We propose an innovative yet straightforward approach to developing and delivering a readily deployable product to support combating IUU. We combine newly available low-cost hyperspectral sensors with state-of-the-art deep learning-based real-time data processing and deploy it on high-availability commodity cloud computing hardware - our solution is compatible with both existing systems as well as a new generation of proposed low-cost/high-quality hyperspectral imaging systems. We propose to develop a small CTIS objective compatible with the Raspberry Pi NOIR Camera Modules; to create an inexpensive high-resolution hyperspectral imaging system built from COTS components -- this will enable cheap and scalable high-resolution hyperspectral imaging for widespread IUU data collection. We also apply modern deep learning-based data processing techniques to fish and seafood hyperspectral datasets (existing and generated with the proposed Raspberry Pi Hyperspectral Module) to the specific problems associated with IUU (e.g. fish species classification, detection of pharmaceuticals/chemicals, adulteration, fraud, origin classification, etc. Finally, we deliver a truly cost-effective and scalable solution to combat the growing problem of IUU that uses inexpensive and ubiquitous cloud computing to deploy our DCNN-based model combined with cheap CTIS hyperspectral sensors (with Raspberry Pi, mobile phones, etc.) and existing table-top systems.
