FieldDino: High-throughput physio-morphological phenotyping of stomatal characteristics for plant breeding research

Abstract

Abstract Stomatal anatomy and physiology define CO 2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding. Advances in instrumentation and data analyses present an opportunity to screen stomatal traits at scales relevant to plant breeding. We present a high-throughput field-based phenotyping approach, FieldDino, for screening of stomatal physiology and anatomy. The method allows coupled measurements to be collected in <15 s and consists of: (1) stomatal conductance measurements using a handheld porometer; (2) in situ collection of epidermal images with a digital microscope, 3D-printed leaf clip and Python-based app; and (3) automated deep learning analysis of stomatal features. The YOLOv8-M model trained on images collected in the field achieved strong performance metrics with an mAP@0.5 of 97.1% for stomatal detection. Validation in large field trials of 200 wheat genotypes with two irrigation treatments captured wide diversity in stomatal traits. FieldDino enables stomatal data collection and analysis at unprecedented scales in the field. This will advance research on stomatal biology and accelerate the incorporation of stomatal traits into plant breeding programs for resilience to abiotic stress. Highlight Chaplin et al., have developed FieldDino which enables rapid, high-throughput phenotyping of stomatal traits, advancing plant breeding research by integrating streamlined in-field measurements with automated deep learning analysis.

Plant phenotyping relevance

FieldDinoは、圃場での気孔生理・解剖形質の高速取得、画像収集、深層学習による自動抽出を統合した植物フェノタイピング手法であり、開発と大規模検証が研究の中心です。

Code and data availability

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