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Objectives
Develop smart tools for accurately identifying prioritized species
Background
  • Researchers of Michigan State University, Texas A&M University, and St. Anselm College have been funded by the National Science Foundation to develop smart tools for identifying species in diverse fisheries contexts.
  • Tools under development consist of: i) a smartphone application that employs artificial intelligence (AI) to perform species identification using user-supplied photos or video, and ii) genomic tests to perform genetic species identification in the field.
  • Together, these tools could make rapid and highly accurate species identification possible without the need for specialized training or equipment.
  • Due to a variety of reasons, accurate species identification in the field (i.e., landing sites) or by observers or cameras on-board (e.g., purse-seines, longlines) is not always possible.
  • Therefore, tools that improve species identification of prioritized species in a rapid and accurate manner are desirable.
Relevance for management
  • Improved species identification during data collection programs will increase data quality provision to enhance stock assessments and other biological and ecological studies for prioritized species performed by the IATTC staff, reducing uncertainty in the scientific-advice and decision making.
  • A trained AI model could increase the effectiveness of algorithms to review records collected by Electronic Monitoring (EM) equipment in a rapid and accurate manner, and help implement EM-programs in the region.
Duration
24 months
Workplan and status
  • Year 1: Sampling and collection of tissue, photo and video collection of prioritized species by technicians in the field and on-board observers or EM-cameras to improve genetic analysis and the training of the AI model, respectively.
  • Year 2: Beta testing of smartphone application and rapid genetic tests.
  • These activities will require the collaboration of national authorities and fishing industry.
External collaborators
Michigan State University, Texas A&M University, and St. Anselm College, fishing industry, CPCs
Deliverables
  • Improved smartphone application that employs an AI model to perform species identification using user-supplied photo or video.
  • Improved genomic tests to perform genetic species identification in the field.
  • Improved AI algorithm to review EM data in a rapid and accurate manner.
  • Dissemination material (e.g., reports, presentations) for the Bycatch Working Group, the SAC, the Tuna Conference, and other meetings of interest.