Abstract
Stomatal anatomy and physiology define CO2 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 [email protected] 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.
Competing Interest Statement
The authors have declared no competing interest.
Data Availability
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. Datasets used for validation of the method are available on Roboflow – https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x.
Abbreviations
- a’
- Mean maximum stomatal pore area which is calculated assuming all stomatal pores are elliptical with the pore length equal to the major axis and the minor axis equal to half the pore length (υm2)
- CAIGE
- CIMMYT Australia ICARDA Germplasm Evaluation
- CIMMYT
- International Maize and Wheat Improvement Center
- CO2
- Carbon Dioxide
- COCO
- Common Objects in Context
- d
- Diffusivity of water in are at 22°C (m2 s-1)
- EDPIE
- Elite Diversity International Experiment
- ESWYT
- Elite Selection Wheat Yield Trial
- F1
- Harmonic mean of precision and recall
- FLOPs
- Floating Point Operations per Second
- gleaf,min
- Minimum leaf stomatal conductance
- gs
- Stomatal conductance
- gsmax
- Maximum anatomical stomatal conductance
- gsop
- Operating rate of stomatal conductance
- gsop/gsmax
- Stomatal conductance operating efficiency
- H2O
- Water
- HTWYT
- High Temperature Wheat Yield Trials
- ICARDA
- International Centre for Agricultural Research in the Dry Areas
- IoU
- Intersection over Union
- IRGA
- Infra-red gas analysers
- l
- Pore depth which is equal to the guard cell width at the centre of the stomata based on half the guard cell length (υm)
- mAP
- Mean average precision
- Params
- Parameters
- ΦPSII
- Quantum yield of electron transfer at PSII
- PSII
- Photosystem II
- SAI
- Stomatal Area Index
- SATYN
- Stress Adapted Trait Yield Nursery
- SAWYT
- Semi-Arid Wheat Yield Trial
- SD
- Stomatal density (stomata m-2)
- v
- Molar volume of air at 22°C (m3 mol-1)
- YOLO
- You Only Look Once
