Quantitative Evaluation of Sugar Beet Plant Height Based on UAV-RGB and UAV-LiDAR

被引:0
|
作者
Wang Q. [1 ]
Che Y. [1 ]
Chai H. [1 ]
Shao K. [2 ]
Li B. [1 ]
Ma Y. [1 ]
机构
[1] College of Land Science and Technology, China Agricultural University, Beijing
[2] Agricultural Artificial Intelligence and Crop Phenotype Engineering Research Centre, Inner Mongolia Institute of Biotechnology, Huhhot
来源
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2021年 / 52卷 / 03期
关键词
Canopy distribution; Plant height; Sugar beet; Three-dimensional point cloud; UAV-LiDAR;
D O I
10.6041/j.issn.1000-1298.2021.03.019
中图分类号
学科分类号
摘要
Sugar beet is the world's main sugar production crop and one of the recognized alternative materials for biofuel production. Plant height of sugar beet can be used to estimate root biomass, indicate water stress, and can also be an effective indicator of nitrogen content and yield. It is an important parameter for breeders and farm managers to assess the growth status of sugar beet in the field. The rotary-wing UAV platform has the characteristics of vertical lifting, fixed-point hovering, and strong maneuverability. It is suitable for obtaining multi-scale, multi-repeat, fixed-point, and high-resolution farmland crop information. Totally 186 genotypes of sugar beet were chosen to explore accuracy difference of estimated plant height for UAV-RGB and UAV-LiDAR system, and to do comparison with the measured value. The correlation between estimated plant height by LiDAR and measured value (straight slope was 0.99, R2 was 0.88, rRMSE was 6.6%) was higher than that measured by RGB (straight slope was 0.94, R2 was 0.8, rRMSE was 9%). Further stratification analysis of point clouds was carried out to compare the difference of point clouds distribution in the canopy. For the later growth stage with relative dense canopy, UAV-LiDAR can reconstruct a more complete three-dimensional canopy structure than that of UAV-RGB system. © 2021, Chinese Society of Agricultural Machinery. All right reserved.
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页码:178 / 184
页数:6
相关论文
共 39 条
  • [21] WANG X, ZHANG R, SONG W, Et al., Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV), Scientific Reports, 9, 1, pp. 3458-3467, (2019)
  • [22] FENG A, ZHANG M, SUDDUTH K A, Et al., Cotton yield estimation from UAV-based plant height, Transactions of the ASABE, 62, 2, pp. 393-404, (2019)
  • [23] FRENCH A N, GORE M A, THOMPSON A., Cotton phenotyping with LiDAR from a track-mounted platform, SPIE Commercial + Scientific Sensing & Imaging, (2016)
  • [24] JIMENEZ-BERNI J A, DEERY D M, PABLO R L, Et al., High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR, Frontiers in Plant Science, 9, pp. 237-254, (2018)
  • [25] XU W, DALJIT S, SANDEEP M, Et al., Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies, Plant Methods, 14, 1, pp. 53-68, (2018)
  • [26] YUAN H, BENNETT R S, WANG N, Et al., Development of a peanut canopy measurement system using a ground-based LiDAR sensor, Frontiers in Plant Science, 10, pp. 203-215, (2019)
  • [27] ZHANG Junguo, YAN Hao, HU Chunhe, Et al., Application and future development of unmanned aerial vehicle in forestry, Journal of Forestry Engineering, 4, 1, pp. 8-16, (2019)
  • [28] LIU K, SHEN X, CAO L, Et al., Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations, ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp. 465-482, (2018)
  • [29] ASSENBAUM M., Monitoring coastal erosion with UAV LiDAR, GIM International, 32, 2, pp. 18-21, (2018)
  • [30] PU Shi, WU Xinqiao, YAN Zhengliang, Et al., Automatic recognition of defects on transmission lines from UAV-borne laser scanning data, Remote Sensing Information, 32, 4, pp. 52-57, (2017)