Using high-throughput phenotyping platform MVS-Pheno to decipher the genetic architecture of plant spatial geometric 3D phenotypes for maize

Abstract

Maize (Zea mays) is one of the world’s most important crops, and its abundant and stable yield is crucial for ensuring global food security. Optimizing maize plant architecture can effectively enhance canopy structure, and ensure an ample supply of assimilates, thereby constituting a crucial strategy for achieving high yields in high-density planting systems. In this study, we used the phenotyping platform MVS-Pheno to synchronously collect multi-view image data of plant architecture, and the 3D phenotype analysis algorithm was developed to batch and automatically extract traits of spatial geometric structure. Using this phenotypic acquisition and analysis platform, 44 traits of maize plant architecture were defined and extracted, including 6 categories: basic phenotype, projection area related phenotype, leaf related phenotype, plant architecture dispersion related phenotype, volume related phenotype, and color related phenotype. Based on abundant phenotypic traits of plant architecture, we analyzed the phenotypic variations among a group of 495 inbred lines and further conducted GWAS to reveal the genetic components of the plant architecture. In summary, our work demonstrates valuable advances in high-throughput identification of qualitative traits for plant architecture, which could have major implications for improving high-density tolerant maize breeding and production.

Plant phenotyping relevance

MVS-Phenoプラットフォームと3D画像解析アルゴリズムによる植物体構造形質の高スループット取得・抽出が研究の中心であるため。

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