Implementing digital multispectral 3D scanning technology for rapid assessment of hemp (Cannabis sativa L.) weed competitive traits

Authors: SINGH, GURSEWAK - Clemson University; Slonecki, Tyler; Wadl, Phillip; SOSNOSKIE, LYNN - Cornell University; FLESSNER, MICHAEL - Virginia Polytechnic Institution & State University; CUTULLE, MATTHEW - Clemson University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/18/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: The demand for hemp derived products, including textiles and fibers, food and feed, personal hygiene and nutraceutical/pharmaceutical products, has created an economic opportunity for United States growers. Weeds are problematic in all types of hemp production, and weed control is needed to prevent crop failure, maximize yield potential, and reduce habitat for other pests such as insects and pathogens. Therefore, there is a need to evaluate hemp varieties exhibiting different types of growth with respect to size and branching habits, which will identify their ability to compete against weeds. The purpose of this study was to comparatively assess the accuracy of determining morphological traits of selected hemp genotypes with different growth habits by traditional methods and by digital phenotyping. In our study, we identified a strong relationship between digital biomass and manually measured biomass, as well as between digital height and manually measured height. This observation suggests that 3D imaging is a precise and potentially valuable method for evaluating weed competitive morphological features in hemp and similar crops. Moreover, image based analyses used in this study are non-destructive, rapid techniques with minimal error and human interference, which have a great potential to utilize in planning weed management. This knowledge can serve as a foundation for plant breeders and weed scientists in the development of tailored strategies for effective weed management in hemp cultivation.

Technical Abstract: 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 for use on hemp, growers have limited weed management options. Slow-growing hemp varieties can be outcompeted by weeds for sunlight, water, and nutrients. Therefore, growers need to use integrated weed management (IWM) strategies that may be easily adopted for effective weed control. To solve these challenges, novel approaches are required to identify quantitative phenotypes and explain the genetic basis of important weed-competitive traits. These advances will facilitate the screening of germplasm with high performance characteristics in resource-limited environments such as crop-weed competition. Plant height and canopy architecture may affect crop-weed competition. However, manually measuring these parameters is a time-consuming process. Therefore, digital phenotyping tools were adopted to address this challenge. The PlantEye (PE) multispectral 3D scanner was selected as the high-throughput digital phenotyping technology for evaluation of plant architecture, which has been optimized for a range of crops and trusted to be reliable and highly accurate, but it has not been tested for crops like hemp with different growth habits and height differences. In this study, the suitability of digital phenotyping was evaluated at the Clemson University Coastal Research and Education Center to screen diverse hemp genotypes with different plant habit. We performed a range of validation tests for morphological features (digital biomass and plant height). Significant correlation (P < 0.001) between digital biomass and manually measured biomass (r = 0.89) as well between digital height and manually measured height (r = 0.94) observed, indicated high precision and usefulness of 3D multispectral scanning in measuring morphological traits. These results increased our confidence in the accuracy of the system and drastically reduced the amount of time-intensive destructive measurements needed to monitor biomass increase over time.