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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Published in:	Remote sensing (Basel, Switzerland) Vol. 16; no. 19; p. 3548
Main Authors:	Ren, Lipeng, Li, Changchun, Yang, Guijun, Zhao, Dan, Zhang, Chengjian, Xu, Bo, Feng, Haikuan, Chen, Zhida, Lin, Zhongyun, Yang, Hao
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.10.2024
Subjects:	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
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Abstract	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 sufficient accuracy. To address these issues, this study proposes an innovative approach that combines the YOLO v8 object detection algorithm with Voronoi spatial analysis to rapidly evaluate maize seedling quality based on high-resolution drone imagery. The YOLO v8 model provides the maize coordinates, which are then used for Voronoi segmentation of the field after applying the Convex Hull difference method. From the generated Voronoi diagram, three key indicators are extracted: Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate to comprehensively evaluate maize seedling quality. The results show that this method effectively extracts the VPUI, missing seedling rate, and repeated seedling rate of maize in the target area. Compared to the traditional plant spacing variation coefficient, VPUI performs better in representing seedling uniformity. Additionally, the R2 for the estimated missing seedling rate and replanting rate based on the Voronoi method were 0.773 and 0.940, respectively. Compared to using the plant spacing method, the R2 increased by 0.09 and 0.544, respectively. The maize seedling quality evaluation method proposed in this study provides technical support for precision maize planting management and is of great significance for improving agricultural production efficiency and reducing labor costs.
AbstractList	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 sufficient accuracy. To address these issues, this study proposes an innovative approach that combines the YOLO v8 object detection algorithm with Voronoi spatial analysis to rapidly evaluate maize seedling quality based on high-resolution drone imagery. The YOLO v8 model provides the maize coordinates, which are then used for Voronoi segmentation of the field after applying the Convex Hull difference method. From the generated Voronoi diagram, three key indicators are extracted: Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate to comprehensively evaluate maize seedling quality. The results show that this method effectively extracts the VPUI, missing seedling rate, and repeated seedling rate of maize in the target area. Compared to the traditional plant spacing variation coefficient, VPUI performs better in representing seedling uniformity. Additionally, the R2 for the estimated missing seedling rate and replanting rate based on the Voronoi method were 0.773 and 0.940, respectively. Compared to using the plant spacing method, the R2 increased by 0.09 and 0.544, respectively. The maize seedling quality evaluation method proposed in this study provides technical support for precision maize planting management and is of great significance for improving agricultural production efficiency and reducing labor costs. 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 sufficient accuracy. To address these issues, this study proposes an innovative approach that combines the YOLO v8 object detection algorithm with Voronoi spatial analysis to rapidly evaluate maize seedling quality based on high-resolution drone imagery. The YOLO v8 model provides the maize coordinates, which are then used for Voronoi segmentation of the field after applying the Convex Hull difference method. From the generated Voronoi diagram, three key indicators are extracted: Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate to comprehensively evaluate maize seedling quality. The results show that this method effectively extracts the VPUI, missing seedling rate, and repeated seedling rate of maize in the target area. Compared to the traditional plant spacing variation coefficient, VPUI performs better in representing seedling uniformity. Additionally, the R[sup.2] for the estimated missing seedling rate and replanting rate based on the Voronoi method were 0.773 and 0.940, respectively. Compared to using the plant spacing method, the R[sup.2] increased by 0.09 and 0.544, respectively. The maize seedling quality evaluation method proposed in this study provides technical support for precision maize planting management and is of great significance for improving agricultural production efficiency and reducing labor costs.
Audience	Academic
Author	Ren, Lipeng Xu, Bo Chen, Zhida Lin, Zhongyun Zhang, Chengjian Yang, Hao Zhao, Dan Yang, Guijun Li, Changchun Feng, Haikuan
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Cites_doi	10.3390/rs9020111 10.1007/s11119-020-09725-3 10.3906/tar-1210-53 10.13031/2013.24494 10.1016/j.measurement.2023.112764 10.1016/j.compag.2020.105766 10.1016/j.biosystemseng.2020.10.013 10.3390/rs15143671 10.34133/plantphenomics.0191 10.1117/1.2784799 10.3389/fmars.2023.1134418 10.2134/agronj2005.0336 10.1016/j.ecolmodel.2012.12.030 10.1016/j.cag.2019.06.007 10.1016/j.compag.2023.108045 10.1016/j.cad.2007.09.006 10.3390/rs15081979 10.3390/rs14153811 10.3390/rs15102530 10.3390/su151512021 10.1057/jors.2008.101 10.1186/s13007-019-0399-7 10.1016/S2095-3119(18)61917-3 10.3390/rs14194892 10.3390/agriculture14020175 10.3390/rs12060923 10.3389/fpls.2021.699085 10.1016/j.compag.2022.107008 10.1016/j.eja.2023.126845 10.1038/s41598-021-01044-5 10.1186/s12866-021-02180-8 10.3390/rs12244170 10.3390/rs12111764 10.3390/jmse8020096
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SubjectTerms	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
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Title	The Detection of Maize Seedling Quality from UAV Images Based on Deep Learning and Voronoi Diagram Algorithms
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