Innovative UAV LiDAR Generated Point-Cloud Processing Algorithm in Python for Unsupervised Detection and Analysis of Agricultural Field-Plots

Abstract

The estimation of plant growth is a challenging but key issue that may help us to understand crop vs. environment interactions. To perform precise and high-throughput analysis of plant growth in field conditions, remote sensing using LiDAR and unmanned aerial vehicles (UAV) has been developed, in addition to other approaches. Although there are software tools for the processing of LiDAR data in general, there are no specialized tools for the automatic extraction of experimental field blocks with crops that represent specific “points of interest”. Our tool aims to detect precisely individual field plots, small experimental plots (in our case 10 m2) which in agricultural research represent the treatment of a single plant or one genotype in a breeding trial. Cutting out points belonging to the specific field plots allows the user to measure automatically their growth characteristics, such as plant height or plot biomass. For this purpose, new method of edge detection was combined with Fourier transformation to find individual field plots. In our case study with winter wheat, two UAV flight levels (20 and 40 m above ground) and two canopy surface modelling methods (raw points and B-spline) were tested. At a flight level of 20 m, our algorithm reached a 0.78 to 0.79 correlation with LiDAR measurement with manual validation (RMSE = 0.19) for both methods. The algorithm, in the Python 3 programming language, is designed as open-source and is freely available publicly, including the latest updates.

Plant phenotyping relevance

UAV-LiDAR点群から実験区画を自動抽出し、植物高や区画バイオマスなどの生育形質を測定するPythonアルゴリズムとツールの開発・検証が中心である。

Code and data availability

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