2017 ASHS Annual Conference

Spinach (Spinacia oleracea L.) is an economically important leafy green crop widely grown in the US. Spinach production must thrive in a dynamic environment constantly challenged by abiotic and biotic stresses. Such stresses have a profound effect on plant growth and development, ultimately reducing yield. Therefore, the current challenge in Spinach production is to increase crop productivity by improving crop resistance and tolerance to diseases and environmental stresses, respectively. Due to the “dioecious” nature of spinach (female and male organs are found in separate plants), breeding efficiency is very low using conventional methods. One of the major constraints to implement modern molecular breeding approaches in spinach breeding used to be the lack of a good linkage maps and the lack of high-density molecular markers. But, with the advancements in next-generation sequencing tools and the future completion of the reference spinach genome, as well as the identification of a large panel of SNPs by high-throughput genotyping, it is now possible to identify markers associated to traits of interest. However, to precisely link these markers, it is required to phenotype large and diverse populations. Traditionally, phenotyping has been performed by manual measurements at single time points, but the task is very time consuming and results in high variability due to human error. Therefore, it is necessary to develop high throughput phenotyping methods to combine with current genotyping capabilities to improve breeding efficiency. To validate our HTP system, we measure plant growth and development on diverse germplasm with different characteristics including leaf shape, color, and bolting time found in the USDA-NPGS spinach collection using an unmanned aerial vehicle equipped with a Blue, Green, and Red spectral wavelength camera and a Tetracam ADC Snap – multispectral sensor that records green, red, and near-infrared spectral wavelength. Canopy cover (CC) and canopy volume (CV) was measured in tomato over the growing season and data was fitted with a sigmoid function to generate CC and CV growth curves. From growing curves, several parameters were calculated to precisely characterize spinach lines. Once parameters were calculated, principal component and regression analysis allowed us to estimate spinach biomass, color, and bolt resistance. It is expected developed HTP methodology will have significant applications in breeding and crop management.