Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F24%3A73627631" target="_blank" >RIV/61989592:15310/24:73627631 - isvavai.cz</a>
Result on the web
<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297153" target="_blank" >https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297153</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1371/journal.pone.0297153" target="_blank" >10.1371/journal.pone.0297153</a>
Alternative languages
Result language
angličtina
Original language name
Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA
Original language description
Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest–the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use.
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
40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)
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
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PLoS One
ISSN
1932-6203
e-ISSN
1932-6203
Volume of the periodical
19
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
"e029715-1"-"e029715-11 "
UT code for WoS article
001174325200046
EID of the result in the Scopus database
2-s2.0-85182754508