Unverified paper record
Comparative Analysis of TLS and UAV Sensors for Estimation of Grapevine Geometric Parameters.
Sensors (Basel, Switzerland) · 11 Aug 2024 · 10.3390/s24165183
Abstract
Understanding geometric and biophysical characteristics is essential for determining grapevine vigor and improving input management and automation in viticulture. This study compares point cloud data obtained from a Terrestrial Laser Scanner (TLS) and various UAV sensors including multispectral, panchromatic, Thermal Infrared (TIR), RGB, and LiDAR data, to estimate geometric parameters of grapevines. Descriptive statistics, linear correlations, significance using the F-test of overall significance, and box plots were used for analysis. The results indicate that 3D point clouds from these sensors can accurately estimate maximum grapevine height, projected area, and volume, though with varying degrees of accuracy. The TLS data showed the highest correlation with grapevine height ( r = 0.95, p R 2 = 0.90; RMSE = 0.027 m), while point cloud data from panchromatic, RGB, and multispectral sensors also performed well, closely matching TLS and measured values ( r > 0.83, p R 2 > 0.70; RMSE r = 0.76, p R 2 = 0.58; RMSE = 0.147 m) and projected area ( r = 0.82, p R 2 = 0.66; RMSE = 0.165 m). The greater variability observed in projected area and volume from UAV sensors is related to the low point density associated with spatial resolution. These findings are valuable for both researchers and winegrowers, as they support the optimization of TLS and UAV sensors for precision viticulture, providing a basis for further research and helping farmers select appropriate technologies for crop monitoring.
Plant phenotyping relevance
TLSおよびUAVセンサーによるブドウ樹の高さ・投影面積・体積推定を比較検証しており、植物形質の取得手法の技術評価が中心である。
abstractThis study compares point cloud data obtained from a Terrestrial Laser Scanner (TLS) and various UAV sensors including multispectral, panchromatic, Thermal Infrared (TIR), RGB, and LiDAR data, to estimate geometric parameters of grapevines.
abstractThe results indicate that 3D point clouds from these sensors can accurately estimate maximum grapevine height, projected area, and volume, though with varying degrees of accuracy.
Code and data availability
The paper's TLS/UAV point cloud data and grapevine geometric measurements are not publicly deposited; the Data Availability Statement states they are available only upon reasonable request. No author analysis code or trained models with a public URL are mentioned. The only URLs in the article are the license, funding-
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