Tasselyzer, a machine learning method to quantify maize anther exertion, based on PlantCV

Abstract

Summary Male fertility in maize involves complex genetic programming affected by environmental factors. Evaluating the presence and proportion of fertile anthers is crucial for agronomic purposes. Anthers in maize emerge from male-only florets, and quantifying anther exertion is a key indicator of male fertility; however, traditional manual scoring methods are subjective. To address this limitation, we developed an automated method, Tasselyzer , for large-scale analysis. This image-based program uses the PlantCV platform to provide a quantitative assessment of anther exertion, capturing regional differences within the tassel based on the distinct color of anthers. We successfully applied this method to diverse maize lines to demonstrate its utility for research and breeding programs. Significance Statement Tasselyzer is a novel image-based segmentation tool for automated, large-scale measurement of anther exertion and the impact of genetic and environmental variation on male fertility in maize.

Plant phenotyping relevance

トウモロコシの葯突出を画像ベースで自動定量する手法とソフトウェアを開発し、複数系統への適用も実施しており、植物フェノタイピング手法が研究の中心です。

Code and data availability

The paper explicitly states that Tasselyzer code, original and pseudo-colored tassel images, and the full image sets are publicly available on GitHub and Zenodo, directly supporting the paper's maize anther exertion phenotyping analysis.