FieldDino: Rapid In-Field Stomatal Anatomy and Physiology Phenotyping

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 robust field-based phenotyping approach, FieldDino, for screening stomatal physiology and anatomy. The method allows 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. 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.