Machine learning in photosynthesis: Prospects on sustainable crop development
- PMID: 37473784
- DOI: 10.1016/j.plantsci.2023.111795
Machine learning in photosynthesis: Prospects on sustainable crop development
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.
Keywords: Crop yield, Deep learning; Machine learning; Photosynthesis; Photosynthetic pigments.
Copyright © 2023 Elsevier B.V. All rights reserved.
Conflict of interest statement
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.
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