Are unmanned aerial vehicle-based hyperspectral imaging and machine learning advancing crop science?

hyperspectral imagery machine learning precision agriculture remote sensing unmanned aerial vehicle (UAV)

Journal

Trends in plant science
ISSN: 1878-4372
Titre abrégé: Trends Plant Sci
Pays: England
ID NLM: 9890299

Informations de publication

Date de publication:
04 Oct 2023
Historique:
received: 13 10 2022
revised: 07 08 2023
accepted: 05 09 2023
medline: 7 10 2023
pubmed: 7 10 2023
entrez: 6 10 2023
Statut: aheadofprint

Résumé

The past few years have seen increased interest in unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) and machine learning (ML) in agricultural research, concomitant with an increase in published research on these topics. We provide an updated review, written for agriculturalists, highlighting the benefits in the retrieval of biophysical parameters of crops via UAVs relative to less sophisticated options. We reviewed >70 recent papers and found few consistent results between similar studies. Owing to their high complexity and cost, especially when applied to crops of low value, the benefits of most of the research reviewed are difficult to explain. Future effort will be necessary to distill research findings into lower-cost options for end-users.

Identifiants

pubmed: 37802693
pii: S1360-1385(23)00294-7
doi: 10.1016/j.tplants.2023.09.001
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests The authors have no interests to declare.

Auteurs

Alessandro Matese (A)

Geosystems Research Institute, Mississippi State University, Box 9627, Starkville, MS, USA; Institute of BioEconomy, National Research Council (CNR-IBE), Via Caproni 8, 50145 Florence, Italy. Electronic address: alessandro.matese@cnr.it.

Joby M Prince Czarnecki (JM)

Geosystems Research Institute, Mississippi State University, Box 9627, Starkville, MS, USA.

Sathishkumar Samiappan (S)

Geosystems Research Institute, Mississippi State University, Box 9627, Starkville, MS, USA.

Robert Moorhead (R)

Geosystems Research Institute, Mississippi State University, Box 9627, Starkville, MS, USA.

Classifications MeSH