Tech innovators partner to use AI to save coral reefs

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CORAL reefs are also affected by COVID-19.

Before COVID-19 however, thousands of coral reefs and reef-rich areas have been destroyed by man’s encroachment, including marine accidents such as that in the Tubataha Reef involving a U.S. warship. With the reduction of tourists flocking to dive sites and coral reefs, due to the coronavirus pandemic, it is expected that these wonderful marine ecosystems will flourish again.

Since last year, AccentureIntel and the Sulubaaí¯ Environmental Foundation came together to develop a new artificial intelligence (AI) powered solution to monitor, characterize and analyze coral reef resiliency. They call it Project: CORaiL, and it has been collecting data needed to make decisions on how to best manage the reefs since May 2019.

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Project: CORaiL is an example of how AI and edge computing can be used to help researchers monitor and restore the coral reef.

“We are very proud to partner with Accenture and the Sulubaaí¯ Environmental Foundation on this important effort to protect our planet,” said Rose Schooler, corporate vice president in Intel’s sales and marketing group.

Engineers from the three companies implemented an artificial, concrete reef–called a Sulu-Reef Prosthesis (SRP)–to provide support for unstable coral fragments underwater. Designed by Sulubaaí¯, the SPR was placed in the reef surrounding the Pangatalan Island in the Philippines. Fragments of living coral were planted on it and will grow and expand, providing a hybrid habitat for fish and marine life.

“Artificial intelligence provides unprecedented opportunities to solve some of society’s most vexing problems,” said Jason Mitchell, a managing director in Accenture’s Communications, Media & Technology practice and the company’s client lead for Intel.

A critical element of Project: CORaiL was to identify the number and variety of fish around a reef, which serve as an important indicator of overall reef health.

Traditional coral reef monitoring efforts involve time-intensive, disruptive and even dangerous, human divers manually capturing video footage and photos. Now using AI, engineers strategically placed intelligent underwater video cameras around the SRP.

Equipped with the Accenture Applied Intelligence Video Analytics Services Platform (VASP), the AI cameras detect and photograph fish as they pass by. Powered by Intel technologies Xeon, FPGA Programmable Acceleration Cards and Movidius VPU, it can accurately count and classify marine life.

The data is then sent to a surface dashboard, providing analytics and trends to researchers on the ground in real-time. Since the solution was deployed, it has collected roughly 40,000 images, which researchers have used to gauge reef health in real-time, enabling them to make data-driven decisions that will help the reef develop.

“The value of your data depends on how quickly you can glean insights to make decisions from it. With the ability to do real-time analysis on streaming video, VASP enables us to tap into a rich data source – in effect doing ‘hands on’ monitoring without disrupting the underwater environment” said Athina Kanioura, Accenture’s chief analytics officer and Accenture Applied Intelligence lead.

“Our ecosystem of corporate and social partners for this ‘AI for social good’ project proves that strength in numbers can make a positive environmental impact,” Mitchell said emphasizing the collaboration between the two technology companies and social impact organization.

Engineers from Accenture and Intel are already at work on the next-gen Project: CORaiL prototype.

This new automated marine device will include an optimized “convolutional neural network” (basically an independent AI device) and a backup power supply. The team is also looking into using infra-red cameras which will enable videos at night to create a complete picture of the coral ecosystem. Additional uses could include studying the migration rate of tropical fish to colder countries and monitoring intrusion in protected or restricted underwater areas.

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