Rapid quantification of biofouling with an inexpensive, underwater camera and image analysis.
To reduce the transport of potentially invasive species on ships' submerged surfaces, rapid-and accurate-estimates of biofouling are needed so shipowners and regulators can effectively assess and manage biofouling. This pilot study developed a model approach for that task. First, photographic images were collected in situ with a submersible, inexpensive pocket camera. These images were used to develop image processing algorithms and train machine learning models to classify images containing natural assemblages of fouling organisms. All of the algorithms and models were implemented in a widely available software package (MATLAB©). Initially, an unsupervised clustering model was used, and three types of fouling were delineated. Using a supervised classification approach, however, seven types of fouling could be identified. In this manner, fouling was successfully quantified over time on experimental panels immersed in seawater. This work provides a model for the easy, quick, and cost-effective classification of biofouling.