Tailored single-belt model for whole potato sorting. Powered by AI-driven high-resolution VIS imaging and high-performance air ejectors, the system integrates 360° panoramic inspection to ensure every surface of potatoes is efficiently scanned, effectively eliminating visible defects and delivering deep-learning–refined sorting precision.

Tailored chute models for dehydrated 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.

Single-belt models designed with high hygiene standards for dehydrated 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 dehydrated 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 aluminum scrap sorting. Powered by dual-sensor fusion and high-performance air ejectors, the system purifies aluminum fractions by removing heavy metal impurities through XRT-based atomic number and material density analysis, and combines AI-driven surface feature recognition to efficiently separate wrought aluminum from cast aluminum, delivering deep-learning-refined sorting precision.

Tailored dual-belt models for non-ferrous metal scrap recycling, featuring upper-level high-sensitivity sorting and lower-level high-efficiency re-inspection. Powered by AI-driven high-resolution VIS imaging and high-performance air ejectors, the system recovers target high-value fractions from scrap streams by identifying color variations and surface characteristics, delivering deep-learning–refined sorting precision.