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

He, Leilei; Wei, Ruiyang; Ding, Yusong; Huang, Juncai; Wei, Xin; Li, Rui; Wang, Shaojin; Fu, Longsheng

doi:10.3390/agronomy15061284

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Open AccessArticle

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

by

Leilei He

¹,

Ruiyang Wei

¹,

Yusong Ding

¹,

Juncai Huang

¹,

Xin Wei

¹,

Rui Li

¹,

Shaojin Wang

^1,2,*

and

Longsheng Fu

^1,3,4,5,*

¹

College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China

²

Department of Biological Systems Engineering, Washington State University, 213 L.J. Smith Hall, Pullman, WA 99164-6120, USA

³

Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, China

⁴

Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling 712100, China

⁵

Northwest A&F University Shenzhen Research Institute, Shenzhen 518000, China

^*

Authors to whom correspondence should be addressed.

Agronomy 2025, 15(6), 1284; https://doi.org/10.3390/agronomy15061284

Submission received: 21 April 2025 / Revised: 18 May 2025 / Accepted: 22 May 2025 / Published: 23 May 2025

(This article belongs to the Special Issue Transforming Agriculture Through Industrial Informatics and Emerging Technologies)

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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.

Keywords: millet; appearance quality inspection; deep learning; overlap-partitioning; high-density detection

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MDPI and ACS Style

He, L.; Wei, R.; Ding, Y.; Huang, J.; Wei, X.; Li, R.; Wang, S.; Fu, L. Enhancing Dense-Scene Millet Appearance Quality Inspection Based on YOLO11s with Overlap-Partitioning Strategy for Procurement. Agronomy 2025, 15, 1284. https://doi.org/10.3390/agronomy15061284

AMA Style

He L, Wei R, Ding Y, Huang J, Wei X, Li R, Wang S, Fu L. Enhancing Dense-Scene Millet Appearance Quality Inspection Based on YOLO11s with Overlap-Partitioning Strategy for Procurement. Agronomy. 2025; 15(6):1284. https://doi.org/10.3390/agronomy15061284

Chicago/Turabian Style

He, Leilei, Ruiyang Wei, Yusong Ding, Juncai Huang, Xin Wei, Rui Li, Shaojin Wang, and Longsheng Fu. 2025. "Enhancing Dense-Scene Millet Appearance Quality Inspection Based on YOLO11s with Overlap-Partitioning Strategy for Procurement" Agronomy 15, no. 6: 1284. https://doi.org/10.3390/agronomy15061284

APA Style

He, L., Wei, R., Ding, Y., Huang, J., Wei, X., Li, R., Wang, S., & Fu, L. (2025). Enhancing Dense-Scene Millet Appearance Quality Inspection Based on YOLO11s with Overlap-Partitioning Strategy for Procurement. Agronomy, 15(6), 1284. https://doi.org/10.3390/agronomy15061284

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

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