High‑Dimensional HDR Camera · Capture Every Detail in One Shot
Equipped with a micro machine‑vision system integrating image acquisition and real‑time processing. In motion‑capture mode, it accurately tracks flowing soybean particles and fully captures surface defect features in a single exposure. Flaws including insect holes, mildew and breakages are clearly detected to meet stringent sorting standards.
Distortion Correction Technology · Ultra‑low Distortion, Restore True Morphology
Featuring sophisticated optical design and self‑developed algorithm models, it delivers efficient distortion correction for captured images, maximally restoring the original geometric form of soybean particles and providing high‑fidelity data for subsequent identification.
AI Sorting Solution · Precise Algorithms, Powerful Computing Performance
Imitating human “eyes‑and‑brain” recognition logic, it boasts strong anti‑interference capability against environmental disturbances. It accurately extracts features such as insect holes, mildew and breakages, and rapidly judges soybean quality via comparative learning. Built‑in dedicated NPU AI chip delivers robust computing power under high‑speed operation for real‑time reasoning and sorting.
Ruize Cloud Platform · Model Factory, Brain‑like Thinking
Self‑developed detection model library with over 30 professional sorting models available for online invocation. It flexibly adapts to sorting requirements for soybeans of different grades and realizes multi‑function capability on one single machine.
Dynamic Ejector Valve System · Cope with Output Fluctuations with Ease
Intelligently controlled multi‑channel ejector valves offer microsecond‑level response, high speed and high sensitivity to precisely reject defective soybeans. Flexible valve opening‑closing supports parallel operation, maintaining high throughput, high efficiency and stable performance even with sharp output increase.
AI Super Brain · End‑to‑end Local Closed‑loop Operation
The whole workflow including soybean sample collection, model training and decision‑making execution is completed locally on‑machine with no external server dependency. Only 50 sample images are required to build a high‑precision sorting model within one hour, achieving “sample‑collect‑train‑deploy‑instant‑use” and greatly shortening deployment cycles.
