Accurately identify discoloration, hard‑to‑detect mildew and insect‑infested features of pistachios. High‑dimensional HDR cameras capture 2D images of pistachios to precisely locate defects on nut bodies. Deep‑learning‑powered AI recognition mode easily identifies shrunken kernels, half‑kernels and defective nuts. Double‑layer structural design realizes multi‑layer screening and graded rejection of bad nuts.

Compact machine vision system integrating image acquisition and processing. Under motion‑capture mode, it accurately tracks high‑speed falling pistachios and captures detailed features of shells and kernels, meeting stringent sorting requirements for pistachios.

Optical design and algorithm models perform image distortion correction to restore the true structure of pistachio shell textures, splits and kernels.

Adopting human‑like "eye‑and‑brain" recognition for pistachios. Resist interference from shell reflection and natural splits. Precisely extract features including shell color, kernel hue, insect spots and mildew, and judge nut quality via comparative learning. Dedicated AI NPU chip delivers strong computing power and accelerated inference for high‑speed pistachio inspection.

Self‑developed special detection model library for pistachios, with more than 30 models available online. Compatible with sorting standards for white kernels, red‑skin nuts, bleached nuts, mildewed nuts and other pistachio varieties.

Intelligently controlled valve ports with multi‑channel parallel operation. High‑speed and high‑sensitivity bad‑nut rejection with microsecond‑level response. Flexible opening‑closing performance enables high throughput and high efficiency, perfectly adapting to high‑volume pistachio production conditions.