The HDF2-R  series integrates deep learning and multi-dimensional UHDR technology to deliver highly precise material sorting with the industry's largest open-source model database. It efficiently handles sorting challenges in food processing, including chili peppers, peanuts, beans, and nuts, overcoming micro color and shape differences that conventional systems struggle to identify.

The HDF2-R series products adopt deep learning and multi-dimensional UHDR technology, allowing users to share the industry's largest, most comprehensive, and most precise open-source model database. They have multi-dimensional and multi-characteristic identification of materials including color, shape, texture, area, light and shade, etc. Problems such as micro color difference, micro shape difference, and impurities of the same color that are hard to identify in conventional sorting can be easily solved.

• Four dimensional ultra definition recognition technology, strong integration of ultra clear and ultra precision, achieving extraordinary quality.
• 20 billion level self-collected open source material database, which can be shared among users.
• Optional high-end infrared configuration, better separation of foreign objects and impurities, easily achieving ultimate quality.
• New optimized architecture design, stronger adaptability to harsh sorting conditions, allowing users to enjoy a superior sorting experience.
• Higher throughput and better sorting effects endow product quality with stronger competitiveness, making it a great choice for high-end users.