Despite the recent recognition of jellyfish as an important component of marine ecosystems and existing concerns on their potential population increase, they are rarely monitored at the appropriate spatial and temporal scales. Traditional jellyfish monitoring techniques are costly and generally restrict the spatial-temporal resolution limiting the quantity and quality of monitoring data. We introduce Jellytoring 2.0, an automatic recognition tool for jellyfish species based on convolutional neural networks (CNN). We trained Jellytoring 2.0 to identify 15 jellyfish species with a global distribution. Our aim is to offer Jellytoring 2.0 as an open-access tool to serve as the backbone for a system that promotes the creation of large-scale and long-term jellyfish monitoring data. Results reveal that Jellytoring 2.0 performed well in the identification of the 15 species with average precision values ranging between 90% and 99% for most of the species. Four of the species presented slightly lower values (75%-80%). Our system was trained on a relatively small dataset, implying that additional integration of image data will further improve the performance of the CNN. We show how the application of CNNs to image data can deliver a tool that will enable the cost-effective collection of jellyfish data on larger spatial and temporal scales. For Jellytoring 2.0 to become a truly global automatic identification system, we ask scientists and nonscientists to actively contribute with jellyfish image data to extend the number of species it can identify.
Keywords: artificial intelligence AI; automatic detection.
© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.