Analyzing architectural diversity in maize plants using the skeleton-image-based method

Abstract

Shoot architecture in maize is critical since it determines resource use, impacts wind and rain damage tolerance, and affects yield stability. Quantifying the diversity among inbred lines in heterosis breeding is essential, especially when describing germplasm resources. However, traditional geometric description methods oversimplify shoot architecture and ignore the plant’s overall architecture, making it difficult to reflect and illustrate diversity. This study presents a new method to describe maize shoot architecture and quantifies its diversity by combining computer vision algorithms and persistent homology. Our results reveal that persistent homology can capture key characteristics of shoot architecture in maize and other details often overlooked by traditional geometric analysis. Based on this method, the morphological diversity of shoot architecture can be mined (quantified), and the main shoot architecture types can be obtained. Consequently, this method can easily describe the diversity of shoot architecture in many maize materials.

Keywords

• maize
• shoot architecture
• persistent homology
• phenotyping technology
• morphological diversity