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

Abstract

Accurate identification of maize kernel morphology is crucial for breeding and quality improvement. Traditional manual methods are limited in dealing with complex structures and cannot fully capture kernel characteristics from a phenome perspective. To address this, our study aims to develop a high-throughput 3D phenotypic analysis method for maize kernels using Micro-CT-based point cloud data, thereby enhancing both accuracy and efficiency. We introduced new phenotypic indicators and developed a kernel phenome interaction network to better characterize the diversity and variability of kernel traits. Using a natural population of maize, high-resolution 2D slice data from Micro-CT scans were converted into 3D point cloud models for detailed analysis. This process led to the proposal of five new indicators, such as the endosperm density uniformity index (ENDUI) and endosperm integrity index (ENII), and the construction of their corresponding phenome interaction network. The study identified 27 3D morphological feature parameters, significantly improving the accuracy of kernel phenotypic analysis. These new indicators enable a more comprehensive evaluation of trait differences between subgroups. Results show that ENDUI and ENII are central to the phenome interaction networks, revealing synergistic relationships and environmental adaptation strategies during kernel growth. Additionally, it was found that length traits significantly impact the volumes of the embryo and endosperm, with linear regression coefficients of 0.599 and 0.502, respectively. This study not only advances maize kernel morphology research but also offers a novel method for phenotypic analysis. By enriching the phenotypic diversity of maize kernels, it contributes to breeding programs and grain processing improvements, ultimately enhancing the quality, and utilization value of maize kernels.

Plant phenotyping relevance

Micro-CT画像からトウモロコシ粒の3D形態形質を抽出する高スループット表現型解析法の開発が研究の中心であり、新規指標と解析手法を提示している。

Code and data availability

The paper describes Micro-CT-based 3D phenotyping of 288 maize association population kernels, but no public repository deposit of phenotype data, point clouds, images, or analysis code is stated. The data availability statement only offers raw data from the authors upon request; the supplementary material link is the仅

No evidence-backed public reproduction asset is currently recorded.