Algal blooms can be a terrible occurrence. When algae bloom in slow-flowing or stagnant water, the organisms extract oxygen from the water, causing the death of marine life on a huge scale. The Electronics and Telecommunications Research Institute (ETRI) in Korea mentions that they managed to develop a technology that incorporates hyperspectral cameras along with a drone. The technology will help scientists understand and possibly predict the occurrence of algal blooms to deal with the potentially toxic situation.
Artificial Intelligence to the Rescue
Currently, it takes a few days to collect samples and analyze them to figure out whether a potential algal bloom is in the offing. The process is far too slow to predict changes in time to deal with them appropriately. ETRI uses the drone to get access to bodies of water and collect as much data as they can using the hyperspectral cameras. The collected data is then analyzed by an AI setup to conclude whether a threat exists. Drones are far more cost-effective for the monitoring process. While similar image capture can be done through satellite or aircraft, drones can get closer to the surface of the water. Additionally, drones cost less to operate than aircraft or spacecraft.
Hyperspectral Photography a Game-Changer
Current remote-sensing techniques with standard camera lenses break the light coming into the camera into three distinct colors: red, green, and blue (RGB). Hyperspectral lenses are more discerning with the incoming light. They can break light up into 200 or more distinct wavelengths, allowing for a more in-depth analysis of the photograph. Using the camera on a drone, an AI can quickly determine whether the algal bloom levels are acceptable or not. Using the light spectrum that blue-green algae reflect, the lens can categorize a standing body of water as “Attention,” “Warning,” and “Outbreak.” The system allows for the study of blue-green algae migration patterns, spread, and distribution across bodies of water. ETRI intends to make its system the most accurate method of monitoring blue-green algae to predict potential blooms before they happen.