Plastic pollution is a global problem that affects millions of animals, especially marine birds. According to a study by the University of Tasmania, more than 60% of seabird species have ingested plastic, and this number is expected to rise to 99% by 2050.
Plastic ingestion can cause various health issues for birds, such as reduced stomach capacity, internal injuries, malnutrition, and even death.
But why do birds eat plastic in the first place? And how can we prevent them from doing so? These are some of the questions that scientists are trying to answer with the help of machine learning, a branch of artificial intelligence that enables computers to learn from data and make predictions.
Machine Learning Can Reveal the Patterns of Plastic Ingestion
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One of the challenges of studying plastic ingestion in birds is the lack of data. It is difficult and costly to monitor the behavior and diet of wild birds, especially in remote areas.
Moreover, different bird species may have different preferences and risks of eating plastic, depending on their feeding habits, habitats, and migration routes.
This is where machine learning can help.
Machine learning can analyze large and complex datasets, such as satellite images, GPS tracking, and ocean currents, to identify the patterns and factors that influence plastic ingestion in birds.
For example, a machine learning model can predict which areas have a high concentration of plastic debris, and which bird species are more likely to encounter them. This can help scientists and conservationists to target their efforts and resources more effectively.
Machine Learning Can Help to Reduce the Attractiveness of Plastic
Another challenge of studying plastic ingestion in birds is the lack of understanding of the underlying mechanisms.
Why do birds mistake plastic for food? What makes plastic appealing to them? These are some of the questions that scientists are trying to answer with the help of machine learning, especially computer vision and deep learning.
Computer vision is a field of machine learning that enables computers to process and understand visual information, such as images and videos.
Deep learning is a subset of machine learning that uses neural networks, inspired by the human brain's structure and function, to learn from large amounts of data and perform complex tasks.
By using computer vision and deep learning, scientists can simulate the visual perception of birds and test how they respond to different types of plastic.
For example, a deep learning model can generate realistic images of plastic items that vary in shape, color, size, and texture, and measure how likely they are to attract the attention of birds.
This can help scientists to understand the cognitive and sensory processes that lead to plastic ingestion in birds, and to design solutions to reduce the attractiveness of plastic.
Machine Learning Can Make a Difference for the Birds and the Planet
Plastic pollution is a serious threat to the biodiversity and health of marine birds, and to the environment as a whole.
Using machine learning, scientists can gain new insights and knowledge about the causes and consequences of plastic ingestion in birds, and develop innovative and effective strategies to prevent and reduce it.
Machine learning can make a difference for the birds and the planet, and help us to protect and conserve these amazing creatures.
