Marine biofouling poses a set of challenges to aquaculture globally and the salmon aquaculture industry specifically. Biofouling causes an accumulation of biota on pens and can lead to significant net mesh occlusion, this can reduce flow rates, risking oxygen depletion. The industry currently manages this challenge through regular cleaning of nets, with visual estimations of net occlusion an important but time-consuming task. The study aims to quantify net occlusion caused by biofouling using an image-based automated desktop application. This application binarizes images collected from cameras currently used by the industry into water and non-water pixels, calculating the percentage of net occlusion from these binary images. Accurate binarization of representative images was achieved by training a deep learning network on images collected in situ. The resulting network attained a validation accuracy of 96% and a mean test accuracy of 93%. From the test images, 98% of pixels annotated as non-water and 89% of pixels annotated as water were correctly classified by the network. This automated tool has the capacity to inform industry more accurately and quickly and therefore create a more efficient cleaning framework based on the needs of individual pens.