High-throughput phenotyping for plant growth and biomass yield of switchgrass under a controlled environment

Abstract

Switchgrass (<italic>Panicum virgatum</italic> L.) is a native and prominent perennial grass species used for feedstocks. High-throughput phenotyping of biomass component traits is desirable for switchgrass improvement and production. The objective of this study was to establish correlations between the manually measured traits and image-extracted measurements in switchgrass grown in a controlled environment. Red-green-blue (RGB) images from side- and top-views were automatically collected from the plants varying in growth stages for assessing their relationships with manually measured traits. Plant height, tiller number, crown diameter, and shoot dry weight were all significantly correlated with RGB image-based measurements including side-view height (SHT), side convex hull (SCH), side projected area (SPA), top convex hull (TCH), and top projected area (TPA). For a particular plant trait, a good prediction was observed based on an image-based measurement, including plant height and SHT (R² = 0.992), tiller number and SPA (R² = 0.86), crown diameter and SCH (R² = 0.72), and shoot dry weight and SPA (R² = 0.88). Plant height was also well predicted by SCH (R² = 0.94) and SPA (R² = 0.88). Overall, SHT, SCH, and SPA extracted from RGB images well predicted plant height, tiller number and shoot dry weight. The results demonstrated that the image-based parameters could be leveraged in quantifying the growth and development of switchgrass.