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Enhancing Dense-Scene Millet Appearance Quality Inspection Based on YOLO11s with Overlap-Partitioning Strategy for Procurement

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Pubblicato in:Agronomy vol. 15, no. 6 (2025), p. 1284
Autore principale: He Leilei
Altri autori: Ruiyang, Wei, Ding Yusong, Huang Juncai, Wei, Xin, Li, Rui, Wang, Shaojin, Fu Longsheng
Pubblicazione:
MDPI AG
Soggetti:
Accuracy
Deep learning
Image resolution
Smartphones
Inspection
Applications programs
Procurement
Impurities
Image detection
Mobile computing
Grain
Automation
Millet
Partitioning
Quality assessment
Machine vision
Quality control
High resolution
Support vector machines
Rice
Image quality
Object recognition
Real time
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Abstract: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.
ISSN:2073-4395
DOI:10.3390/agronomy15061284
Fonte:Agriculture Science Database