# Multispectral AI transforms plastic waste recycling and sorting

- Date: 2025-07-01
- Who: AI Singapore (AI research and talent-development organisation) · AI Singapore
- Source: https://www.youtube.com/watch?v=CCRKmYRFCKg
- sgai: https://sgai.md/videos/v048/
- License: sgai-authored content (summaries, translations, analysis) is CC BY 4.0 — attribute and link to sgai.md. Verbatim source text (Hansard, speeches, transcripts, policy documents) remains © its original rights holders and is reproduced for reference only. Terms: https://github.com/meltflake/sgai/blob/main/DATA-LICENSE.md

## Why it matters

A recycling rate of only 5% in 2023 exposed shortfalls in the circular economy; the AISG 100E project explored deployment pathways for industrial robots for AI Singapore

## Summary

An AISG 100E project applies multispectral AI to convert plastic-waste sorting from a manual process to an automated one, addressing a long-standing pain point in the recycling industry.

## Key points

- Plastic is one of Singapore's top four waste streams; in 2023 its recycling rate was 5%, the lowest of all categories.
- An AISG 100E project uses multispectral AI to identify plastic types on conveyor belts and distinguish clean bottles from contaminated ones.
- Self-supervised learning cuts labelled-data needs by 90%; the system runs inside a robotic platform built for real recycling lines.

## Full text

© AI Singapore — reproduced for reference only.

Plastic waste is one of the top four waste streams generated in Singapore. Yet, it had the lowest recycling rate, only 5% in 2023. A critical step in the recycling chain is the sorting of plastics. In the current system, recyclables are first disposed of in blue bins and transported to materials recovery facilities. There, materials are manually sorted and bailed for shipment overseas. However, manual plastic sorting remains inefficient. It is slow, labor intensive, errorprone, and poses health risks to workers. Introducing a novel approach that uses multisspectral AI technology to transform the plastic sorting process. By capturing reflectance patterns across different wavelengths, the system enables accurate identification of plastic types, even as materials move along a conveyor belt.

Our self-supervised learning technology achieves high classification performance as compared to existing state-of-the-art while reducing the need for labeled data by 90%. To further improve sorting quality, the AI system is also capable of distinguishing between clean and contaminated bottles, ensuring that only recyclable materials continue down the processing line. These capabilities are integrated into a robotic platform designed for seamless operation in real world environments. In practice, the system automatically identifies and sorts plastic waste in real time, minimizing the need for human intervention while maintaining high precision. Our proposed multisspectral AI technologies can automate passive weight sorting, reducing reliance on manual sorting.

This technologies decreases manpower cost and enables large scale plastic recycling. We'll continue to scale up the system and cooperate with industrial partners and government agencies to transform plastic recycling.