High-throughput phenotyping for crop improvement in the genomics era

Area of Science:

• Plant Science
• Genomics
• Crop Improvement

Background:

• Genomics technologies have advanced significantly, yet their application in crop improvement is limited by a lag in high-quality phenotypic data collection.
• The primary research bottleneck in plant sciences is shifting from genotyping to phenotyping.

Purpose of the Study:

• To review the progress in systematic phenotypic data collection over the last decade to address the 'phenomics bottleneck'.
• To highlight the advancements in phenomics platforms and their application in major crops.

Main Methods:

• Utilizing sophisticated non-invasive imaging, spectroscopy, image analysis, and robotics for high-throughput phenotyping.
• Employing high-performance computing and phenomics databases for data management and analysis.
• Generating trait data on plant development, architecture, photosynthesis, growth, and biomass productivity.

Main Results:

• Modern phenomics platforms can collect phenotypic data on thousands of plants daily, marking a 'phenomics revolution'.
• Advances in phenomics platforms over the past decade have been applied to major crops like rice, wheat, barley, and maize.
• Phenomics data aids in identifying genes/QTL through mapping and genome-wide association studies (GWAS).

Conclusions:

• The phenomics revolution provides essential tools for unlocking genomic information and accelerating crop improvement.
• Establishing phenomics databases and promoting data sharing are crucial for advancing crop breeding.
• Genomics-assisted breeding (GAB) is significantly enhanced by integrated phenomics and genomics data.