Comparative Evaluation of the Performance of the PTD and CSF Algorithms on UAV LiDAR Data for Dynamic Canopy Height Modeling in Densely Planted Cotton
文献类型： 外文期刊
作者： Yang, Weiguang 1 ; Wu, Jinhao 1 ; Xu, Weicheng 4 ; Li, Hong 2 ; Li, Xi 2 ; Lan, Yubin 1 ; Li, Yuanhong 1 ; Zhang, Lei 2 ;
作者机构： 1.South China Agr Univ, Coll Elect Engn, Coll Artificial Intelligence, Guangzhou 510642, Peoples R China
2.Guangdong Lab Lingnan Modern Agr, Guangzhou 510642, Peoples R China
3.Natl Ctr Int Collaborat Res Precis Agr Aviat Pesti, Guangzhou 510642, Peoples R China
4.Guangdong Acad Agr Sci, Guangdong Key Lab New Technol Rice Breeding, Rice Res Inst, Guangzhou 510640, Peoples R China
5.South China Agr Univ, Coll Agr, Guangzhou 510642, Peoples R China
关键词： LiDAR; dynamic height growth; densely planted cotton
期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）
ISSN：
年卷期： 2024 年 14 卷 4 期
页码：
收录情况： SCI
摘要： This study introduces a novel methodology for the dynamic extraction of information on cotton growth in terms of height utilizing the DJI Zenmuse L1 LiDAR sensor mounted onto a DJI Matrice 300 RTK Unmanned Aerial Vehicle (UAV), aimed at enhancing the precision and efficiency of growth monitoring within the realm of precision agriculture. Employing the Progressive TIN Densification (PTD) and Cloth Simulation Filter (CSF) algorithms, combined with Kriging interpolation, we generated Canopy Height Models (CHMs) to extract the cotton heights at two key agricultural sites: Zengcheng and Tumxuk. Our analysis reveals that the PTD algorithm significantly outperforms the CSF method in terms of accuracy, with its R2 values indicating a superior model fit for height extraction across different growth stages (Zengcheng: 0.71, Tumxuk: 0.82). Through meticulous data processing and cluster analysis, this study not only identifies the most effective algorithm for accurate height extraction but also provides detailed insights into the dynamic growth patterns of cotton varieties across different geographical regions. The findings highlight the critical role of UAV remote sensing in enabling large-scale, high-precision monitoring of crop growth, which is essential for the optimization of agricultural practices such as precision fertilization and irrigation. Furthermore, the study demonstrates the potential of UAV technology to select superior cotton varieties by analyzing their growth dynamics, offering valuable guidance for cotton breeding and cultivation.
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