Implementing Digital Multispectral 3D Scanning Technology for Rapid Assessment of Hemp (<i>Cannabis sativa</i> L.) Weed Competitive Traits

Abstract

The economic significance of hemp (<i>Cannabis sativa</i> L.) as a source of grain, fiber, and flower is rising steadily. However, due to the lack of registered herbicides effective in hemp cultivation, growers have limited weed management options. Plant height, biomass, and canopy architecture may affect crop&ndash;weed competition. Greenhouse experiments conducted at the joint Clemson University Coastal Research and Education Center and USDA-ARS research facility at Charleston, SC, USA used 27 hemp varieties, grown under controlled temperature and light conditions. Weekly plant scans using a digital multispectral phenotyping system, integrated with machine learning algorithms of the PlantEye F500 instrument, (Phenospex, Heerlen, Netherlands) captured high-resolution 3D models and spectral data of the plants. Manual and scanner-based measurements were validated and analyzed using statistical methods to assess plant growth and morphology. This study included validation tests showing a significant correlation (<i>p</i> &lt; 0.001) between digital and manual measurements (R² = 0.89 for biomass, R² = 0.94 for height), indicating high precision. The use of 3D multispectral scanning significantly reduces the time-intensive nature of manual measurements, allowing for a more efficient assessment of morphological traits. These findings suggest that digital phenotyping can enhance integrated weed management strategies and improve hemp crop productivity by facilitating the selection of competitive hemp varieties.