Greyparrot AI Waste Recognition System is deployed globally on moving conveyor belts in MRF's and PRF's, automating waste composition analysis to monitor, audit, and sort waste at scale. Greyparrot empowers waste managers, packaging producers and regulators with actionable insights to increase recycling rates, reduce the cost of manual sampling, enhance product quality and thereby divert valuable resources from landfill and incineration. Greyparrot also collaborates with equipment suppliers to integrate AI machine learning with existing automated waste sorting infrastructure. The company is committed to unlocking the financial value of waste, which will, in turn, support our transition to a circular economy.
The Greyparrot AI Waste Recognition System consists of two parts, the AI monitoring unit, which is installed on conveyor belts to capture and analyse composition data without the need for digital watermarking and a Waste Analytics Dashboard that provides unique insights from that data in real time. This allows us to monitor 100% of waste streams across all areas of a facility (MRFs, PRFs and other reprocessors). Compared to most AI models in the sector which are mostly optimised for robot integrations, our AI model is optimised for waste composition analysis, providing weight estimation and other useful information beyond item count for robot picking. The system enables the identification of all types of material and packaging, including black plastics and mixed material packaging (e.g sleeved bottles) that confuse current NIR systems; but also distinguishing packaging by usage (e.g food vs non-food), which helps assist and improve current mechanical and optical sorting methods as well as help implement policies such as Extended Producer Responsibility (EPR) and Deposit Return Scheme (DRS).