StomaQuant: Deep learning-based quantification for stomatal trait assessment

Stomata are microscopic pores on leaf surfaces that play a vital role in
transpiration and gaseous exchange. The stomatal density and size directly
influence photosynthesis and hydrodynamics capacity. Conventional
approaches for counting and determining stomatal density is
labour-intensive and lack scalability. Although there are several AI-based
stomata finder tools that were published in the last decade, existing
models were trained on model plants like wheat, barley and Arabidopsis.
Stomata in such model plants are generally elliptical in shape, but
applying a universal model to all plant species would be inappropriate due
to their diverse morphological characteristics. Previous studies have
suggested using the stomatal index to quantify the ratio between epidermal
cells and total stomatal count. However, this approach can be difficult to
apply consistently, as epidermal cell shape and size vary across plant
species. Instead, we propose measuring stomatal density based on the
number of stomata per total imaged pixel area in the captured images. In
this study, a comparison between YOLOv12 and RF-DETR models were made for
real-time stomata detection in normal and difficult-to-image and
out-of-focus occluded images. The in-house training dataset consisted of
images of 300 rice, 100 barley and 50 sugarcane leaves that were captured
against a dark background. YOLOv12 outperformed RF-DETR with higher
mAP50:95 score. The models were trained with image augmentation for 300
epochs and YOLOv12 achieved a peak mean average precision of 98.5% and
excelled at detecting stomata across both monocot and dicot plants.
StomaQuant has shown to be effective for both epidermal peel and ethanol
decolouration samples. It can be used to estimate stomatal density and
size.