Precise 3D geometric phenotyping and phenotype interaction network construction of maize kernels

Area of Science:

• Agricultural Science
• Plant Biology
• Computational Biology

Background:

• Traditional maize kernel morphology analysis is labor-intensive and lacks phenomic depth.
• Existing methods struggle to capture complex 3D kernel structures and variability.
• High-throughput phenotyping is essential for maize breeding and quality improvement.

Purpose of the Study:

• To develop a high-throughput 3D phenotypic analysis method for maize kernels using Micro-CT.
• To introduce novel phenotypic indicators and a kernel phenome interaction network.
• To enhance the accuracy and efficiency of maize kernel morphology identification.

Main Methods:

• Utilized Micro-CT scanning to obtain high-resolution 2D slice data of maize kernels.
• Converted 2D data into 3D point cloud models for detailed morphological analysis.
• Developed five new phenotypic indicators (e.g., ENDUI, ENII) and a phenome interaction network.

Main Results:

• Identified 27 3D morphological feature parameters, significantly improving analysis accuracy.
• ENDUI and ENII emerged as central nodes in the phenome interaction network, indicating synergistic relationships.
• Maize kernel length was found to significantly impact embryo and endosperm volumes.

Conclusions:

• The developed 3D analysis method offers a novel approach for maize kernel phenotyping.
• New indicators and network analysis provide deeper insights into kernel trait diversity and environmental adaptation.
• This research contributes to maize breeding programs and enhances grain quality and utilization.