Varghese, Ressin and Cherukuri, Aswani Kumar and Doddrell, Nicholas H and Doss, C George Priya and Simkin, Andrew J and Ramamoorthy, Siva (2023) Machine learning in photosynthesis: prospects on sustainable crop development. Plant Science, 335. p. 111795. DOI https://doi.org/10.1016/j.plantsci.2023.111795
Varghese, Ressin and Cherukuri, Aswani Kumar and Doddrell, Nicholas H and Doss, C George Priya and Simkin, Andrew J and Ramamoorthy, Siva (2023) Machine learning in photosynthesis: prospects on sustainable crop development. Plant Science, 335. p. 111795. DOI https://doi.org/10.1016/j.plantsci.2023.111795
Varghese, Ressin and Cherukuri, Aswani Kumar and Doddrell, Nicholas H and Doss, C George Priya and Simkin, Andrew J and Ramamoorthy, Siva (2023) Machine learning in photosynthesis: prospects on sustainable crop development. Plant Science, 335. p. 111795. DOI https://doi.org/10.1016/j.plantsci.2023.111795
Abstract
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.
Item Type: | Article |
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Uncontrolled Keywords: | Crop yield, Deep learning; Machine learning; Photosynthesis; Photosynthetic pigments |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Life Sciences, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 24 Jul 2023 17:53 |
Last Modified: | 18 Jul 2024 01:00 |
URI: | http://repository.essex.ac.uk/id/eprint/36036 |
Available files
Filename: Varghese et al AAM.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0