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

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

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 qu...

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Bibliographic Details
Main Authors: Leilei He, Ruiyang Wei, Yusong Ding, Juncai Huang, Xin Wei, Rui Li, Shaojin Wang, Longsheng Fu
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Agronomy
Subjects:
millet
appearance quality inspection
deep learning
overlap-partitioning
high-density detection
Online Access:https://www.mdpi.com/2073-4395/15/6/1284
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