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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
