Like all harmful algal blooms (HABs), the persistent prevalence of toxin-producing cyanobacteria in the freshwater habitats of the Lake Champlain basin presents growing risks to Vermont's aquatic ecosystems, public health, and our local economy.
A ¶¶Òõ̽̽ research project led by electrical engineering professor Tian Xia and mechanical engineering professor Dryver Huston, and Ph.D. student at Xia’s , with collaboration from ¶¶Òõ̽̽'s , is pioneering new technologies to expand the real-time monitoring capabilities of uncrewed aerial vehicles (drones), which are currently being used to determine the distribution and movement of HABs like cyanobacteria.
To address these challenges, the groundbreaking drone-based monitoring platform integrates advanced sensors, near-infrared imaging, GIS mapping capabilities, and real-time data transmission to track bloom dynamics and water quality indicators. Hagh has further enhanced the system's capabilities by designing and crafting unique 3D-printed water samplers, which are grouped with sensors in a wireless apparatus suspended below the drone.
The comprehensive platform developed by the ¶¶Òõ̽̽ researchers bridges the gap between remote sensing and in situ analysis, offering a versatile tool that improves the efficiency and expense of existing monitoring systems. Currently, these systems involve a time-consuming combination of manual and autonomous systems that lack real-time data-sharing features.
In the past year, Hagh has received honors at two professional conferences where he presented the team's innovative system. Hagh was awarded the top honor in May of 2023 for his presentation at the Rochester Institute of Technology (RIT) hosting the
At last month's , organized by the American Water Resources Association, University Council on Water Resources, and National Institutes for Water Resources, Hagh was awarded 2nd Place in the Student Oral Presentations for his abstract titled "A Wireless Drone System for Real-Time Monitoring, Sampling, and Imaging of Water Bodies with HABs."
The idea of integrating sensor and sampling technologies in an autonomous drone arose when Xia and his colleague, Mechanical Engineering Professor Dryver Huston, attended a conference and realized the potential of drones to improve the efficiency of HAB monitoring in remote watersheds.
The professors sponsored three successive Senior Experience in Engineering Design (SEED) undergraduate teams to develop drone and sensor technology prototypes before applying for and receiving funding from the to further optimize the design.
The project's research and design team grew to include experts from across campus. Joining Hagh and Xia in electrical engineering is Ph.D. student , and a group of researchers and technicians from the Spatial Analysis Laboratory, including , , , Spencer Karins, , and the late Director of SAL, Jarlath Patrick O'Neil-Dunne. Also contributing are Ana M. Morales-Williams, an assistant professor in The Rubenstein School of Environment and Natural Resources (RSENR), , an assistant professor of environmental studies and science at Saint Michael's College, and Professor of Mechanical Engineering Dryver Huston.
The system architecture developed by the researchers integrates sensors, automated water samplers, real-time wireless data transmission, micro-SD card backup storage, true-color and near-infrared (NIR) cameras, and GIS mapping. The tethered sensor and sample apparatus features four sensors to capture Temperature, pH levels, Turbidity (measuring water clarity and quality), and total dissolved solids (TDSs).
The automated remote sampling equipment designed by Hagh features four separate containers, each with its motorized cap that seals the sample from contamination, which was a concern for alternative systems using pumps and shared tubing. As a result of this unique design, the drone can collect several samples from areas of interest during the same flight.
Atop the drone sits the innovative brains of the system. Hagh and Amngostar, who share research interests and marriage vows, used HP-CAS Lab equipment to design and fabricate the LoRaWAN (Long Range Wide Area Network) printed circuit board (PCB). Along with sensor electronics, the circuit board includes a precise GPS module and a slot for a backup micro-SD card.
Recent field tests conducted at Shelburne Pond—a nearby nutrient-rich body of water hosting large algae blooms—have demonstrated the efficacy of the system which currently uses two drones, a modified Aurelia X4 drone containing the sensor node PCB with the tethered sensor and sampling system, and second Mavic 3M drone to capture multispectral imagery.
Suspended about two and a half meters below the drone, the pilot positions the drone over the test area and submerges the sensing apparatus at a depth of one meter for about 2 minutes to collect the data and water samples, if desired. The captured information is wirelessly shared over the LoRaWAN where the system can synchronize the image and sensor data.
Future plans for the system look to expand the drone's capabilities and range while incorporating AI to allow for truly autonomous operation. The open architecture of the system also allows for customization based on the researcher's needs and could include additional sensors, such as depth sensors, dissolved oxygen (O2), or carbon dioxide (CO2), nutrient sensors, fluorometer, and heavy metal sensors.
"Our wireless sensing technology allows for efficient water sampling and studying water conditions in remote locations in real-time," said Xia. "With our goal of fully autonomous flights, the system will have the intelligence to monitor water conditions and detect HABs with minimum to no human intervention."
Sample Collection Video
Video provided by Soheyl Faghir Hagh.