Harmful algal blooms negatively impact the environment, economy, and health of waterfront communities

The earlier a potential bloom is detected, the more time to plan and execute a response, the smaller the remedial action required, and the less the expense and environmental impact. The proposed submersible camera combines multiple spectral images with artificial intelligence to identify and count HAB species. Adding environmental sensors for nutrients, temperature, light, and other growth variables provide a complete solution for the earliest possible prediction of bloom potential. The camera has a large field of view and frequent sampling to optimize the HAB detectability limit. 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 can be deployed on autonomous underwater vehicles. The habcamera will provide earlier, more accurate, and more cost-effective predictions. Adapting the flexible