution. This study proposes a machine-vision-based approach using deep learning for dense-scene millet detection and quality evaluation. High-resolution images of standardized millet samples were collected via smartphone and annotated into seven categories covering impurities, high-quality grains, and various defects. To address the challenges with small object detection and feature loss, the YOLO11s model with an overlap-partitioning strategy were introduced, dividing the high-resolution images into smaller patches for improved object representation. The experimental results show that the optimized model achieved a mean average precision (mAP) of 94.8%, significantly outperforming traditional whole-image detection with a mAP of 15.9%. The optimized model was deployed in a custom-developed mobile application, enabling low-cost, real-time millet inspection directly on smartphones. It can process full-resolution images (4608 × 3456 pixels) containing over 5000 kernels within 6.8 s. This work provides a practical solution for on-site quality evaluation in procurement and contributes to real-time agricultural inspection systems. | |
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Image resolution | ||
| 653 | |a Smartphones | ||
| 653 | |a Inspection | ||
| 653 | |a Applications programs | ||
| 653 | |a Procurement | ||
| 653 | |a Impurities | ||
| 653 | |a Image detection | ||
| 653 | |a Mobile computing | ||
| 653 | |a Grain | ||
| 653 | |a Automation | ||
| 653 | |a Millet | ||
| 653 | |a Partitioning | ||
| 653 | |a Quality assessment | ||
| 653 | |a Machine vision | ||
| 653 | |a Quality control | ||
| 653 | |a High resolution | ||
| 653 | |a Support vector machines | ||
| 653 | |a Rice | ||
| 653 | |a Image quality | ||
| 653 | |a Object recognition | ||
| 653 | |a Real time | ||
| 700 | 1 | |a Ruiyang, Wei |u College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Chinawx130@nwafu.edu.cn (X.W.); | |
| 700 | 1 | |a Ding Yusong |u College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Chinawx130@nwafu.edu.cn (X.W.); | |
| 700 | 1 | |a Huang Juncai |u College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Chinawx130@nwafu.edu.cn (X.W.); | |
| 700 | 1 | |a Wei, Xin |u College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Chinawx130@nwafu.edu.cn (X.W.); | |
| 700 | 1 | |a Li, Rui |u College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Chinawx130@nwafu.edu.cn (X.W.); | |
| 700 | 1 | |a Wang, Shaojin |u College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Chinawx130@nwafu.edu.cn (X.W.); | |
| 700 | 1 | |a Fu Longsheng |u College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Chinawx130@nwafu.edu.cn (X.W.); | |
| 773 | 0 | |t Agronomy |g vol. 15, no. 6 (2025), p. 1284 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223865162/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223865162/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223865162/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |