The progress of technology nowadays is incredibly and inevitably moving at a rapid pace. The innovation of it and how it helps in our lives is amazing. But these technologies are not just to help humans. There are certain technologies that use ways to help even the other species of our ecosystem. AI Tech is one of them.
From science fiction films to Stephen Hawking, there is a school of thought that says artificial intelligence (AI) could mean the end of humanity. However, conservationists are increasingly turning to artificial intelligence (AI) as a cutting-edge technological solution to biodiversity disaster and climate change mitigation.
What is AI Tech?
According to a study made by Western Governors University, artificial intelligence technology is defined as the theory and creation of digital programs capable of performing activities and solving issues that would normally need human intelligence.
Visual perception, speech recognition, decision-making, and word translation are all tasks that would typically require human intelligence, but computer programs may now use their intelligence and capability to tackle these problems.
Artificial intelligence works by processing data using complex algorithms. It uses algorithms to search through massive data sets, learning from the patterns or features in the data. The overall goal of AI is to create software that can learn about input and explain a result with its output.
5 ways AI Technology helped to save and preserve wildlife
The Guardian said that AI is assisting in the conservation of a wide range of species, including humpback whales, koalas, and snow leopards, by assisting scientists, researchers, and rangers in critical activities ranging from anti-poaching patrols to species monitoring. It is often able to do the work of hundreds of humans using machine learning (ML) computer systems that use algorithms and models to learn, understand, and adapt.
Here are the five artificial intelligence (AI) projects that are helping us understand biodiversity and species:
Stopping poaching in Zambia’s Kafue national park, which is home to nearly 6,600 African savanna elephants and encompasses 22,400 square kilometers, is a huge logistical problem. Illegal fishing in the park’s border lake, Lake Itezhi-Tezhi, is a concern, and poachers disguise themselves as fishermen to enter and exit the park undetected, typically at night.
Game Rangers International (GRI), Zambia’s Department of National Parks and Wildlife, and other partners are collaborating to create a 19-kilometer-long virtual fence across Lake Itezhi-Tezhi as part of the Connected Conservation Initiative. Every boat crossing into and out of the park, day and night, is recorded by forward-looking infrared (FLIR) thermal cameras.
The cameras, which were installed in 2019, were manually watched by rangers, who may then respond to evidence of criminal behavior. FLIR AI has now been trained to detect boats entering the park automatically, improving efficiency and decreasing the need for ongoing manual observation. Because waves and flying birds can potentially cause warnings, the AI is being trained to ignore these misleading signals.
Water loss tracking
Brazil has lost more than 15% of its surface water in the last 30 years, a disaster that has only been revealed thanks to artificial intelligence. Growing population, economic expansion, deforestation, and the deteriorating consequences of the climate catastrophe have all put strain on the country’s rivers, lakes, and wetlands.
But no one realized the scope of the problem until last August, when the MapBiomas water project, which used machine learning to process over 150,000 images generated by Nasa’s Landsat 5, 7, and 8 satellites from 1985 to 2020 across the 8.5 million square kilometers of Brazilian territory, released its findings.
Researchers would not have been able to analyze water variations across the country at the scale and level of detail required without AI. AI can also tell the difference between natural and man-made water bodies.
Knowing where whales can be found in the first step toward putting protection measures in place, such as marine protected areas. It’s difficult to see humpback whales across large oceans, yet their distinctive song can be heard for hundreds of miles underwater.
Acoustic recorders are employed to monitor marine animal populations in remote and difficult-to-access islands at National Oceanic and Atmospheric Association (Noaa) fisheries in the Pacific islands, according to Ann Allen, a Noaa research oceanographer.
She also stated that they had amassed roughly 190,000 hours of acoustic recordings over the course of 14 years. A person would have to spend an inordinate amount of time painstakingly identifying whale vocalizations.
Noaa collaborated with Google AI for Social Good’s bioacoustics team to develop a machine learning model that could recognize humpback whale singing in 2018. According to Allen, they were quite successful in recognizing humpback whale songs across their whole dataset, demonstrating patterns of their presence in the Hawaiian and Mariana islands.
They also discovered a new occurrence of humpback songs in Kingman Reef, a region where humpback presence has never been observed before. Without AI, they would not have been able to conduct a full study of their data.
Due to habitat degradation, domestic dog attacks, traffic accidents, and bushfires, Australia’s koala populations are falling rapidly. It’s difficult to save them if you don’t know how many there are or where they are. With government and Landcare Australia funding, Grant Hamilton, associate professor of ecology at Queensland University of Technology (QUT), has established a conservation AI hub to count koalas and other vulnerable creatures.
An AI technique uses drones and infrared cameras to quickly analyze infrared film and determine whether a heat signature belongs to a koala or another animal. After Australia’s severe bushfires in 2019 and 2020, Hamilton used the method to locate remaining koala populations, particularly on Kangaroo Island.
It’s a massive effort to save species on the endangered list in the Congo basin, the world’s second-largest rainforest. Appsilon, a data science firm, collaborated with the University of Stirling in Scotland and Gabon’s national parks agency (ANPN) in 2020 to create the Mbaza AI picture classification algorithm for large-scale biodiversity monitoring in Gabon’s Lopé and Waka national parks.
Conservationists had been employing automated cameras to catch African forest elephants, gorillas, chimps, and pangolins, which had to be manually identified afterward. Millions of photos could take months or years to classify, and time is of the essence in a country where poachers kill approximately 150 elephants every month.
In 2020, the Mbaza AI system was used to analyze over 50,000 photos from 200 camera traps dispersed across 7,000 square kilometers of woodland. Mbaza AI can classify up to 3,000 photos per hour with a 96 percent accuracy rate.
Conservationists can follow and monitor wildlife and notice anomalies or warning signs rapidly, allowing them to intervene fast when necessary. The algorithm can also be used without an internet connection on a regular laptop, which is useful in areas where there is no or limited internet access.
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