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

Abstract

Maize anthers emerge from male-only florets, a process that involves complex genetic programming and is affected by environmental factors. Quantifying anther exertion provides a key indicator of male fertility; however, traditional manual scoring methods are often subjective and labor-intensive. To address this limitation, we developed Tasselyzer - an accessible, cost-effective, and time-saving method for quantifying maize anther exertion. This image-based program uses the PlantCV platform to provide a quantitative assessment of anther exertion by capturing regional differences within the tassel based on the distinct color of anthers. We applied this method to 22 maize lines with six genotypes, showing high precision (F 1 score > 0.8). Furthermore, we demonstrate that customizing the parameters to assay a specific line is straightforward and practical for enhancing precision in additional genotypes. Tasselyzer is a valuable resource for maize research and breeding programs, enabling automated and efficient assessments of anther exertion.

Plant phenotyping relevance

画像解析と機械学習により、雄性不稔性に関わるトウモロコシ葯の突出度を定量化する手法を開発・評価しており、植物表現型取得が研究の中心である。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

No evidence-backed public reproduction asset is currently recorded.