A cost-effective maize ear phenotyping platform enables rapid categorization and quantification of kernels

Area of Science:

• Plant biology
• Genetics
• Bioinformatics

Background:

• High-throughput phenotyping is revolutionizing biological research.
• Automated analysis of plant traits, like maize kernel phenotypes, is crucial for genetic studies.

Purpose of the Study:

• To develop a cost-effective, high-throughput imaging and deep learning system for analyzing maize (Zea mays) ear and kernel phenotypes.
• To automate the quantification of kernel phenotypes and identify genetic transmission defects.

Main Methods:

• Construction of a low-cost maize ear scanner using readily available parts.
• Development of a deep learning computer vision pipeline for automated kernel counting and phenotype analysis.
• Utilizing transfer learning and object detection models to analyze over 390,000 kernels.

Main Results:

• The system successfully generated 2D projections of maize ears, clearly distinguishing GFP and anthocyanin kernel phenotypes.
• The automated system rapidly assessed hundreds of thousands of kernels, identifying male-specific transmission defects in GFP-marked mutant alleles.
• A novel genetic defect was identified, potentially linked to the vacuolar processing enzyme gene Zm00001d002824.

Conclusions:

• The developed imaging and deep learning system significantly accelerates and scales the quantification of maize ear and kernel phenotypes.
• This cost-effective approach enables the generation of large datasets for robust genetic analyses and discovery of novel gene functions.