Maka Niu: A low-cost, modular imaging and sensor platform to increase observation capabilities of the deep ocean

• Published in: Frontiers in Marine Science Vol. 9
• Main Authors: Novy, Dan, Kawasumi, Lui, Ferguson, Jon, Sullivan, Margaret, Bell, Peter, Chow, Jennifer S., de Sousa, João Borges, Cantner, Kat A., Woodward, Benjamin, Adams, Allan, Bell, Katherine L.C.
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 17-11-2022
• Subjects: deep sea; exploration; machine learning; participatory design; technology; user-centered design
• ISSN: 2296-7745; 2296-7745
• DOI: 10.3389/fmars.2022.986237

The deep sea (>200 m) is vast, covering 92.6% of the seafloor and largely unexplored. Imaging and sensor platforms capable of surviving the immense pressures at these depths are expensive and often engineered by individuals and institutions in affluent countries as unique, monolithic vehicles that require significant expertise and investment to build, operate, and maintain. Maka Niu was co-designed with a global community of deep-sea researchers. It is a low-cost, modular imaging and sensor platform that leverages off-the-shelf commodity hardware along with the efficiencies of mass production to decrease the price per unit and allow more communities to explore previously unseen regions of the deep ocean. Maka Niu combines a Raspberry Pi single-board computer, a Pi Camera Module V2, and a novel pressure housing and viewport combination capable of withstanding 1,500 m water depth. Other modules, including high-lumen LEDs, can be engineered to use the same battery charging and control system and form factor, allowing for an ever-increasing number of capabilities to be added to the system. After deployment, imagery and sensor data are wirelessly uploaded to Tator, an integrated media management and machine learning backend for automated analysis and classification. Maka Niu’s mobile mission programming and data management systems are designed to be user-friendly. Here, Maka Niu is described in detail along with data and imagery recorded from deployments around the world.