Ocean Ruler– an image-based, AI-driven approach to small-scale fisheries monitoring and catch size estimation

Abstract The ubiquity of data-limited, data-absent, and unmanaged fisheries around the world illustrates a significant need for enhanced monitoring of living marine resources beyond conventional agency-led programs. While quantitative stock assessments represent the gold standard for fisheries management, simple length-based datasets alone can provide important insights into fishery status and can be collected by citizen and community scientists. Here, we demonstrate the performance of Ocean Ruler, a web-based tool that uses computer vision software and digital edge detection to measure individual lengths of harvested catch from images submitted by users (e.g., fishers, scientists, fisheries managers). Specifically, we compared software-derived measurements to conventional hand measurements to estimate rates of software bias and measurement error across four fishery groups in commercial, recreational, and artisanal fisheries that operate along the coast of California, USA and Baja California, Mexico. Through collaboration with local fishing communities, we demonstrate minimal software bias across three out of the four fishery groups, with minor, yet consistent overestimation observed while using the tool to estimate finfish lengths. We also note that efforts must be made to reduce software measurement error, despite achieving acceptable levels of accuracy on average in many cases. Nevertheless, we believe our efforts represent early successes in integrating machine learning tools with citizen and community science to generate management-relevant fishery size-structure data. Such approaches, when implemented effectively, have the potential to directly support management of living marine resources, particularly in small-scale, data-limited contexts.