Innovative UAV LiDAR Generated Point-Cloud Processing Algorithm in Python for Unsupervised Detection and Analysis of Agricultural Field-Plots
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389030%3A_____%2F21%3A00551116" target="_blank" >RIV/61389030:_____/21:00551116 - isvavai.cz</a>
Alternative codes found
RIV/61989592:15310/21:73610712
Result on the web
<a href="http://doi.org/10.3390/rs13163169" target="_blank" >http://doi.org/10.3390/rs13163169</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/rs13163169" target="_blank" >10.3390/rs13163169</a>
Alternative languages
Result language
angličtina
Original language name
Innovative UAV LiDAR Generated Point-Cloud Processing Algorithm in Python for Unsupervised Detection and Analysis of Agricultural Field-Plots
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10608 - Biochemistry and molecular biology
Result continuities
Project
<a href="/en/project/EF16_019%2F0000827" target="_blank" >EF16_019/0000827: Plants as a tool for sustainable global development</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Remote Sensing
ISSN
2072-4292
e-ISSN
2072-4292
Volume of the periodical
13
Issue of the periodical within the volume
16
Country of publishing house
CH - SWITZERLAND
Number of pages
23
Pages from-to
3169
UT code for WoS article
000689861600001
EID of the result in the Scopus database
2-s2.0-85113671926