Every year, 650,000 tons of fishing gear is lost at sea. This gear can become tangled on rocks or coral and ensnare wildlife, who are then unable to escape.

To prevent the creation of further ghost gear, it is important to be able to identify and track the gear that is found. There is currently no easy way to do this.

What it does

Ghost Busters makes it easy to collect data on ghost gear anywhere in the world. Furthermore, it automatically queries the entries against a known database of gear, returning the most relevant results for admins to sort through.

Whenever a submission is made, Ghost Busters automatically retrieves the location of submission along with pictures and data that the user has provided. this data is used to generate a list of possible matching gear types.

Our app affords huge big data analysis opportunities as each submission is stored in our database, awaiting queries. It also offers education opportunities, by offering in-app help and tips.

How we built it

The front end of Ghost Gear was created with Android Studio, while the database is created and queried with mySQL and the admin web interface with Flask.

Challenges we ran into

This application requires many parts, from the mobile interface to the submission database to the database of known gear types. Ensuring these parts worked together was the greatest challenge our team faced.

Accomplishments that we're proud of

Ghost Gear fully realizes all features we set out to accomplish during this weekend, is largely scalable and and has huge potential for growth.

What we learned

Our team members are all new to Android Studio, and learning the ropes was an excellent experience for all of us.

What's next for Ghost Busters!

Ghost Busters has huge potential for growth!

User Accounts could be created to keep track of who is submitting the data, and initiatives could be offered to those who continue to collect.

Picture recognition technology could be used to further automate the data collection process.

The opportunity for big data analysis are huge; as the submission database grows, it will become possible for data analysts to get fast but accurate answers to difficult questions.

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