Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping

doi:10.1007/s11119-021-09811-0

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping
	W. X. Zhu; Z. G. Sun; Y. H. Huang; T. Yang; J. Li; K. Y. Zhu; J. Q. Zhang; B. Yang; C. X. Shao; J. B. Peng
刊名	Precision Agriculture
	2021
卷号	22 期号:6 页码:1768-1802
ISSN号	1385-2256
DOI	10.1007/s11119-021-09811-0
英文摘要	Unmanned aerial vehicle (UAV) system is an emerging remote sensing tool for profiling crop phenotypic characteristics, as it distinctly captures crop real-time information on field scales. For optimizing UAV agro-monitoring schemes, this study investigated the performance of single-source and multi-source UAV data on maize phenotyping (leaf area index, above-ground biomass, crop height, leaf chlorophyll concentration, and plant moisture content). Four UAV systems [i.e., hyperspectral, thermal, RGB, and Light Detection and Ranging (LiDAR)] were used to conduct flight missions above two long-term experimental fields involving multi-level treatments of fertilization and irrigation. For reducing the effects of algorithm characteristics on maize parameter estimation and ensuring the reliability of estimates, multi-variable linear regression, backpropagation neural network, random forest, and support vector machine were used for modeling. Highly correlated UAV variables were filtered, and optimal UAV inputs were determined using a recursive feature elimination procedure. Major conclusions are (1) for single-source UAV data, LiDAR and RGB texture were suitable for leaf area index, above-ground biomass, and crop height estimation; hyperspectral outperformed on leaf chlorophyll concentration estimation; thermal worked for plant moisture content estimation; (2) model performance was slightly boosted via the fusion of multi-source UAV datasets regarding leaf area index, above-ground biomass, and crop height estimation, while single-source thermal and hyperspectral data outperformed multi-source data for the estimation of plant moisture and leaf chlorophyll concentration, respectively; (3) the optimal UAV scheme for leaf area index, above-ground biomass, and crop height estimation was LiDAR + RGB + hyperspectral, while considering practical agro-applications, optical Structure from Motion + customer-defined multispectral system was recommended owing to its cost-effectiveness. This study contributes to the optimization of UAV agro-monitoring schemes designed for field-scale crop phenotyping and further extends the applications of UAV technologies in precision agriculture.
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内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/65505]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	W. X. Zhu,Z. G. Sun,Y. H. Huang,et al. Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping[J]. Precision Agriculture,2021,22(6):1768-1802.
APA	W. X. Zhu.,Z. G. Sun.,Y. H. Huang.,T. Yang.,J. Li.,...&H. L. Hu and X. H. Liao.(2021).Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping.Precision Agriculture,22(6),1768-1802.
MLA	W. X. Zhu,et al."Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping".Precision Agriculture 22.6(2021):1768-1802.