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Canopy height uniformity: a new 3D phenotypic indicator linking individual plant to canopy

文献类型： 外文期刊

作者： Chang, Wushuai 1 ; Wen, Weiliang 2 ; Gu, Shenghao 2 ; Li, Yinglun 2 ; Fan, Jiangchuan 2 ; Lu, Xianju 2 ; Chen, Bo 3 ; Xu, Tianjun 4 ; Wang, Ronghuan 4 ; Guo, Xinyu 2 ; Li, Ruiqi 1 ;

作者机构： 1.Hebei Agr Univ, Coll Agron, Baoding 071001, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Maize Res Inst, Beijing 100097, Peoples R China

关键词： UAV; Lidar; Plant height; Canopy height uniformity; Above ground biomass

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2024 年 227 卷

页码：

收录情况： SCI

摘要： Canopy height uniformity (CHU) is a key indicator linking individual plants to populations. Determining CHU by the manual measurement of the height of individual plants is inefficient and subjective, making meeting the demand for a high-throughput assessment of CHU in high-yield crop management and variety selection challenging. Therefore, a high-throughput CHU estimation approach using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data is proposed. First, individual plant point clouds were segmented by incorporating planting density, and the CHU was estimated based on the extracted plant heights (PHs). The CHU was then applied to quantify the effects of different cropping practices on maize canopy structure. In addition, 18 canopy structural parameters (SPs) were extracted from the canopy point cloud, and aboveground biomass (AGB) was estimated by combining these SPs with Pelican Optimization Algorithm (POA) machine learning models (POA-MLs). Finally, as an indicator of individual variability within the canopy, CHU was integrated into the training process to evaluate the accuracy of AGB estimation for different models and datasets. The results showed that the plant height extracted by the canopy population-individual plant segmentation was accurate, with R2 ranging from 0.85 to 0.93. The CHU was able to accurately quantify the effects of different cropping practices on canopy structure. An increase in applied nitrogen fertilizer and irrigation could significantly contribute to an increase in CHU and the formation of a clean and homogeneous canopy structure. Meanwhile, marginal effects can be accurately quantified through PHs estimation to further quantify the intra-canopy differences. In addition, the accuracy of the AGB estimation can be effectively improved by merging SPs, PH, and CHU. In this study, we demonstrated the efficacy of CHU as a phenotypic indicator for representing differences in canopy structure, thus providing practical phenotypic identification information for breeders and field managers.

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