Machine learning in photosynthesis: Prospects on sustainable crop development.

Area of Science:

• Plant science
• Agricultural science
• Biotechnology

Background:

• Improving photosynthesis is key to enhancing crop yield and global food security.
• Analyzing large datasets of photosynthetic traits for efficiency presents a significant challenge.
• Machine learning (ML) offers advanced analytical capabilities for complex biological data.

Purpose of the Study:

• To review the application of ML in photosynthetic research and pigment studies.
• To highlight ML's potential in correlating hyperspectral data with photosynthetic parameters for crop yield improvement.
• To propose ML strategies for advancing photosynthetic pigment research.

Main Methods:

• Literature review of ML applications in photosynthesis research.
• Analysis of ML algorithms for correlating hyperspectral data with photosynthetic parameters.
• Exploration of ML for photosynthetic pigment research.

Main Results:

• ML algorithms can effectively analyze large datasets in photosynthetic research.
• Hyperspectral data combined with ML can improve the prediction of crop yield.
• ML provides novel approaches for studying photosynthetic pigments.

Conclusions:

• Machine learning is a powerful tool for advancing photosynthetic research and crop improvement.
• Integrating ML into pigment studies can accelerate progress in enhancing crop yield.
• ML applications are crucial for future innovations in agricultural science and food security.