The economic significance of hemp (Cannabis sativa L.) as a source of grain, fiber, and flower is rising steadily. However, due to the lack of registered herbicides, hemp growers have limited weed management options. Slow-growing hemp varieties can be outcompeted by weeds for sunlight, water, and nutrients. Hence, easily adoptable integrated weed management (IWM) strategies are essential. Addressing these challenges necessitates novel approaches to identify quantitative phenotypes and explain the genetic basis of key weed-competitive traits. Plant height and canopy architecture may affect crop-weed competition. However, manually measuring these parameters is a time-consuming process. The PlantEye (PE) multispectral 3D scanner was selected as the high-throughput digital phenotyping technology for the evaluation of plant architecture. In this study, the suitability of digital phenotyping was evaluated at the Clemson University Coastal Research and Education Center to screen diverse hemp varieties with different plant habits. Digital plant biomass, plant height, and plant 3D-leaf area (including leaf area index, leaf angle, and light penetration) were periodically monitored. We performed a range of validation tests for morphological features (digital biomass and plant height). A significant correlation (P < 0.001) was observed between digital biomass and manually measured biomass (R = 0.89), as well as between digital height and manually measured height (R = 0.94), indicating the high precision and usefulness of 3D multispectral scanning in measuring morphological traits. Multispectral analyses used in this study are non-destructive, rapid techniques with minimal error and human interference, which have great potential for use in planning weed management.
