A Robust Automated Image-Based Phenotyping Method for Rapid Vegetative Screening of Wheat Germplasm for Nitrogen Use...

Area of Science:

• Agricultural Science
• Plant Biology
• Genetics

Background:

• Low nitrogen use efficiency (NUE) in crops leads to significant nitrogen loss, increasing costs and environmental impact.
• Breeding crop varieties with enhanced NUE requires efficient phenotyping and molecular approaches.

Purpose of the Study:

• To develop and validate an automated, image-based phenotyping method for screening wheat germplasm for improved nitrogen use efficiency (NUE).
• To identify optimal nitrogen (N) levels and growth stages for effective NUE screening in wheat.

Main Methods:

• Wheat varieties were screened under low (5 mM) and optimum (20 mM) nitrogen conditions using an automated imaging phenotyping platform.
• Plant parameters like shoot biomass and top-view area were estimated from digital images and correlated with traditional phenotypic traits.
• The
• broken-stick
• model was used to analyze vegetative linear growth for differentiating NUE performance.

Main Results:

• Estimated plant parameters from digital images strongly correlated with actual shoot biomass and leaf area, serving as effective surrogate measures.
• The automated phenotyping method successfully differentiated wheat varieties based on their nitrogen use efficiency (NUE) during the vegetative growth phase.
• Screening under low nitrogen conditions during the vegetative stage showed a high correlation with biomass and grain yield at harvest.

Conclusions:

• Automated image-based phenotyping is a highly applicable method for identifying nitrogen-efficient (N-efficient) wheat germplasm early in the vegetative growth phase.
• Greenhouse-based phenotyping can effectively prioritize germplasm for subsequent field studies, optimizing resource allocation.
• This screening approach aids in breeding wheat varieties with improved nitrogen use efficiency (NUE), addressing environmental and economic concerns.