High-Level Project Summary
We are using NASA's API, TensorFlow API, toxicological threats of plastic EPA, NASA earth data, national oceanic and atmospheric administration data, and Oceans.NASA.data.With the aid of AI and ML, especially deep learning, herein, we propose a fast scalable, and potentially cost-effective method for automatically identifying, floating marine plastics. we are using a region-based convolutional neural network and real-time footage besides the NASA's data to monitor, detect and quantify plastic debris. It will help us to monitor and detect plastic automatically . Further, our goal is to detect and quantify plastics that are recyclable thus it also going to help us economically.
Link to Project "Demo"
Link to Final Project
Detailed Project Description
A man traveling in a car throws a plastic bottle in the road. A boy noticed that and started thinking about it. How much plastic waste is generated every day and where does it go. it sounds simple right? but it is not.
the journey of this plastic bottle is going to end up in the ocean. Now, we produce about 300 million tons of plastic waste, and At least 8 million tons of plastic end up in our oceans every year and makeup 80% of all marine debris from surface waters to deep-sea sediments. it is estimated that about 80% of marine debris originates as land-based trash and the remaining 20% is attributed to at-sea intentional and accidental disposal or loss of goods and waste. plastic marine debris is of particular concern due to its longevity in the marine environment, the physical and chemical hazards it presents in marine and birdlife. Moreover, it is frequently mistaken as food by birds and fishes.
Plastics are inexpensive, lightweight, and durable, but their durability poses a problem because they take several hundred years to decompose. The longer plastic is in the water the more weathered and fragmented.
Marine debris is one of the most pervasive and pernicious global threats to the health of the world’s coastal areas, oceans, and waterways. It is an issue of growing local, regional, national, and international concern. Marine debris can injure or kill marine and coastal wildlife; damage and degrade habitats; interfere with navigational safety; cause economic loss to fish and maritime industries, degrade the quality of life in coastal communities; and threaten human health and safety.
plastic pollution affects at least 700 marines species, while some data suggest that at least 100 million marine mammals are killed each year from plastic pollution.
In 2019, about 90 million tons of CO2 were added to the air through burning plastic debris.
To solve this major issue we need a lot of tools to measure the amount and classify the types of plastic. That plastic should be taken under recycling process. Estimating the volume of macro plastic which dot the world's oceans is one of the most pressing environmental concerns of our time. The prevailing method for determining the amount of floating plastic debris, usually conducted manually, costly, and rather limited in coverage.
Our project is finding some solution for that.
We are using NASA's API, TensorFlow API, toxicological threats of plastic EPA, NASA earth data, national oceanic and atmospheric administration data, and Oceans.NASA.data. We used google collaboratory and VS code as our coding platform. We actually used python language to solve this problem.
With the aid of AI and ML, especially deep learning, herein, we propose a fast scalable, and potentially cost-effective method for automatically identifying, floating marine plastics. we are using a region-based convolutional neural network and real-time footage besides NASA's data to monitor, detect and quantify plastic debris. It will help us to monitor and detect plastic automatically. Further, our goal is to detect and quantity plastics that are recyclable thus it also going to help us economically.
Our uniqueness: We are offering scientists get a better understanding of this problem and protect our earth from plastic disasters. They can see the first-person view of debris clearly. Moreover, we can illustrate to people the bad impacts and results of plastic wastes. We are also trying to extend AI all around the world by solving this major issue.
Galactic Impact: impacting mass population Experiencing real-time plastic detection automatically in zero cost environment and also our project is eco-friendly.
Together we can make the mother earth best place.
Space Agency Data
1. Toxicological threats of Plastic / EPA
2. Unwelcome enrichment in the artic/ NASA Earthdata
3. Scientists use NASA Data to Track Ocean Microplastics From Space
4. Marine debris Fast Facts / NOAA
5. Marine Debris: Garbage patch Experiments (drifters and model)/ NOAA
6. Marine Debris Impacts/ U.S Department of the interior
7. The United States federal strategy for addressing the global issues of marine litter
8. Nasa's API
9.TensorFlow API
10. Oceans.NASA.Data
Hackathon Journey
This Hackathon is our very first entry into the coding world. We enjoyed it a lot though it was not easy at all. This virtual event also helped us a lot to engage ourselves in solving real-world problems with friends. Moreover, The mentorship program was undoubtedly awesome and remarkable.
This world's largest Hackathon also helps us to think out of the box and teaches us about the ups and downs of life.
Overall, this experience was one of the best experiences for us. We just loved it.
References
1. Toxicological threats of Plastic / EPA
2. Unwelcome enrichment in the artic/ NASA Earthdata
3. Scientists use NASA Data to Track Ocean Microplastics From Space
4. Marine debris Fast Facts / NOAA
5. Marine Debris: Garbage patch Experiments (drifters and model)/ NOAA
6. Marine Debris Impacts/ U.S Department of the interior
7. The United States federal strategy for addressing the global issues of marine litter
8. Nasa's API
9.TensorFlow API
10. Oceans.NASA.Data
Tags
#Machine learning ,#Artificial Intilligence, #Marine debris
Global Judging
This project has been submitted for consideration during the Judging process.