Monarch HWN SWIR Sorter: Dual-Band InGaAs SWIR + AI Enables Whole-Nut Inspection Across Surface, Composition, and Internal Defects
Technical Features
- Customized sorting solution for premium nut processors
- Compact single-belt sorting system designed for quick installation and easy production line integration
- High-capacity vibratory feeder spreads nuts into a uniform single layer, significantly reducing overlap
- Sealed scanning and control enclosure provides high-grade dust protection for long-term reliable operation
- Full food-grade stainless steel construction with quick-change belt design facilitates daily cleaning and maintenance, meeting stringent food safety and hygiene standards
- Integrated, field-proven dust control system ensures operational stability and consistent performance, greatly reducing downtime risk
- Modular belt width options (400/800/1200 mm) offer flexibility to match different production capacities
Advanced AI-Powered 5-in-1 Multi-Spectral Detection System
- Dual-band Indium Gallium Arsenide (InGaAs) Short-Wave Infrared (SWIR) camera system
- Proprietary AI deep learning algorithms that continuously optimize defect recognition with new sample data
- High-resolution RGB camera system
- Highly stable air-cooled halogen lighting system
- High-brightness LED lighting system
Delivers Ultra-High Precision Inspection for Premium Nut Processing
Capable of accurately identifying defects across multiple dimensions including color, shape, texture, chemical composition, and moisture content. Compared to traditional RGB visible-light sorting, the core advantages are as follows:
- Transcends Surface-Level Inspection: Accurately identifies internal defects such as hidden mold, insect damage, and rancidity that are missed by RGB cameras
- More Precise Foreign Material Removal: Effectively removes shell fragments and kernel residues. Reliably identifies foreign materials like plastic, stones, glass, and metal—even when their color matches the nuts
- Superior Early-Stage Defect Detection: Detects early-stage mold and aflatoxin contamination undetectable by RGB-based systems
- Higher Throughput and Efficiency: Supports greater processing capacity while significantly reducing manual intervention
- Self-Learning Algorithm Evolution: Detection accuracy continuously improves as the self-learning algorithm accumulates more data
Specifications
| Model | Belt Width (mm) | Air Nozzle | Air Pressure (MPa) | Air Consumption (m³/min) | Voltage | Power (kW) | Dimension (mm) | Unpacked Weight (kg) |
|---|
| HAD4 | 1200 | 512 | 0.6~0.8 | <2.4 | 220V ~ 50/60Hz | 7.9 | 3590×2170×3125 | 2264 |