Single-belt models designed with high hygiene standards for fresh vegetables sorting. Powered by AI-driven high-resolution VIS imaging and high-performance air ejectors, the system efficiently identifies and rejects discolorations, surface blemishes, and foreign material, delivering deep-learning–refined sorting precision.

Dual-belt models designed with high hygiene standards for fresh vegetables sorting, featuring secondary re-inspection to minimize false rejections and maximize yield. Powered by AI-driven VIS imaging and high-performance air ejectors, the system efficiently identifies and rejects discolorations, surface blemishes and foreign material, delivering deep-learning–refined sorting precision.

Tailored single-belt models for IQF fruits and vegetables sorting to the highest hygiene standards. Powered by AI-driven ultra-high-resolution VIS imaging and high-performance air ejectors, the system efficiently identifies and rejects discolorations, surface blemishes, clumps, adhesion, and foreign material, delivering deep-learning–refined sorting precision.

Tailored dual-belt models for IQF fruits and vegetables sorting to the highest hygiene standards, featuring secondary re-inspection to minimize false rejection and maximize yield. Powered by AI-driven ultra-high-resolution VIS imaging and high-performance air ejectors, the system efficiently identifies and rejects various contaminants and foreign material, delivering deep-learning–refined sorting precision.

Tailored single-belt model for whole carrot sorting. Powered by AI-driven high-resolution VIS imaging and high-performance air ejectors, the system integrates consecutive top-view and off-belt parabolic detection to more efficiently identify and reject defects that conventional sorters struggle to handle, delivering deep-learning–refined sorting precision.

Tailored single-belt model for French fries sorting. Powered by AI-driven high-resolution VIS imaging and high-performance air ejectors, the system integrates consecutive top-view and off-belt parabolic detection to more efficiently identify and reject defects that conventional sorters struggle to handle, delivering deep-learning–refined sorting precision.