Stomatal function is a critical determinant of overall plant vigor, health, and yield. Higher stomatal conductance is associated with higher yields, and therefore is a trait of interest for plant improvement. Although stomatal conductance is governed by a complex balance between many factors, stomatal size and density are two traits that set the foundation for a genotype’s response to the external/internal factors. Understanding the genetic architecture of these traits is a key first step in the process of genetic selection; unfortunately, phenotyping stomatal traits on the scale required for mapping studies can be logistically challenging. In this experiment, we microscopically imaged stomata in apple leaves and used two computer vision methods to rapidly phenotype stomatal traits- a convolutional neural network (CNN) and the web-based computer vision platform BioDock. Two apple populations with existing molecular marker information were phenotyped: a biparental mapping population of approximately 400 individuals and the USDA’s Malus germplasm collection. Genetic mapping was carried out using the ‘r/qtl’ and ‘GWASpoly’ packages in R for the mapping population and germplasm collection, respectively. Both computer vision models yielded accuracies >90% for phenotyping stomatal density in the training and validation datasets, demonstrating that these models are effective methods for quickly phenotyping large stomatal image datasets. Preliminary results indicated peaks associated with stomatal density on chromosome 1 and chromosome 7. Furthermore, stomatal density was negatively correlated with stomatal size- resulting in less variation in total stomatal area than either the distributions of distribution or size would indicate. Future work in this project will focus on identifying the genes involved in regulating stomatal density in apples, as well as generalizing the computer vision models to function on multiple plant species.
