Sustainable Recreational Fishing Using a Novel Electrical Muscle Stimulation (EMS) Lure and Ensemble Network Algorithm to Maximize Catch and Release Survivability

TL;DR

This paper introduces a sustainable fishing system combining a novel electrical muscle stimulation lure and an AI-powered underwater monitoring app to reduce fish injury and overfishing, promoting eco-friendly recreational fishing practices.

Contribution

It presents a new electrically stimulated fishing lure and an ensemble neural network for fish classification and regulation compliance, enhancing sustainability and safety in recreational fishing.

Findings

Electrical stimulation lure supports safe reeling tension of 2N

Underwater camera and neural network accurately classify fish species

System reduces catch and release mortality and regulatory violations

Abstract

With 200-700 million anglers in the world, sportfishing is nearly five times more common than commercial trawling. Worldwide, hundreds of thousands of jobs are linked to the sportfishing industry, which generates billions of dollars for water-side communities and fisheries conservatories alike. However, the sheer popularity of recreational fishing poses threats to aquatic biodiversity that are hard to regulate. For example, as much as 25% of overfished populations can be traced to anglers. This alarming statistic is explained by the average catch and release mortality rate of 43%, which primarily results from hook-related injuries and careless out-of-water handling. The provisional-patented design proposed in this paper addresses both these problems separately First, a novel, electrical muscle stimulation based fishing lure is proposed as a harmless and low cost alternative to sharp hooks. Early prototypes show a constant electrical current of 90 mA applied through a 200g European perch's jaw can support a reeling tension of 2N - safely within the necessary ranges. Second, a fish-eye camera bob is designed to wirelessly relay underwater footage to a smartphone app, where an ensemble convolutional neural network automatically classifies the fish's species, estimates its length, and cross references with local and state fishing regulations (ie. minimum size, maximum bag limit, and catch season). This capability reduces overfishing by helping anglers avoid accidentally violating guidelines and eliminates the need to reel the fish in and expose it to negligent handling. IN conjunction, this cheap, lightweight, yet high-tech invention is a paradigm shift in preserving a world favorite pastime; while at the same time making recreational fishing more sustainable.