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Remote Sensing-Based Estimation of Seedling Density in Nursery Gardens Using YOLOv4 Deep Learning Algorithm
Y. Wang, J. Sun, F. Wang and D. Li
2023
发表期刊Traitement du Signal
ISSN07650019
卷号40期号:4页码:1533-1541
摘要In nursery gardens, seedlings are traditionally densely planted, leading to large errors and questionable accuracy when employing standard sampling and probability statistical methods. Such conventional methods also prove labor-intensive. To address these challenges, a patrol platform equipped with a drone-mounted image acquisition system was developed. Remote sensing images, sourced from a nursery garden situated in the Linjiang Forestry Bureau of Jilin Province, China, served as the primary dataset. By leveraging the deep learning-based target detection capabilities of the YOLOv4 algorithm, seedlings within the nursery garden were meticulously surveyed, delineated, and enumerated. For the statistical evaluation of Pinus Koraiensis (Korean pine) seedlings, a precision of 91.85% was achieved using the YOLOv4 algorithm. Results suggest a notable robustness of the model in standard environments. Compared to traditional quadrat sampling and detection approaches, the methodology introduced here offers an intelligent, efficient, and precise mapping strategy for large-scale seedling surveys. © 2023 Lavoisier. All rights reserved.
DOI10.18280/ts.400421
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收录类别sci ; ei
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文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/67992
专题中国科学院长春光学精密机械与物理研究所
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Y. Wang, J. Sun, F. Wang and D. Li. Remote Sensing-Based Estimation of Seedling Density in Nursery Gardens Using YOLOv4 Deep Learning Algorithm[J]. Traitement du Signal,2023,40(4):1533-1541.
APA Y. Wang, J. Sun, F. Wang and D. Li.(2023).Remote Sensing-Based Estimation of Seedling Density in Nursery Gardens Using YOLOv4 Deep Learning Algorithm.Traitement du Signal,40(4),1533-1541.
MLA Y. Wang, J. Sun, F. Wang and D. Li."Remote Sensing-Based Estimation of Seedling Density in Nursery Gardens Using YOLOv4 Deep Learning Algorithm".Traitement du Signal 40.4(2023):1533-1541.
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