Using UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Crops
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140808" target="_blank" >RIV/00216305:26220/21:PU140808 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2072-4292/13/10/1878" target="_blank" >https://www.mdpi.com/2072-4292/13/10/1878</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/rs13101878" target="_blank" >10.3390/rs13101878</a>
Alternative languages
Result language
angličtina
Original language name
Using UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Crops
Original language description
The optimum corn harvest time differs between individual harvest scenarios, depending on the intended use of the crop and on the technical equipment of the actual farm. It is therefore economically significant to specify the period as precisely as possible. The harvest maturity of silage corn is currently determined from the targeted sampling of plants cultivated over large areas. In this context, the paper presents an alternative, more detail-oriented approach for estimating the correct harvest time; the method focuses on the relationship between the ripeness data obtained via photogrammetry and the parameters produced by the chemical analysis of corn. The relevant imaging methodology utilizing a spectral camera-equipped unmanned aerial vehicle (UAV) allows the user to acquire the spectral reflectance values and to compute the vegetation indices. Furthermore, the authors discuss the statistical data analysis centered on both the nutritional values found in the laboratory corn samples and on the information obtained from the multispectral images. This discussion is associated with a detailed insight into the computation of correlation coefficients. Statistically significant linear relationships between the vegetation indices, the normalized difference red edge index (NDRE) and the normalized difference vegetation index (NDVI) in particular, and nutritional values such as dry matter, starch, and crude protein are evaluated to indicate different aspects of and paths toward predicting the optimum harvest time. The results are discussed in terms of the actual limitations of the method, the benefits for agricultural practice, and planned research.
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
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
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
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Volume of the periodical
13
Issue of the periodical within the volume
10
Country of publishing house
CH - SWITZERLAND
Number of pages
19
Pages from-to
1-20
UT code for WoS article
000662694600001
EID of the result in the Scopus database
2-s2.0-85106500641