Datum
2020-10-19Metadata
Zur Langanzeige
Aufsatz
Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data
Zusammenfassung
Remote sensing (RS) has been an e ective tool to monitor agricultural production systems, but for vegetable crops, precision agriculture has received less interest to date. The objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage). The study was conducted in an experimental layout in Bengaluru, India, at five dates in summer 2017. The prediction accuracy varied strongly depending on the RS dataset used. For all crops, a good predictive performance with cross-validated prediction error < 10% was achieved. The growth stage of the crops had no significant e ect on the prediction accuracy, although increasing trends of an underestimation of FMY with later sampling dates for eggplant and tomato were found. The study proves that an estimation of vegetable FMY using RS data is successful throughout the growing season. Di erent RS datasets were best for biomass prediction of the three vegetables, indicating that multi-sensory data collection should be preferred to single sensor use, as no one sensor system is superior.
Zitierform
In: Agronomy Volume 10 / Issue 10 (2020-10-19) EISSN 2073-4395Förderhinweis
Gefördert durch den Publikationsfonds der Universität KasselZitieren
@article{doi:10.17170/kobra-202011202229,
author={Astor, Thomas and Dayananda, Supriya and Nautiyal, Sunil and Wachendorf, Michael},
title={Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data},
journal={Agronomy},
year={2020}
}
0500 Oax 0501 Text $btxt$2rdacontent 0502 Computermedien $bc$2rdacarrier 1100 2020$n2020 1500 1/eng 2050 ##0##http://hdl.handle.net/123456789/12007 3000 Astor, Thomas 3010 Dayananda, Supriya 3010 Nautiyal, Sunil 3010 Wachendorf, Michael 4000 Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data / Astor, Thomas 4030 4060 Online-Ressource 4085 ##0##=u http://nbn-resolving.de/http://hdl.handle.net/123456789/12007=x R 4204 \$dAufsatz 4170 5550 {{Fernerkundung}} 5550 {{Cloud Computing}} 5550 {{Biomasse}} 7136 ##0##http://hdl.handle.net/123456789/12007
2020-11-20T11:53:54Z 2020-11-20T11:53:54Z 2020-10-19 doi:10.17170/kobra-202011202229 http://hdl.handle.net/123456789/12007 Gefördert durch den Publikationsfonds der Universität Kassel eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ multi-source data combination vegetable biomass hyperspectral point cloud analysis 004 540 Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data Aufsatz Remote sensing (RS) has been an e ective tool to monitor agricultural production systems, but for vegetable crops, precision agriculture has received less interest to date. The objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage). The study was conducted in an experimental layout in Bengaluru, India, at five dates in summer 2017. The prediction accuracy varied strongly depending on the RS dataset used. For all crops, a good predictive performance with cross-validated prediction error < 10% was achieved. The growth stage of the crops had no significant e ect on the prediction accuracy, although increasing trends of an underestimation of FMY with later sampling dates for eggplant and tomato were found. The study proves that an estimation of vegetable FMY using RS data is successful throughout the growing season. Di erent RS datasets were best for biomass prediction of the three vegetables, indicating that multi-sensory data collection should be preferred to single sensor use, as no one sensor system is superior. open access Astor, Thomas Dayananda, Supriya Nautiyal, Sunil Wachendorf, Michael doi:10.3390/agronomy10101600 Fernerkundung Cloud Computing Biomasse publishedVersion EISSN 2073-4395 Issue 10 Agronomy Volume 10 false 1600
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