FishEye.ai

• 

  An example of Indonesia shrimp pond distributions

• 

  An example of shrimp pond that's NOT converted from mangrove forest

• 

  An example of shrimp pond that's converted from mangrove forest

Inspiration

The rapid growth of aquaculture has brought, unfortunately, a significant conversion of mangrove forest (“Green Belt”) into seafood farming sites.

For many downstream buyers, they have no idea whether they are contributing to this practice of not-so-sustainable aquaculture.

What it does

Building application & Artificial Intelligence to empower traceability and sustainability of a shrimp pond in all around Indonesia whether it is converted from a mangrove forest or not. In a hope, we can:

1. Increase the sustainability awareness of the shrimp farmers in the near future.
2. Giving more traceability for a seafood product to the end consumer
3. Increase a practice of sustainable aquaculture all around the world (long-term business)

How we built it

Challenges we ran into

Although if we want to, the amount of data are so large and difficult to obtain regarding the land current state and its historical development

Accomplishments that we're proud of

A.I.'s accuracy currently is 86% and the scalability of the project.

What we learned

1. We find out that the AI not only can detect shrimp ponds but also fish ponds.
2. The shrimp pond location data throughout Indonesia can be utilized even more for a better decision making in trading activity.
3. We find several locations in Indonesia that are becoming more conscious and start planting mangroves in coastal areas.

What's next for FishEye.ai

1. Increasing A.I.'s accuracy by getting more data
2. Improving API so other applications can use
3. Working together with government and other stakeholders to increase the practice of sustainable aquaculture

Built With

• javascript
• machine-learning
• python