Assessing Grapevine Biophysical Parameters From Unmanned Aerial Vehicles Hyperspectral Imagery.

hyperspectral sensing image segmentation precision viticulture unmanned aerial vehicles (UAV) vegetation indices

Journal

Frontiers in plant science
ISSN: 1664-462X
Titre abrégé: Front Plant Sci
Pays: Switzerland
ID NLM: 101568200

Informations de publication

Date de publication:
2022
Historique:
received: 17 03 2022
accepted: 12 05 2022
entrez: 30 6 2022
pubmed: 1 7 2022
medline: 1 7 2022
Statut: epublish

Résumé

Over the last 50 years, many approaches for extracting plant key parameters from remotely sensed data have been developed, especially in the last decade with the spread of unmanned aerial vehicles (UAVs) in agriculture. Multispectral sensors are very useful for the elaboration of common vegetation indices (VIs), however, the spectral accuracy and range may not be enough. In this scenario, hyperspectral (HS) technologies are gaining particular attention thanks to the highest spectral resolution, which allows deep characterization of vegetative/soil response. Literature presents few papers encompassing UAV-based HS applications in vineyard, a challenging conditions respect to other crops due to high presence of bare soil, grass cover, shadows and high heterogeneity canopy structure with different leaf inclination. The purpose of this paper is to present the first contribution combining traditional and multivariate HS data elaboration techniques, supported by strong ground truthing of vine ecophysiological, vegetative and productive variables. Firstly the research describes the UAV image acquisition and processing workflow to generate a 50 bands HS orthomosaic of a study vineyard. Subsequently, the spectral data extracted from 60 sample vines were elaborated both investigating the relationship between traditional narrowband VIs and grapevine traits. Then, multivariate calibration models were built using a double approach based on Partial Least Square (PLS) regression and interval-PLS (iPLS), to evaluate the correlation performance between the biophysical parameters and HS imagery using the whole spectral range and a selection of more relevant bands applying a variable selection algorithm, respectively. All techniques (VIs, PLS and iPLS) provided satisfactory correlation performances for the ecophysiological (

Identifiants

pubmed: 35769294
doi: 10.3389/fpls.2022.898722
pmc: PMC9235871
doi:

Types de publication

Journal Article

Langues

eng

Pagination

898722

Informations de copyright

Copyright © 2022 Matese, Di Gennaro, Orlandi, Gatti and Poni.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Alessandro Matese (A)

Institute of BioEconomy, National Research Council (CNR-IBE), Firenze, Italy.

Salvatore Filippo Di Gennaro (SF)

Institute of BioEconomy, National Research Council (CNR-IBE), Firenze, Italy.

Giorgia Orlandi (G)

Institute of BioEconomy, National Research Council (CNR-IBE), Firenze, Italy.

Matteo Gatti (M)

Department of Sustainable Crop Production (DI.PRO.VE.S.), Università Cattolica del Sacro Cuore, Piacenza, Italy.

Stefano Poni (S)

Department of Sustainable Crop Production (DI.PRO.VE.S.), Università Cattolica del Sacro Cuore, Piacenza, Italy.

Classifications MeSH