Recreational fishing is a popular pastime in New South Wales, bringing $3.4 billion to the state annually. In NSW, fishers are obligated to land their catch. To facilitate this, fish cleaning tables have been installed along the coast. Cleaning fish generates waste, such as fish frames that wildlife can scavenge on. The ecological impact of supplemental feeding depends on its frequency and quantity. However, we have little information on this in a recreational fishing context, as its monitoring is limited to a biennial telephone diary survey with a 0.3% participation rate. Our research aims to improve catch monitoring and investigate the quantity of waste produced at fish cleaning tables. To do this, we combined artificial intelligence and fisher survey data. We deployed a stereo camera system over several months and used detection and classification models to identify and estimate the length of fish being processed at a fish cleaning table. The system first detects if a fish is present in the footage, and then tracks each fish and takes photos, which are later uploaded to the cloud to be classified with a deep learning model. Additionally, we conducted a survey to better understand how recreational fishers use cleaning tables to estimate waste produced. The survey also helped us evaluate fishers’ opinions on the use of artificial intelligence devices to monitor their catch. Overall, this research will help develop future management practices that benefit both wildlife and recreational fishing within New South Wales and may apply to other areas.