Research on visualization of cotton canopy structure and extraction of feature parameters based on dual-perspective point cloud data

透视图(图形) 点云 天蓬 计算机科学 可视化 萃取(化学) 对偶(语法数字) 云计算 点(几何) 数据挖掘 特征(语言学) 特征提取 遥感 环境科学 人工智能 数学 地质学 地理 几何学 化学 文学类 哲学 艺术 操作系统 考古 色谱法 语言学
作者
Yongjian Hu,Sheng Wen,Lei Zhang,Yubin Lan,Xiaoshuai Chen
出处
期刊:International Journal of Remote Sensing [Taylor & Francis]
卷期号:45 (17): 6043-6076
标识
DOI:10.1080/01431161.2024.2384099
摘要

Cotton is one of the crops that requires the most time and labor. Precision agriculture technology is required for efficient management of cotton, and the identification of cotton attribute information in the field is a necessary and crucial step towards implementing precision agriculture. Unmanned aerial vehicles (UAVs) and Light Detection and Ranging (LiDAR) have evolved into essential instruments for plant phenotyping research. In this study, in order to address the demand for cotton attribute identification over wide areas in the field, an airborne LiDAR system was built based on LiDAR detection technology. This work acquired a dual-view point cloud of a cotton field in order to address the high density and low accuracy of the cotton point cloud attributes. Following pre-processing of the data, the point cloud was first coarsely regenerated using a combination of Fast Point Feature Histograms (FPFH) and Intrinsic Shape Signatures (ISS) techniques. The dual-view point cloud registration was then refined and finished using an Iterative Closest Point (ICP) algorithm. The height of the cotton plant was determined using the reconstructed point cloud of the cotton canopy, and a method combining Graham's algorithm and the Alpha-Shape algorithm was suggested to determine the porosity of the cotton layers. The findings revealed that the root mean square errors (RMSE) between calculated and measured values of cotton plant height and stratified porosity were, respectively, 3.98 cm and 5.21%, and that their mean absolute percentage errors (MAPE) were 4.39% and 9.31%, with correlation coefficients (R2) of 0.951 and 0.762, respectively. On the whole, our study has demonstrated the effectiveness of the proposed method in terms of providing accurate and reliable cotton parameters in agriculture.
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