| Brand Name: | PHOENIX |
| Model Number: | LX4-B4 Color Sorter |
| MOQ: | 1 |
| Price: | Price Negotiable |
| Payment Terms: | L/C,D/A,D/P,T/T |
Single‑layer belt model custom‑built for IQF frozen raspberry sorting, engineered to the highest hygiene standards. Powered by AI‑enhanced ultra‑high‑resolution RGB camera system, it accurately rejects surface defects and foreign materials, with sorting accuracy continuously improved via deep‑learning iteration.
Sorting IQF frozen raspberries presents unique challenges distinct from fresh berries. Raspberries feature soft texture and fine surface trichomes; fruits tend to crack under low‑temperature conditions. Manual sorting brings heavy labor intensity, while finger contact causes thawing and sticking, resulting in high escape rates. Surface frost and inter‑fruit ice adhesion obscure critical defects such as dark‑red spots, mildew, softening and collapse, which are barely identifiable by conventional vision technologies. In addition to frozen clumps and stuck defective fruits (e.g. double fruits, fragment agglomerates) further complicate sorting. Traditional solutions show clear limitations: manual sorting suffers low efficiency and inconsistent quality in cold working environments; air separation and gravity sorting only work on size, weight and density, and cannot detect color deviations, surface damage, calyx residues or foreign contaminants.
The PHOENIX LX4‑B4 Series is an intelligent visual sorting system custom‑tailored for industrial‑grade sorting and quality control of premium IQF frozen raspberries, built to top‑tier hygiene specifications. Equipped with ultra‑high‑resolution RGB imaging and self‑developed AI algorithms, it adapts to the physical properties of bulk IQF raspberries. Even under frost‑obscured conditions, deep‑learning models reliably identify raspberry‑specific dark‑red mildew spots, partial softening and flesh breakages. Morphology, texture and edge feature analysis distinguishes same‑color foreign matter such as leaves and stems, and intelligently detects frozen clumps and adhered defective produce. Through high‑speed visual inspection and precise ejection, the system efficiently sorts out color variations, surface damages, foreign contaminants and clumped berries, improving the uniformity and purity of finished products. It delivers a more efficient, reliable and quantifiable solution for IQF frozen raspberry processing.
AI‑powered surface defect recognition algorithms are trained on real‑world production datasets via deep learning for continuous sorting‑accuracy enhancement. They help processors upgrade product quality, boost throughput and cut labor costs to sustain competitive advantages in the marketplace.
| Model | Belt Width(mm) | Air Nozzle | Air Pressure(MPa) | Air Consumption(m³/min) | Voltage | Power(kW) | Dimension(mm) | Unpacked Weight(kg) |
|---|---|---|---|---|---|---|---|---|
| LX4-B4 | 1200 | 256 | 0.6~0.8 | <3.6 | 220V~50/60Hz | 4.8 | 5271×2330×2077 | 1725 |
| LX4-B6 | 1800 | 384 | 0.6~0.8 | <3.6 | 220V~50/60Hz | 6.8 | 4547×2964×2100 | 1425 |
Ultra‑high‑precision visual inspection for multiple defects on IQF frozen raspberries based on surface characteristics: