In this study, a model to predict LWP using via UAS equipped with a VIS-NIR multispectral camera and trained machine learning algorithm, is developed and tested. This research shows the potential for estimating LWP at a vineyard scale based on UAS information, represents a good and relatively cheap solution to assess plant status spatial distribution and therefore it could provide a direct way to achieve precise agricultural vineyard.

Effect of multi-level and multi-scale spectral data source on vineyard state assessment

Eugenia Monaco;Maurizio Buonanno;Rossella Albrizio;Pasquale Giorio;Arturo Erbaggio;Antonello Bonfante
2023

Abstract

In this study, a model to predict LWP using via UAS equipped with a VIS-NIR multispectral camera and trained machine learning algorithm, is developed and tested. This research shows the potential for estimating LWP at a vineyard scale based on UAS information, represents a good and relatively cheap solution to assess plant status spatial distribution and therefore it could provide a direct way to achieve precise agricultural vineyard.
2023
Istituto per i Sistemi Agricoli e Forestali del Mediterraneo - ISAFOM
Inglese
EGU General Assembly 2023
EGU General Assembly 2023
https://meetingorganizer.copernicus.org/EGU23/EGU23-16735.html
Sì, ma tipo non specificato
24-28/04/2023
Vienna, Austria
Leaf water potential
vineyard
UAS
VIS-NIR multispectral camera
12
info:eu-repo/semantics/conferenceObject
restricted
274
04 Contributo in convegno::04.02 Abstract in Atti di convegno
Monaco, Eugenia; Ezzy, Haitham; Brook, Anna; Buonanno, Maurizio; Albrizio, Rossella; Giorio, Pasquale; Erbaggio, Arturo; Arena, Carmen; Petracca, Fran...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/459458
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