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The DAISy (Digital Agro-environment and Intelligent Systems) Lab aims to advance our understanding of the natural environment by developing new use cases for mobile and optical sensor systems to monitor environmental and agricultural processes.
The lab currently has two major thrusts:
(1) non-destructive, optical sensing to model and predict characteristics of biological materials, and
(2) integrating sensing and sampling technologies with mobile aerial and aquatic vehicles for informative environmental monitoring.
Current applications of interest and pursuit include evaluating water quality using UAS, measuring earth surface processes with ground-based instrumentation, and monitoring crops across various production systems with optical sensors.
Selected Recently Completed and Ongoing Projects
Selected Recently Completed and Ongoing Projects
Advancing camera-based monitoring for operational hydrologic applications
Advancing camera-based monitoring for operational hydrologic applications
To address the challenges of implementing low-cost cameras into hydrologic monitoring, this project, funded by the U.S. Geological Survey, focuses on integrating existing and cutting-edge computer vision algorithms, AI models, and computational tools into a camera-based monitoring system. These systems will be deployed at selected sites, and the images and video, along with advanced processing algorithms, will generate data on water levels and surface velocity.
Publications:
Neupane, S., Horsburgh, J. S., Issa, R. B.*, & Young, S. (2026). "HydrocamCompute: Serverless Computing Workflow for Camera-based Hydrological Monitoring." Environmental Modelling & Software, 106929. doi.org/10.1016/j.envsoft.2026.106929
Issa, R. B.*, Neupane, S., Khan, S.*, Horsburgh, J. S., & Young, S. (2026). "Towards real-time water level and discharge measurements using imagery, machine learning, and edge computing." Journal of Hydroinformatics, jh2026147. doi.org/10.2166/hydro.2026.147
S. Neupane, J. S. Horsburgh, R. Bin Issa*, and S. Young. (2026). “HydrocamCollect: A Robust Data Acquisition and Cloud Data Transfer Workflow for Camera-based Hydrological Monitoring,” Environmental Modelling & Software, vol. 196. doi.org/10.1016/j.envsoft.2025.106770.
Surface and aerial vehicles to advance water quality monitoring for aquaculture
Surface and aerial vehicles to advance water quality monitoring for aquaculture
Our ongoing project through the National Robotics Initiative aims to develop surface and aerial vehicles for smart sampling of coliform bacteria. In collaboration with faculty from NC State, we are currently integrating water quality models with sampling and planning strategies for shellfish production areas along the coast of NC. Field studies are ongoing -- stay tuned for more publications in progress!
Publications:
Nguyen, A., Ore, J.P., Castro-Bolinaga, C., Hall, S., Young, S. (2024). Towards autonomous, optimal water sampling with aerial and surface vehicles for rapid water quality assessment. Journal of the ASABE. doi.org/10.13031/ja.15796
Exploring new applications for aerial vehicles
Exploring new applications for aerial vehicles
Our group is interested in exploring new ways to expand the capabilities of and find new uses for aerial platforms for agricultural and environmental applications. Recently, we've developed a platform for in situ surface soil moisture measurement, and we've previously developed a lightweight payload for controlled pollinations in loblolly pine orchards. Other areas of ongoing exploration include water quality monitoring and sampling.
Publications:
Dakshinamurthy, H. N., Jones, S. B., Schwartz, R. C., & Young, S. N. (2025). Waveform analysis for short time domain reflectometry (TDR) probes to obtain calibrated moisture measurements from partial vertical sensor insertions. Computers and Electronics in Agriculture, 235, 110233. doi.org/10.1016/j.compag.2025.110233
Dakshinamurthy, H. N., Jones, S. B., Corkins, S., Pandey, P., & Young, S. N. (2024). Design and evaluation of an aerial vehicle payload for automated near-surface soil moisture measurements. Computers and Electronics in Agriculture, 227, 109518. doi.org/10.1016/j.compag.2024.109518
Pandey*, Acosta, Payn, and Young, Towards autonomous, aerial pollination: Design of a robotic pollinator payload for controlled crosses in loblolly pine. Applied Engineering in Agriculture, vol. 40, no. 6, 2024. doi.org/10.13031/aea.15916.
Young, Lanciloti, Peschel (2022). The Effects of Interface Views on Performing Aerial Telemanipulation Tasks using Small UAVs. International Journal of Social Robotics. doi.org/10.1007/s12369-021-00783-9.
Hyperspectral imaging for managing industrial hemp
Hyperspectral imaging for managing industrial hemp
Through a project funded by the NC Ag Foundation, we developed methods using hyperspectral imagery to evaluate the growth stage, cultivar, and cannabinoid content in industrial hemp flowers and leaves. This project also aimed at understanding the effects of harvest date and transplant date on biomass and cannabinoid production.
Publications:
Lu, Li, Young, Li, Linder, Suchoff (2022). Hyperspectral imaging with chemometrics for non-destructive determination of cannabinoids in floral and leaf materials of industrial hemp (Cannabis sativa L.). Computers and Electronics in Agriculture, doi.org/10.1016/j.compag.2022.107387.
Linder, Young, Li, Henriquez-Inoa, Suchoff (2022). The Effect of Harvest Date on Temporal Cannabinoid and Biomass Production in the Floral Hemp (Cannabis sativa L.) Cultivars BaOx and Cherry Wine. Horticulturae, 8(10), 959, doi.org/10.3390/horticulturae8100959.
Lu, Young, Linder, Whipker, Suchoff. (2022). Hyperspectral imaging with machine learning to differentiate cultivars, growth stages, flowers and leaves of industrial hemp (Cannabis sativa). Frontiers in Plant Science, doi.org/10.3389/fpls.2021.810113.
Linder, Young, Li, Henriquez-Inoa, Suchoff (2022). The Effect of Transplant Date and Plant Spacing on Biomass Production for Floral Hemp (Cannabis sativa L.). Agronomy, doi.org/10.3390/agronomy12081856.
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