A method for obtaining field wheat freezing injury phenotype based on RGB camera and software control

Area of Science:

• Agricultural Science
• Plant Physiology
• Biotechnology

Background:

• Freezing stress negatively impacts wheat growth and yield.
• Traditional visual assessment of freezing injury is inefficient and labor-intensive.
• A need exists for accurate, high-throughput methods for identifying freezing damage.

Purpose of the Study:

• To develop and validate a high-throughput phenotyping system for quantifying wheat freezing injury.
• To automate the assessment of freezing damage in field-grown wheat.
• To establish a grading system for wheat freezing injury.

Main Methods:

• Developed an RGB freezing injury system using a mobile phenotype cabin for automated image collection and analysis.
• Implemented a data management system for storing images and phenotypic data.
• Extracted image features (ExG, ExR, ExV) from wheat canopy images at the seedling stage.

Main Results:

• The RGB freezing injury system efficiently quantifies wheat freezing damage in field conditions.
• Wheat canopy coverage significantly impacts freezing injury assessment.
• A preliminary four-grade system was established to evaluate freezing injury across different wheat varieties.

Conclusions:

• The automated phenotypic analysis offers an alternative for high-throughput freezing damage analysis in crops.
• This method enables precise quantification of freezing stress.
• The system guides the selection of wheat genotypes with superior frost resistance, accelerating breeding programs.