The Detection of Maize Seedling Quality from UAV Images Based on Deep Learning and Voronoi Diagram Algorithms

Assessing the quality of maize seedlings is crucial for field management and germplasm evaluation. Traditional methods for evaluating seedling quality mainly rely on manual field surveys, which are not only inefficient but also highly subjective, while large-scale satellite detection often lacks suf...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 16; H. 19; S. 3548
Hauptverfasser:	Ren, Lipeng, Li, Changchun, Yang, Guijun, Zhao, Dan, Zhang, Chengjian, Xu, Bo, Feng, Haikuan, Chen, Zhida, Lin, Zhongyun, Yang, Hao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.10.2024
Schlagworte:	Accuracy Aerial surveys Agricultural production Agriculture Algorithms Coefficient of variation Comparative analysis Convexity Corn Deep learning Distribution Drone aircraft emergence uniformity Environmental aspects Evaluation Germplasm Identification and classification Image processing Image quality Image resolution Image segmentation Machine learning Machine vision maize emergence Methods Object recognition Photography, Aerial plant detection Plant extracts Planting management Polygons Quality assessment Remote sensing systems Satellite imagery Seedlings Spatial analysis Unmanned aerial vehicles unmanned aerial vehicles (UAVs) Vegetation Voronoi graphs Voronoi polygons China
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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