FieldDino: High-throughput physio-morphological phenotyping of stomatal characteristics for plant breeding research
Edward ChapliniD, Guy ColemaniD, Andrew MerchantiD, William SalteriD
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 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. HighlightChaplin 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.
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In-depth summary
Claude Opus 5.5 · xhigh effort · 8 Nov 2026 · from the full text of version 1 (14 Oct 2024) · CC BY 4.0
Handheld porometry, a 3D-printed leaf-clip microscope and YOLOv8 segmentation jointly phenotype stomatal conductance and anatomy on the same wheat leaf across 200 field genotypes.
Breeders seldom target stomatal traits because gas-exchange analysers are slow and costly, epidermal peels are low-throughput, and earlier deep-learning stomata models covered few glasshouse-grown genotypes. FieldDino links in-field porometry, imaging of the same leaf and automated segmentation, so anatomical maximum conductance and operating efficiency can be screened at breeding-trial scale.
Key results
ShownThe combined porometer and leaf-clip microscope workflow scaled to a replicated field trial. It collected paired physiology and anatomy data much faster than screening based on gas exchange.
2,400 coupled measurements on 200 wheat genotypes over 5 days (09:30 to 15:00); < 15 s per measurement, about 1 min including leaf selection and plot moves; over 60 plants per hour vs less than 10 for IRGAs; microscope capture < 10 s per leaf at under AU $2,000 per unit. · 3 Results; 4 Discussion
ShownA COCO-pretrained YOLOv8 medium segmentation model, chosen from five model sizes, accurately outlined whole stomatal complexes in field microscope tiles.
Fine-tuned on 518 images covering all 200 genotypes, tiled into 14,928 sub-images (13,068 train, 1,236 validation, 624 test); YOLOv8-M reached mask mAP@0.5 of 97.1% and mAP@[0.5:0.95] of 65.9%; the N, S, L and X variants were compared on precision, recall, F1, FLOPs and parameter count. · Image annotation; Model training and validation; Tables
ShownGuard cell length predicts pore length closely enough to derive anatomical maximum conductance (gsmax) from segmented stomata. The authors report that the resulting gsmax values align closely with previously published values.
ImageJ measurements of 40 stomata on each of 50 adaxial and 50 abaxial images gave R^2=0.98, with pore length = 0.543 x guard cell length; gsmax spanned 0.58 to 2.22 mol m^-2 s^-1 (adaxial tending higher), slightly wider than the 0.7-2.0 mol m^-2 s^-1 range cited from prior studies. · Guard cell – pore length relationship &…; 3.2 Stomatal Anatomy; 4 Discussion
SuggestedThe automated anatomy measurements reproduced expected patterns: higher stomatal density on the adaxial than the abaxial surface, denser but smaller stomata under rainfed conditions, and a negative relationship between stomatal size and density.
Mean density 50.31 (adaxial) vs 42.87 (abaxial) per mm^2, and the Discussion separately cites a 31.5% higher adaxial density; guard cell length 68.39/68.55 µm irrigated vs 64.30/66.04 µm rainfed (abaxial/adaxial); overall means 66.8 µm length, 40.2 µm width, 46.6 per mm^2; no statistical tests were run. · Analyses and visualisation of data; 3.2 Stomatal Anatomy; 4 Discussion
SuggestedPairing operating conductance with gsmax showed that stomatal operating efficiency varies with genotype and water supply. Efficiency fell under rainfed conditions, together with lower conductance and lower ΦPSII (quantum yield of photosystem II).
Rainfed conductance was 24.4% (abaxial) and 28.2% (adaxial) below irrigated, vs 26% - 50% drought reductions reported previously; median adaxial gsop/gsmax across genotypes 0.03 to 0.25 irrigated vs 0.01 to 0.17 rainfed; median abaxial ΦPSII 0.61 irrigated vs 0.46 rainfed. · 3.1 Stomatal conductance and chlorophyll…; 3.3 Combined stomatal physiology and anatomy…; 4 Discussion
Methods, data and reproducibility
A LI-COR LI-600 porometer/fluorometer measures abaxial and adaxial conductance and fluorescence. The same leaf is then imaged at 200x with a Dino-Lite microscope in a 3D-printed clip. A fine-tuned YOLOv8-M instance segmentation model plus OpenCV ellipse fitting gives stomatal density, size and gsmax (Franks and Farquhar equation).
- Diverse CIMMYT, ICARDA and University of Sydney bread wheat lines in a rainfed vs irrigated field trial (Narrabri NSW, 2023), with 3 replicate plots per genotype per treatment · wheat (Triticum aestivum) · n = 200 genotypes; 2,400 coupled measurements
- Dino-Lite 200x flag-leaf images annotated with stomatal polygons in Roboflow, tiled into 640 x 640 augmented sub-images for training · wheat (Triticum aestivum) · n = 518 images; 14,928 sub-images (13,068 train/1,236 val/624 test)
- Manual ImageJ measurements of guard cell vs pore length, used to calibrate the pore length ratio for gsmax · wheat (Triticum aestivum) · n = 40 stomata per image; 50 adaxial and 50 abaxial images
- Common Objects in Context (COCO) dataset, used as pretraining for the YOLOv8 segmentation weights
- Methods & tools
- LI-COR LI-600 porometer/fluorometer, Dino-Lite 5MP AM7515MT2A handheld USB microscope, DinoXcope v2.4.1, Tinkercad (3D-printed leaf clip design), Roboflow (annotation, tiling, augmentation), YOLOv8-seg via Ultralytics (N, S, M, L, X), PyTorch, OpenCV (ellipse fitting, stomatal measurements), PyQt5 (FieldDino capture app GUI), ImageJ, R (dplyr, ggplot2, gridExtra, tidyr), Franks and Farquhar (2001) gsmax equation
- Data & accessions
- https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x
- Software & environment
- Python FieldDino app built on PyQt5, Ultralytics and PyTorch; DinoXcope v2.4.1 used for the 2023 image capture; R with dplyr, ggplot2, gridExtra and tidyr. No other package versions or containers are specified.
- Reproducibility gaps
- Only the pre-augmentation training images are released. The conductance and anatomy measurements from the 2,400 samples are part of a larger dataset and are not linked. Field tests of the FieldDino app are reported as data not shown.
Limitations (6)
- At 200x magnification the pore was obscured in some images, so aperture was not measured directly. Pore length was instead inferred as 0.543 of guard cell length; the authors recommend 400x imaging for wheat and higher magnification for species with smaller stomata. · stated by the authors
- The microscope is still manually focused. The LI-600 lacks a proper light source, so electron transport rate readings were too noisy under passing clouds and were discarded. · stated by the authors
- No inferential statistics were run on the trait data: differences between genotypes and treatments are described only as qualitative trends, pending analysis of the larger dataset. · stated by the authors
- The method does not capture dynamic stomatal responsiveness. It also still requires staff to walk plot to plot measuring single leaves, rather than remote or automated sensing across whole trials. · stated by the authors
- Automated density and size outputs were not compared with manual counts on the same leaves, and porometer readings were not compared with IRGA readings. Accuracy is argued only from agreement with literature ranges. · noted in this summary
- Training images came from a single day, and the 624 test tiles were cut from the same 518-image pool without a stated image-level split. Generalization to other sites, seasons, magnifications or species is untested. · noted in this summary
Who should read:Read this if you need a low-cost, field-portable workflow to measure stomatal conductance, density, size and gsmax on the same leaf across hundreds of crop genotypes.
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