UDEEP: Edge-based Computer Vision for In-Situ Underwater Crayfish and Plastic Detection
This addresses environmental monitoring challenges for conservationists and policymakers in aquatic ecosystems, but it appears incremental as it applies existing AI and edge computing methods to a new domain-specific dataset.
The paper tackles the problem of monitoring invasive signal crayfish and plastic debris in UK rivers using an edge-based computer vision platform called UDEEP, which performs on-the-fly classification to provide accurate data on presence and spread, though no concrete performance numbers are reported.
Invasive signal crayfish have a detrimental impact on ecosystems. They spread the fungal-type crayfish plague disease (Aphanomyces astaci) that is lethal to the native white clawed crayfish, the only native crayfish species in Britain. Invasive signal crayfish extensively burrow, causing habitat destruction, erosion of river banks and adverse changes in water quality, while also competing with native species for resources and leading to declines in native populations. Moreover, pollution exacerbates the vulnerability of White-clawed crayfish, with their populations declining by over 90% in certain English counties, making them highly susceptible to extinction. To safeguard aquatic ecosystems, it is imperative to address the challenges posed by invasive species and discarded plastics in the United Kingdom's river ecosystem's. The UDEEP platform can play a crucial role in environmental monitoring by performing on-the-fly classification of Signal crayfish and plastic debris while leveraging the efficacy of AI, IoT devices and the power of edge computing (i.e., NJN). By providing accurate data on the presence, spread and abundance of these species, the UDEEP platform can contribute to monitoring efforts and aid in mitigating the spread of invasive species.