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Innovative UAV Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 4104

Special Issue Editors


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Guest Editor
Department of Theoretical and Experimental Electrical Engineering, Brno University of Technology, Technická 12, 616 00 Brno, Czech Republic
Interests: numerical modeling; coupled model; sensing, measurement method; low-level measurement; electromagnetic field; photonics; noise spectroscopy; IR measurement
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Theoretical and Experimental Electrical Engineering, Brno University of Technology, Technická 12, 616 00 Brno, Czech Republic
Interests: measurement; MRI/NQR; spectroscopy; image processing; multispectral imaging; UAV; drones
Special Issues, Collections and Topics in MDPI journals
Department of Theoretical and Experimental Electrical Engineering, Brno University of Technology, Technická 12, 616 00 Brno, Czech Republic
Interests: measurement; image processing; electron microscop; numerical modeling; experiments
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Theoretical and Experimental Electrical Engineering, Brno University of Technology, Technická 12, 616 00 Brno, Czech Republic
Interests: measurement; image processing; multispectral imaging; UAV; drones; signal processing; low-level measurement; experiments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision agriculture is an internationally recognized concept and term referring to land cultivation by means of nontraditional technologies that were first designed and developed at the end of the 1980s.

The aim of the concept rests in adjusting cultivation procedures to suit local conditions, the main principle being to perform the crop-growing tasks at the right place, intensity, and time.

The optimal crop 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. There is a scientific space which uses a more detail-oriented approach for estimating the correct harvest time; the method focuses on the relationship between ripeness data obtained via photogrammetry and parameters produced by the chemical analysis of the crop. 

A corresponding imaging methodology using an unmanned aerial vehicle (UAV) equipped with a spectral camera allows spectral reflectance values to be obtained and vegetation indices to be calculated.

The topics of interest for this Special Issue include but are not limited to:

  • Image processing theory;
  • Experimental measurement and validation in the laboratory or in situ;
  • Sensing and signal processing of electrical and non-electrical quantities (e.g., images);
  • Methods of identifying the type, species and parts of melts, organisms or micro-organisms (crop diseases);
  • Mathematical methods and procedures to achieve the above main objectives.

Prof. Dr. Pavel Fiala
Prof. Dr. Petr Marcoň
Dr. Jiri Maxa
Dr. Jiri Janousek
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV
  • measurement
  • data transfer
  • image evaluation
  • image processing
  • multispectral imaging
  • sensing techniques

Published Papers (2 papers)

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Research

19 pages, 4003 KiB  
Article
Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging
by Jiří Janoušek, Petr Marcoň, Přemysl Dohnal, Václav Jambor, Hana Synková and Petr Raichl
Remote Sens. 2023, 15(12), 3152; https://doi.org/10.3390/rs15123152 - 16 Jun 2023
Cited by 1 | Viewed by 1426
Abstract
Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. [...] Read more.
Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic. Full article
(This article belongs to the Special Issue Innovative UAV Applications)
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20 pages, 10683 KiB  
Article
Using UAV to Identify the Optimal Vegetation Index for Yield Prediction of Oil Seed Rape (Brassica napus L.) at the Flowering Stage
by Vojtěch Lukas, Igor Huňady, Antonín Kintl, Jiří Mezera, Tereza Hammerschmiedt, Julie Sobotková, Martin Brtnický and Jakub Elbl
Remote Sens. 2022, 14(19), 4953; https://doi.org/10.3390/rs14194953 - 4 Oct 2022
Cited by 12 | Viewed by 1955
Abstract
Suitability of the vegetation indices of normalized difference vegetation index (NDVI), blue normalized difference vegetation index (BNDVI), and normalized difference yellowness index (NDYI) obtained by means of UAV at the flowering stage of oil seed rape for the prediction of seed yield and [...] Read more.
Suitability of the vegetation indices of normalized difference vegetation index (NDVI), blue normalized difference vegetation index (BNDVI), and normalized difference yellowness index (NDYI) obtained by means of UAV at the flowering stage of oil seed rape for the prediction of seed yield and usability of these vegetation indices in the identification of anomalies in the condition of the flowering growth were verified based on the regression analysis. Correlation analysis was performed to find the degree of yield dependence on the values of NDVI, BNDVI, and NDYI indices, which revealed a strong, significant linear positive dependence of seed yield on BNDVI (R = 0.98) and NDYI (R = 0.95). The level of correlation between the NDVI index and the seed yield was weaker (R = 0.70) than the others. Regression analysis was performed for a closer determination of the functional dependence of NDVI, BNDVI, and NDYI indices and the yield of seeds. Coefficients of determination in the linear regression model of NDVI, BNDVI, and NDYI indices reached the following values: R2 = 0.48 (NDVI), R2 = 0.95 (BNDVI), and R2 = 0.90 (NDYI). Thus, it was shown that increased density of yellow flowers decreased the relationship between NDVI and crop yield. The NDVI index is not appropriate for assessing growth conditions and prediction of yields at the flowering stage of oil seed rape. High accuracy of yield prediction was achieved with the use of BNDVI and NDYI. The performed analysis of NDVI, BNDVI, and NDYI demonstrated that particularly the BNDVI and NDYI indices can be used to identify problems in the development of oil seed rape growth at the stage of flowering, for their precise localization, and hence to targeted and effective remedial measures in line with the principles of precision agriculture. Full article
(This article belongs to the Special Issue Innovative UAV Applications)
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