Machine learning in photosynthesis: Prospects on sustainable crop development.
Crop yield, Deep learning
Machine learning
Photosynthesis
Photosynthetic pigments
Journal
Plant science : an international journal of experimental plant biology
ISSN: 1873-2259
Titre abrégé: Plant Sci
Pays: Ireland
ID NLM: 9882015
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
03
05
2023
revised:
10
07
2023
accepted:
13
07
2023
medline:
31
8
2023
pubmed:
21
7
2023
entrez:
20
7
2023
Statut:
ppublish
Résumé
Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.
Identifiants
pubmed: 37473784
pii: S0168-9452(23)00212-1
doi: 10.1016/j.plantsci.2023.111795
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
111795Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest 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.