From rule-based sorting to full rule-based sorting to full integration and connectivity. Since the introduction of the first AUTOSORT™ 30 years ago, TOMRA has led the industry transition from pioneering automated sorting to fully integrated, connected systems. Visitors to the booth will learn about the evolution of the AUTOSORT™ and the broader history of sensor and AI-based sorting through the various editions of the system launched since its debut.  

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The journey began with the 1996 edition, which introduced high-capacity automated sorting supported by smart optics and early AI principles. Over the years, hyperspectral imaging and software-based flexibility enabled adaptation to new material streams, optical upgrades enhanced illumination and made black material detection possible and advanced AI‑driven material intelligence expanded accuracy and material range. A critical milestone came in 2019 with the integration of GAINnext™, the first deep learning technology on the market. As an add-on to AUTOSORT™, it unlocked entirely new sorting applications and solved long-standing challenges, such as food-grade plastics sorting. 

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Today, AUTOSORT™ is recognised as the most powerful sorting system on the market, combining AI, multi-sensor technology and advanced image processing with enhancements such as GAINnext™, waste analysis and other digital tools for real-time monitoring, data-driven optimisation, and seamless plant integration – making it the connected core of high-quality recycling operations worldwide. 

Intelligent, interconnected and user-friendly ecosystems 

Lars Enge, EVP and Head of TOMRA Recycling, comments: "For three decades, the AUTOSORT™ brand has set the benchmark for performance in the recycling industry. IFAT 2026 offers us the ideal stage not only to reflect on our history, but also to share a clear vision of what comes next. We will introduce several new, impressive deep learning and AI solutions that help bridge the gap to true circularity. These innovations enable sorting plants to move beyond high-speed detection toward truly intelligent, interconnected and user-friendly ecosystems.” 

Next-generation sorter control and analytics  

Alongside the AUTOSORT™ and its award-winning GAINnext™ technology, TOMRA’s focus for IFAT 2026 is its brand‑new and intuitive software platform. TOMRA Local Control allows operators to manage all sorting machines from a single interface on the plant network. It provides full visibility of every machine on the line, delivering updates every second on machine status and performance trends. Operators can adjust configurations, activate sorting plans and create custom programs remotely from the office – a real game changer that transforms plant control into a faster, more efficient and data-driven experience.  

Visitors to TOMRA’s booth will also meet the team from PolyPerception, whose AI-powered waste analysis technology brings a new layer of intelligence to modern sorting plants. Its deep learning waste analyser delivers full transparency and traceability across the entire line, providing real-time insights into material composition, recovery and purity. By turning continuous stream data into reliable, actionable information, PolyPerception enables operators to boost efficiency, optimise performance and make confident, data-driven decisions – further strengthening TOMRA's vision of a smarter, more connected and future-ready recycling ecosystem. 

New deep learning and AI solutions 

TOMRA’s plans for the show also include the launch of various new deep learning and AI solutions that will be presented to the public for the first time. Experts will be available to discuss these latest developments in deep learning, along with the company's broader portfolio of sensor-based sorting solutions. 

“We welcome customers, partners and industry companions to our TOMRA booth in Hall 6 to discover what’s next in AI sorting,” concludes Enge. 

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Note: This article has been issued by TOMRA and has been published by AL Circle with its original information without any modifications or edits to the core subject/data.