Plants can talk: a new era in plant acoustics.

airborne sounds machine learning plant bioacoustics plant stress sound transmission

Journal

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

Informations de publication

Date de publication:
09 2023
Historique:
received: 05 05 2023
revised: 19 06 2023
accepted: 19 06 2023
medline: 14 8 2023
pubmed: 3 7 2023
entrez: 2 7 2023
Statut: ppublish

Résumé

Plants release chemical signals to interact with their environment when exposed to stress. Khait and colleagues unveiled that plants 'verbalize' stress by emitting airborne sounds. These can train machine learning models to identify plant stressors. This unlocks a new path in plant-environment interactions research with multiple possibilities for future applications.

Identifiants

pubmed: 37394307
pii: S1360-1385(23)00205-4
doi: 10.1016/j.tplants.2023.06.014
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Comment

Langues

eng

Sous-ensembles de citation

IM

Pagination

987-990

Commentaires et corrections

Type : CommentOn

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Muzammil Hussain (M)

College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, China.

Muhammad Khashi U Rahman (M)

Department of Microbiology and Genetics, University of Salamanca, Salamanca, 37007, Spain.

Ratnesh Chandra Mishra (RC)

Laboratory of Functional Plant Biology, Ghent University, 9000 Ghent, Belgium.

Dominique Van Der Straeten (D)

Laboratory of Functional Plant Biology, Ghent University, 9000 Ghent, Belgium. Electronic address: Dominique.VanDerStraeten@UGent.be.

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Classifications MeSH