文献类型： 外文期刊

第一作者： Lei, Lei

作者： Lei, Lei;Lei, Lei;Li, Zhenhong;Li, Zhenhong;Yang, Hao;Xu, Bo;Yang, Guijun;Hoey, Trevor B.;Wu, Jintao;Yang, Xiaodong;Feng, Haikuan;Yang, Guijun;Yang, Guijun

作者机构： Changan Univ, Coll Geol Engn & Geomat, Key Lab Loess, Xian 710054, Peoples R China;Changan Univ, Big Data Ctr Geosci & Satellites BDCGS, Xian 710054, Peoples R China;Changan Univ, Coll Geol Engn & Geomat, Big Data Ctr Geosci & Satellites BDCGS, Minist Educ,Key Lab Loess,Key Lab Western Chinas M, Xian 710054, Peoples R China;Changan Univ, Minist Nat Resources, Key Lab Ecol Geol & Disaster Prevent, Xian 710054, Peoples R China;Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China;Brunel Univ London, Dept Civil & Environm Engn, London UB8 3PH, England;Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China;Minist Agr & Rural Affairs, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China;Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China

关键词： Vegetation mapping; Laser radar; Point cloud compression; Feature extraction; Agriculture; Data models; Data mining; Different cultivars; different growth stages; different planting densities; different platforms; light detection and ranging (LiDAR) data; simulated datasets; two-stage leaf-stem separation model

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 2023影响因子：7.5； 五年影响因子：7.6 ）

ISSN： 0196-2892

年卷期： 2024 年 62 卷

页码：

收录情况： SCI

摘要： The accurate and high-throughput extraction of phenotypic traits is of great significance for crop breeding and growth monitoring. The segmentation of structural components (e.g., leaves and stems) is a prerequisite for extracting phenotypic traits. In the past decade, there has been an increase in methods attempting to separate leaves and stems in point clouds. However, previous researches mainly focus on plants at the individual level due to the interlocked and overlapped nature of leaves and the bottleneck existing for field plants to extract phenotypic traits. To address this issue, a novel two-stage leaf-stem separation model encompassing the initial separation of leaves and stems and optimization is presented in this article. The model is based on the different geometric features of leaves and stems of maize plants defined by neighborhood points, and a cylinder is used to find the neighborhood points by considering the elongated characteristic of maize stems. After that, another elongated cylinder (0.5 m high and 0.02 m diameter) is used to traverse the stem points to optimize the initially separated results. Maize plants with the planting density of 45 000 plants/ha in the filling stage (Exp. 2019) were used to train and test the model in the initial separation step (Experiment 1), showing that the separation accuracy (SA) could be up to 91.3%. It was concluded that a 0.11-m-high and 0.07-m diameter cylinder was the optimal searching parameter for the initial separation and 0.25 m was the optimal threshold for optimization. We also tested the transferability of the model (Experiment 2) for maize plants with different planting densities (45 000, 67 500, 90 000, and 105 000 plants/ha), different growth stages (jointing, silking and filling), and point clouds collected using multiple platforms [terrestrial laser scanning (TLS), light detection and ranging (LiDAR) Backpack (LiBackpack), and unmanned aerial vehicle-LiDAR (UAV-LiDAR)], suggesting that the model performed well for all the datasets. In addition, the simulated datasets of maize with different planting densities were used to assess the model performance at the point level, showing the SA values were 0.92, 0.91, 0.91, and 0.90 for maize with the planting densities of 45 000, 67 500, 90 000, and 105 000 plants/ha, respectively. The proposed model in this study is innovative, and it has promising prospects for the high-throughput extraction of the phenotypic traits in field maize plants and could facilitate genotype selection in crop breeding and 3-D plant modeling.

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