Maize Ear Height and Ear-Plant Height Ratio Estimation with LiDAR Data and Vertical Leaf Area Profile
文献类型： 外文期刊
作者： Wang, Han 1 ; Zhang, Wangfei 2 ; Yang, Guijun 1 ; Lei, Lei 1 ; Han, Shaoyu 1 ; Xu, Weimeng 1 ; Chen, Riqiang 1 ; Zhang, Chengjian 1 ; Yang, Hao 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
2.Southwest Forestry Univ, Coll Forestry, Kunming 650224, Peoples R China
3.Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China
关键词： ear height; ear-plant height ratio; TLS LiDAR; DLS LiDAR
期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）
ISSN：
年卷期： 2023 年 15 卷 4 期
页码：
收录情况： SCI
摘要： Ear height (EH) and ear-plant height ratio (ER) are important agronomic traits in maize that directly affect nutrient utilization efficiency and lodging resistance and ultimately relate to maize yield. However, challenges in executing large-scale EH and ER measurements severely limit maize breeding programs. In this paper, we propose a novel, simple method for field monitoring of EH and ER based on the relationship between ear position and vertical leaf area profile. The vertical leaf area profile was estimated from Terrestrial Laser Scanner (TLS) and Drone Laser Scanner (DLS) data by applying the voxel-based point cloud method. The method was validated using two years of data collected from 128 field plots. The main factors affecting the accuracy were investigated, including the LiDAR platform, voxel size, and point cloud density. The EH using TLS data yielded R-2 = 0.59 and RMSE = 16.90 cm for 2019, R-2 = 0.39 and RMSE = 18.40 cm for 2021. In contrast, the EH using DLS data had an R-2 = 0.54 and RMSE = 18.00 cm for 2019, R-2 = 0.46 and RMSE = 26.50 cm for 2021 when the planting density was 67,500 plants/ha and below. The ER estimated using 2019 TLS data has R-2 = 0.45 and RMSE = 0.06. In summary, this paper proposed a simple method for measuring maize EH and ER in the field, the results will also offer insights into the structure-related traits of maize cultivars, further aiding selection in molecular breeding.
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