Automated Secchi disk depth measurement based on artificial intelligence object recognition
Abstract
Water transparency affects the degree of sunlight penetration in water, which is important to many water quality processes. It can be visually measured by lowering a Secchi disk (SD) into water and recording its disappearance depth -the Secchi disk depth (SDD). High frequency SDD measurement is manpower intensive, precluding better understanding of the daily and diurnal variation of water transparency. For the first time, an artificial intelli-gence based object detection algorithm was employed for the automatic detection of SD from images, mimicking SDD measurement by human eyes. The trained model was validated on a large number of images (about 2000 for a single day in daytime) obtained from a remote-controlled imaging system in a fish farm in a Hong Kong embayment, demonstrating high detection accuracy of 93 %. The work opens up opportunities in the nowcast and forecast of short-term water quality changes (e.g. algal blooms) in coastal waters.