SCAN: an automated phenotyping tool for real-time capture of leaf stomatal traits in canola

Abstract

Canola is an important economic and agronomic crop globally, but its yield is under threat due to climate change. Stomata are a key breeding target because of their importance in carbon capture and water use efficiency. However, screening for elite stomatal traits could be laborious and time-consuming. We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN that combines the use of high-resolution portable digital microscopy with machine learning to automate stomatal trait phenotyping in canola. We show that SCAN can rapidly measure stomatal density, size, and pore area in canola at 97–99% accuracy, and capture real-time stomatal pore status that strongly correlated with leaf porometer measurement in canola. Here we use SCAN to investigate how leaf stomatal traits vary through a canopy in different ecotypes of canola grown in the field and glasshouse conditions. SCAN revealed that stomatal density in canola decreases in more expanded leaves with the abaxial surface having up to 40% more stomata that are 2× more open than the adaxial surface. SCAN also showed that patterns of stomatal traits in canola vary between leaf position in the canopy and change with environment in an ecotype-dependent manner.