The Detection of Maize Seedling Quality from UAV Images Based on Deep Learning and Voronoi Diagram Algorithms
文献类型： 外文期刊
作者： Ren, Lipeng 1 ; Li, Changchun 1 ; Yang, Guijun 2 ; Zhao, Dan 2 ; Zhang, Chengjian 2 ; Xu, Bo 2 ; Feng, Haikuan 2 ; Chen, Zhida 2 ; Lin, Zhongyun 1 ; Yang, Hao 2 ;
作者机构： 1.Henan Polytech Univ, Res Inst Quantitat Remote Sensing & Smart Agr, Sch Surveying & Mapping Land Informat Engn, Jiaozuo 454000, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
关键词： unmanned aerial vehicles (UAVs); deep learning; Voronoi polygons; plant detection; maize emergence; emergence uniformity; missing seedling rate; repeated seeding rate
期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）
ISSN：
年卷期： 2024 年 16 卷 19 期
页码：
收录情况： SCI
摘要： 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.
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