AI, including machine learning and deep learning, could be a game-changer in the environmental conservation space. It can be used to monitor endangered species, track diseases, and optimize crops.
AI-based technologies can also improve climate-change forecasting, air quality, and disaster response. It can help cities plan for heat waves and pollution from traffic, reduce greenhouse gas emissions by optimizing power plant operations, and detect underground leaks in water supply systems.
Artificial intelligence (AI) has the potential to revolutionize the way we approach environmental conservation. Its applications, such as machine learning and deep learning, can help monitor endangered species, track diseases, and optimize crops. With the help of AI-based technologies, it is possible to improve climate change forecasting, air quality, and disaster response.
In this article, we will explore the role of AI in environmental conservation by looking at the monitoring of salmon farms, seabed ecosystems, and water quality.
Monitoring of Salmon Farms
Salmon farms have played a key role in the growth of aquaculture, creating new jobs and exports. They also help to reduce the pressure on wild food stocks. But, there are a number of environmental challenges that the industry faces.
One of the biggest issues is sea lice, which affects both the quality and quantity of production. To counteract this problem, the Norwegian Seafood Innovation Cluster is working on an AI-based system that can detect the presence of lice and predict their spread to help farmers take appropriate defensive measures.
The system uses deep learning to analyze video images and determine the most efficient lice-eating fish. These cleaner fish, typically ballan wrasse and lumpfish, are used to eat lice off the salmon and this method has become an alternative to the use of veterinary drugs.
But, even though the process is simple, the problem with using these clean-eating fish is that not all of them are equally effective at removing lice. Consequently, the system needs to identify which ones are most efficient at this task and a number of algorithms have been developed to achieve this.
In addition, the farm has to ensure that the conditions are suitable for the fish. This includes the temperature, salinity, and dissolved oxygen levels of the water.
To monitor these factors in real-time, the company has installed a water monitoring system. It is a sophisticated solution that helps to improve yield, minimize the impact on aquatic environments and ensure a high-quality product.
Besides these, the system is capable of determining the health of each fish. This means that the system can record wounds, signs of illness, and other issues affecting individual fish on the farm, enabling targeted intervention if needed.
Another way that AI can help in the conservation of salmon is by ensuring that the fish are moved in the right direction. This is achieved by a laser camera system that can move the salmon between a net and a boat in rough seas.
In addition, the technology can also be used to ensure that the correct dosage of feed is given to the fish. This allows the farm to maximize output and reduce costs. It can also help to optimize the biomass of each fish, thereby improving rates of feed conversion and reducing waste.
Monitoring of Seabed Ecosystems
Artificial intelligence (AI) can play an essential role in environmental conservation. By integrating and processing data, AI systems can be used to identify changes in the environment that may affect biodiversity. It can also be used to prevent and detect potential threats.
Currently, many species living in the seabed are being threatened by anthropogenic activities such as oil exploration. As a result, marine scientists need more information about what inhabits the seabed in order to better understand and manage their populations.
The development of new tools to monitor deep-sea ecosystems is crucial for the protection of these habitats. The Sustainable Seabed Knowledge Initiative (SSKI) is a key step in this process. This project will use a combination of genomic and image libraries, together with artificial intelligence, to identify deep-sea species.
These libraries can be accessed by researchers and managers using app-based field tools that integrate data from the ecosystem into a coherent picture of the area. This will allow them to monitor deep-sea biodiversity and ensure that the ecosystem remains intact, which is a vital goal for global ocean governance.
Another important role of AI is in detecting illegal fishing activity. For decades, Synthetic Aperture Radar (SAR) imagery has been seen as a promising tool for identifying illegal vessels. However, this low-resolution, publicly available data is difficult for humans to process.
With help from the European Union, AI2 is developing a technology called Skylight that can quickly scan thousands of square miles to identify vessels. This has the potential to reduce IUU fishing, protect local economies, and save our oceans.
In addition to reducing IUU fishing, Skylight can also detect illegal ships and identify non-compliant ship behaviors that can threaten the health of our oceans. This can be done through a combination of artificial intelligence and computer vision.
Using an automated approach to habitat mapping can speed up the process and make it more reliable. It can also improve the accuracy of the maps, which are important for assessing environmental change over time.
The NCCOS is testing several artificial intelligence solutions for annotating underwater imagery to develop optimized habitat models. It is then comparing their performance using multiple metrics to determine which one is the most effective. The best solution will then be integrated into the NCCOS’s map-making process.
Monitoring of Water Quality
Artificial intelligence is a technology that has been proven to improve efficiency and accuracy in many industries. Its use in environmental conservation can have a significant impact on the way we monitor and respond to the environment.
Water is a vital resource for life on earth, but it is also a target for contamination due to human activities and agricultural wastes. Pollution in water causes problems for ecosystems and animals, which may lead to disease and death if not treated or prevented.
As a result, it is important to monitor water quality on a continuous basis. This can be done through the use of automated sensors and data analysis.
There are several techniques for analyzing data from these sensors, including artificial neural networks (ANN), support vector machines (SVM), and machine learning algorithms. These algorithms can be applied to monitoring systems for different water quality parameters, such as dissolved oxygen and micropollutants.
For example, AI-based models can be used to predict the occurrence of a particular bacteria, such as Staphylococcus aureus, in a given water sample. This can help managers make decisions about water treatment and prevent the spread of illness.
However, the performance of these models is often dependent on two factors: model selection and the quality of the training dataset. In addition, ANN can be susceptible to noise and nonlinear disturbances.
SVM, on the other hand, is more stable and can withstand noise. In addition, SVM has a higher generalization ability than ANN.
Furthermore, SVM is able to process large volumes of data with limited computing time and can be applied to real-time monitoring. This can be particularly useful for monitoring wastewater treatment plants and wastewater treatment plant (WWTP) operations.
Despite the challenges that still exist, AI has been shown to have significant potential for enhancing efficiency in monitoring water quality on a large scale. This can provide useful information to management teams and inform policymakers about future trends in water quality, allowing for effective conservation and management of ecosystems and water resources.
Successful implementation of AI for environmental monitoring requires an enterprise strategy that clearly defines use cases and metrics of success. In addition, it is important to ensure that projects have adequate funding and support. This may include an increased budget for research and development, as well as cooperation between different stakeholders.
Monitoring of Energy Consumption
Using artificial intelligence (AI), environmental conservationists can monitor energy consumption on a large scale and predict problems before they occur. This can reduce energy costs, increase efficiency, and improve the safety and performance of water resource management systems.
AI can also assist in monitoring energy use within factories and buildings, allowing for energy efficiency to be improved. For example, Google uses machine learning to help optimize the cooling of its data centers, which reduces energy use by up to 40 percent.
The monitoring of energy consumption could potentially play a significant role in climate change mitigation and adaptation, by helping to prevent power outages and restore power more quickly when problems arise.
A system developed by the Department of Energy’s SLAC National Accelerator Laboratory in California uses data from renewable energy sources, battery storage, and satellite imagery to identify vulnerabilities in the grid and strengthen them in advance of failures, ultimately reducing the chance that power is lost.
This is important in order to mitigate the impacts of climate change on natural systems, such as ecosystems, which may be impacted by changes in temperature, precipitation, and sea level. This approach can also help to predict potential risks to natural systems in advance of disasters, such as floods or hurricanes.
However, while the use of AI and automated systems may contribute to enhancing sustainable development, there are several factors that could inhibit their widespread adoption. For instance, there are many challenges related to the scalability and feasibility of implementing such technologies in a variety of environments.
Moreover, social factors such as reluctance among in-house employees and the lack of easily accessible data can also hinder their implementation.
As a result, it is crucial for the adoption of AI to be regulated and overseen by governments to ensure that its implementation does not inhibit the delivery of all 17 goals and 169 targets recognized in the 2030 Agenda for Sustainable Development. In this study, we conducted a consensus-based expert elicitation process to assess the impact of AI on these goals and targets.
In conclusion, AI has the potential to make a significant impact on the way we approach environmental conservation. It can help us monitor the health of our oceans, identify and prevent illegal fishing activities, and improve water quality.
The use of AI can provide us with real-time information and enable us to make informed decisions to protect our ecosystems and preserve biodiversity. The integration of AI-based technologies in the environmental conservation space is a crucial step toward a sustainable future.