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- W4384701561 endingPage "111795" @default.
- W4384701561 startingPage "111795" @default.
- W4384701561 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." @default.
- W4384701561 created "2023-07-20" @default.
- W4384701561 creator A5065718195 @default.
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- W4384701561 creator A5085408978 @default.
- W4384701561 creator A5088101563 @default.
- W4384701561 creator A5088728124 @default.
- W4384701561 date "2023-10-01" @default.
- W4384701561 modified "2023-10-17" @default.
- W4384701561 title "Machine learning in photosynthesis: Prospects on sustainable crop development" @default.
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