UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations

The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-ry...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Javier Plaza [verfasserIn] Marco Criado [verfasserIn] Nilda Sánchez [verfasserIn] Rodrigo Pérez-Sánchez [verfasserIn] Carlos Palacios [verfasserIn] Francisco Charfolé [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	crop biophysical variables drone forage association PLS vegetation indices

Übergeordnetes Werk:	In: Agronomy - MDPI AG, 2012, 11(2021), 9, p 1697
Übergeordnetes Werk:	volume:11 ; year:2021 ; number:9, p 1697

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agronomy11091697

Katalog-ID:	DOAJ059252057

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520			\|a The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters.
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allfields	10.3390/agronomy11091697 doi (DE-627)DOAJ059252057 (DE-599)DOAJ4987e4960e5c4de88f0780821ca564e3 DE-627 ger DE-627 rakwb eng Javier Plaza verfasserin aut UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters. crop biophysical variables drone forage association PLS vegetation indices Agriculture S Marco Criado verfasserin aut Nilda Sánchez verfasserin aut Rodrigo Pérez-Sánchez verfasserin aut Carlos Palacios verfasserin aut Francisco Charfolé verfasserin aut In Agronomy MDPI AG, 2012 11(2021), 9, p 1697 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:11 year:2021 number:9, p 1697 https://doi.org/10.3390/agronomy11091697 kostenfrei https://doaj.org/article/4987e4960e5c4de88f0780821ca564e3 kostenfrei https://www.mdpi.com/2073-4395/11/9/1697 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 9, p 1697
spelling	10.3390/agronomy11091697 doi (DE-627)DOAJ059252057 (DE-599)DOAJ4987e4960e5c4de88f0780821ca564e3 DE-627 ger DE-627 rakwb eng Javier Plaza verfasserin aut UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters. crop biophysical variables drone forage association PLS vegetation indices Agriculture S Marco Criado verfasserin aut Nilda Sánchez verfasserin aut Rodrigo Pérez-Sánchez verfasserin aut Carlos Palacios verfasserin aut Francisco Charfolé verfasserin aut In Agronomy MDPI AG, 2012 11(2021), 9, p 1697 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:11 year:2021 number:9, p 1697 https://doi.org/10.3390/agronomy11091697 kostenfrei https://doaj.org/article/4987e4960e5c4de88f0780821ca564e3 kostenfrei https://www.mdpi.com/2073-4395/11/9/1697 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 9, p 1697
allfields_unstemmed	10.3390/agronomy11091697 doi (DE-627)DOAJ059252057 (DE-599)DOAJ4987e4960e5c4de88f0780821ca564e3 DE-627 ger DE-627 rakwb eng Javier Plaza verfasserin aut UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters. crop biophysical variables drone forage association PLS vegetation indices Agriculture S Marco Criado verfasserin aut Nilda Sánchez verfasserin aut Rodrigo Pérez-Sánchez verfasserin aut Carlos Palacios verfasserin aut Francisco Charfolé verfasserin aut In Agronomy MDPI AG, 2012 11(2021), 9, p 1697 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:11 year:2021 number:9, p 1697 https://doi.org/10.3390/agronomy11091697 kostenfrei https://doaj.org/article/4987e4960e5c4de88f0780821ca564e3 kostenfrei https://www.mdpi.com/2073-4395/11/9/1697 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 9, p 1697
allfieldsGer	10.3390/agronomy11091697 doi (DE-627)DOAJ059252057 (DE-599)DOAJ4987e4960e5c4de88f0780821ca564e3 DE-627 ger DE-627 rakwb eng Javier Plaza verfasserin aut UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters. crop biophysical variables drone forage association PLS vegetation indices Agriculture S Marco Criado verfasserin aut Nilda Sánchez verfasserin aut Rodrigo Pérez-Sánchez verfasserin aut Carlos Palacios verfasserin aut Francisco Charfolé verfasserin aut In Agronomy MDPI AG, 2012 11(2021), 9, p 1697 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:11 year:2021 number:9, p 1697 https://doi.org/10.3390/agronomy11091697 kostenfrei https://doaj.org/article/4987e4960e5c4de88f0780821ca564e3 kostenfrei https://www.mdpi.com/2073-4395/11/9/1697 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 9, p 1697
allfieldsSound	10.3390/agronomy11091697 doi (DE-627)DOAJ059252057 (DE-599)DOAJ4987e4960e5c4de88f0780821ca564e3 DE-627 ger DE-627 rakwb eng Javier Plaza verfasserin aut UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters. crop biophysical variables drone forage association PLS vegetation indices Agriculture S Marco Criado verfasserin aut Nilda Sánchez verfasserin aut Rodrigo Pérez-Sánchez verfasserin aut Carlos Palacios verfasserin aut Francisco Charfolé verfasserin aut In Agronomy MDPI AG, 2012 11(2021), 9, p 1697 (DE-627)658000543 (DE-600)2607043-1 20734395 nnns volume:11 year:2021 number:9, p 1697 https://doi.org/10.3390/agronomy11091697 kostenfrei https://doaj.org/article/4987e4960e5c4de88f0780821ca564e3 kostenfrei https://www.mdpi.com/2073-4395/11/9/1697 kostenfrei https://doaj.org/toc/2073-4395 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 9, p 1697
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authorswithroles_txt_mv	Javier Plaza @@aut@@ Marco Criado @@aut@@ Nilda Sánchez @@aut@@ Rodrigo Pérez-Sánchez @@aut@@ Carlos Palacios @@aut@@ Francisco Charfolé @@aut@@
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author	Javier Plaza
spellingShingle	Javier Plaza misc crop biophysical variables misc drone misc forage association misc PLS misc vegetation indices misc Agriculture misc S UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations
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topic_title	UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations crop biophysical variables drone forage association PLS vegetation indices
topic	misc crop biophysical variables misc drone misc forage association misc PLS misc vegetation indices misc Agriculture misc S
topic_unstemmed	misc crop biophysical variables misc drone misc forage association misc PLS misc vegetation indices misc Agriculture misc S
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title	UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations
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title_full	UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations
author_sort	Javier Plaza
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author_browse	Javier Plaza Marco Criado Nilda Sánchez Rodrigo Pérez-Sánchez Carlos Palacios Francisco Charfolé
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title_sort	uav multispectral imaging potential to monitor and predict agronomic characteristics of different forage associations
title_auth	UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations
abstract	The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters.
abstractGer	The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters.
abstract_unstemmed	The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters.
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title_short	UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations
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author2	Marco Criado Nilda Sánchez Rodrigo Pérez-Sánchez Carlos Palacios Francisco Charfolé
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