Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and a case study on stomatal patterning

Abstract

Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs. In doing so, we hope to provide plant biologists with a foundational understanding of AI/ML and summarize the current capabilities and limitations of published tools. While most models show human-level performance for stomatal density (SD) quantification at superhuman speed, they are often likely to be limited in how broadly they can be applied across phenotypic diversity associated with genetic, environmental, or developmental variation. Other models can make predictions across greater phenotypic diversity and/or additional stomatal/epidermal traits, but require significantly greater time investment to generate ground-truth data. We discuss the challenges and opportunities presented by AI/ML-enabled computer vision analysis, and make recommendations for future work to advance accelerated stomatal phenotyping.

Plant phenotyping relevance

AI/ML画像解析による気孔形質推定を扱うレビューであり、植物フェノタイピング手法の開発・応用、性能と限界の評価が中心です。

Code and data availability

The supplied blocks are from a review/primer on ML for stomatal phenotyping. They describe methods and cite prior studies but contain no paper-specific phenotype datasets, images, code, or trained models with explicit public availability statements or author URLs. The only URLs present are ORCID profiles and the CC BY-

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