Harmful algal blooms (HAB) negatively impact the environment, fisheries, and health of waterfront communities

The earlier a potential bloom is detected, the more time there is to plan and execute a response, the smaller the remedial action required, and the less the expense and environmental impact. Habcamera is an artificial intelligence-enhanced combination of light sources, lenses, cameras, and sensors. IT measures photosynthetic pigments and observes morphology to automatically identify and count HAB specimens in any water source. Environmental sensors for nutrients, temperature, light, and other growth variables provide a complete solution for the earliest possible prediction of bloom risk. The product is unique in taking in-situ multispectral images and analyzing them in real time. IT brings the lab to the field in a compact form that scientists can deploy on moorings or autonomous underwater vehicles for constant monitoring. Summary of anticipated results linespect anticipates producing a novel product that 1) predicts HAB bloom potential a week earlier than any other approach, 2) automates specimen counting to relieve the tedious practice of manual counting, 3) does not disturb the interactions between phytoplankton, zooplankton, and other components of the pelagic environment in their natural state, and 4) reduces cost to allow deployment of numerous instruments to increase spatial density of measurements. The habcamera will provide earlier, more accurate, cost-effective HAB predictions. Adapting the flexible optics and retraining the core AI technology could also address numerous other environmental issues. These include detecting and quantifying ocean microplastics, detecting bacteria producing off-flavors in catfish farms, optimizing algae production, confirming wastewater treatment, testing water quality, and enhancing aquatic research.