Machine learning in photosynthesis : Prospects on sustainable crop development
Copyright © 2023 Elsevier B.V. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:335 |
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| Enthalten in: |
Plant science : an international journal of experimental plant biology - 335(2023) vom: 20. Okt., Seite 111795 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Varghese, Ressin [VerfasserIn] |
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| Links: |
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| Themen: |
Crop yield, Deep learning |
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| Anmerkungen: |
Date Completed 31.08.2023 Date Revised 31.08.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.plantsci.2023.111795 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2023 Elsevier B.V. All rights reserved. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
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| 650 | 4 | |a Photosynthetic pigments | |
| 700 | 1 | |a Cherukuri, Aswani Kumar |e verfasserin |4 aut | |
| 700 | 1 | |a Doddrell, Nicholas H |e verfasserin |4 aut | |
| 700 | 1 | |a Doss, C George Priya |e verfasserin |4 aut | |
| 700 | 1 | |a Simkin, Andrew J |e verfasserin |4 aut | |
| 700 | 1 | |a Ramamoorthy, Siva |e verfasserin |4 aut | |
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