AI can transform many important industries that pursue Sustainable Development Goals. Active use of natural resources and high production rates have led to environmental degradation within the world. AI technologies are often actively wont to solve problems associated with global climate change and therefore the preservation of biodiversity.
The key advantages of using AI in ecology are the power to accelerate information transfer and access, automate routine operations, perform complex tasks, process large arrays of territories inaccessible to humans, and more.
Here are some prominent samples of AI-based apps that help preserve our nature:
– Ocean pollution:
Autonomous boats are equipped with meteorological and oceanographic sensors, because of which scientists collect data on the state of the ocean and global climate change.
– Conservation of biodiversity:
UAV (drone) flies above the surface of the water, trying to find whales, and picks up drops of water exhaled by animals, which are then sent for analysis.
A system for monitoring agricultural land. Its main element may be a robot, which is found directly on the sector and monitors crops, then photos and video materials are processed by the software platform.
– Municipal solid waste management:
Drones are designed for sorting garbage. they’re ready to recognize the sort of waste and choose what to try to to with it. The accuracy of their actions is 98%.
– Air pollution:
The mobile application shows users the extent of pollution, which is decided by the photos uploaded daily by users within the database.
– Natural disasters:
An app uses AI to timely predict and evaluates the consequences of natural disasters.
Finally, we would like to say that AI can make a big contribution to achieving the goals of sustainable development. Generally speaking, the most advantages of introducing AI technologies are concentrated around reducing production costs, simplification of general trading procedures, and simple compliance control processes. it’s important that each one of the above advantages is often achieved without a systemic risk of human error thanks to machine learning technology and deep learning technologies.