EcoSort

Inspiration

Modern cities produce massive amounts of waste every day, yet most of it is still sorted manually or not sorted at all. Landfills continue to grow while recyclable materials are lost simply because they are mixed, degraded, or too difficult to process.

This raises a critical gap: despite advances in AI and automation, waste segregation—the first and most important step in recycling—remains inefficient and inconsistent.

What We Built

EcoSort is an automated waste sorting system that does more than classify waste—it actively separates it.

The system uses a combination of computer vision and sensor fusion to identify waste even in challenging conditions such as degraded, dirty, or visually ambiguous materials. Once identified, EcoSort is designed to trigger physical sorting mechanisms, enabling real-world waste segregation rather than just digital classification.

EcoSort operates in three modes:

Sensor-only mode for low-visibility or degraded waste
Vision-only mode when sensors are unavailable
Fusion mode combining both for maximum reliability

This makes the system adaptable across environments—from households to large-scale waste facilities.

How We Built It

We developed a multi-stage pipeline that integrates:

Computer vision for visual identification
Sensor-based classification for non-visible properties
A fusion layer that improves reliability by combining both

To support real-world deployment, we also built:

A mapping system to estimate object position and depth using a single camera
A simulation model demonstrating how EcoSort integrates with physical sorting mechanisms

Challenges

One of the biggest challenges was the lack of physical hardware. To address this, we designed and simulated the system in a way that remains realistic and directly transferable to real-world deployment.

Another challenge was ensuring reliability across different waste conditions, especially when dealing with degraded or mixed materials.

What We Learned

We learned that solving real-world problems is not just about building models, but about designing complete systems that can operate reliably in imperfect environments.

EcoSort combines multiple technologies into a single pipeline that prioritizes robustness and practical deployment.

Impact

Unlike systems that only identify waste, EcoSort is designed to act on it.

By automating the segregation process:

Recycling efficiency can be significantly improved
Landfill dependency can be reduced
Manual sorting risks can be minimized

EcoSort has the potential to scale across urban waste systems, enabling more efficient and sustainable waste management.

Conclusion

EcoSort bridges the gap between waste identification and waste action.

It transforms classification into a real-world process—bringing automation to one of the most overlooked yet critical parts of environmental sustainability.