rse.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x . As outlined, all files for the 3D printed leaf clip, the Python script for stomatal annotation and the files and instructions for installing and using the FieldDino App are provided in a public GitHub repository which guides users through each step – https://github.com/williamtsalter/FieldDinoMicroscopy . Validation datasets for the method are available on Roboflow – https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x . References Baloch , M. J. , J. Dunwell , K. DrN , et al. 2013 . “ Morpho‐Physiological Characterization of Spring Wheat Genotypes Under Drought Stress .” Inter
Open resource ↗williamtsalter/FieldDinoMicroscopy · lines:337-498Unverified paper record
FieldDino: Rapid In-Field Stomatal Anatomy and Physiology Phenotyping.
Plant, Cell & Environment · 27 May 2025 · 10.1111/pce.15639
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 robust field‐based phenotyping approach, FieldDino, for screening stomatal physiology and anatomy. The method allows measurements to be collected in p @0.5 of 97.1% for stomatal detection. When validated in large field trials of 200 wheat genotypes under two irrigation treatments, FieldDino 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.
Plant phenotyping relevance
FieldDinoは、圃場で気孔の生理・解剖形質を高スループットに取得するフェノタイピング手法として開発され、大規模圃場試験で検証されているため含める。
abstractWe present a high‐throughput robust field‐based phenotyping approach, FieldDino, for screening stomatal physiology and anatomy.
abstractWhen validated in large field trials of 200 wheat genotypes under two irrigation treatments, FieldDino captured wide diversity in stomatal traits.
Code and data availability
The paper's Data Availability Statement explicitly points to a public GitHub repository containing the 3D-printed leaf clip STL files, the Python stomatal annotation/measurement script, and the FieldDino app, plus a public Roboflow dataset hosting the training/validation stomatal image set used to train the YOLOv8-M模型.
Research Infrastructure Strategy (NCRIS). Open access publishing facilitated by The University of Sydney, as part of the Wiley ‐ The University of Sydney agreement via the Council of Australian University Librarians. Data Availability Statement The data that support the findings of this study are openly available in Roboflow at https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x . As outlined, all files for the 3D printed leaf clip, the Python script for stomatal annotation and the files and instructions for installing and using the FieldDino App are provided in a public GitHub repository which guides users through each step – https://github.com/williamt
Open resource ↗narrabri-plant-physiology-hclvi/fielddino-training-set-200x · lines:337-498This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.