Key takeaways
- Computer vision can identify material types, contamination and fill levels from container photos.
- Combined with invoices and weight tickets, photos turn into a continuous waste audit.
- The main use cases: catch missorting, prove material quality to buyers, and spot half-empty lifts you pay for.
- Photos are evidence: they support downgrade disputes and compliance audits.
- Start with your five most expensive or most valuable containers.
What a photo can tell you
Signal
Business use
Material type
Is the container the stream it is billed as?
Contamination
Missorting before the buyer finds it
Fill level
Paying for half-empty lifts
Material quality
Evidence for price negotiation and downgrade disputes
From photos to an audit
- Photograph each container at pickup (operators or drivers).
- AI classifies material, contamination and fill level.
- Match with invoices and weight tickets per lift.
- Flag anomalies: wrong stream, low fill, contamination, unexpected charges.
- Report monthly per stream: cost, revenue, diversion.
Do a first manual round with our industrial waste audit checklist.
Where it pays back fastest
- Sites with many containers and frequent lifts.
- Recyclable streams sold to buyers who downgrade.
- Multi-site groups needing comparable data.
- Data centers during deployment waves (packaging).
Wastetide
Scan, analyse, monetize
Wastetide assesses material quality from photos, audits invoices and contracts, and matches recyclables with buyers. Try it free.
FAQ
How accurate is AI waste recognition from photos?
It is good at material families, visible contamination and fill levels. Weights and chemical composition still come from tickets and lab analysis.
Do we need new hardware?
No: a smartphone photo per container is enough to start.