Knowledge Hub
Rice Value Chain

Detailed analysis of multi-spectral computer vision architectures, grain instance segmentation models, and flatbed IoT scanners reducing grain quality inspection time from 30 minutes to under 2 minutes with >98% accuracy matching TCVN and international standards.

An in-depth look at dual-illumination optical systems detecting internal stress cracks in rice endosperm, preserving structural integrity, and maximizing industrial head rice milling yields.

An automated intake architecture linking IoT scanners with truck weighbridges, computing moisture, foreign matter, and chalkiness deduction tables in real time to eliminate subjective disputes.

Encoding GrainScan analysis results onto the BOS Blockchain ledger to generate immutable QR certificates that meet strict import tolerances in the EU, US, and Middle East markets.

Deep learning models analyzing microscopic embryo patterns and lemma curvature of fresh unhusked paddy (Paddy Rice) to identify ST24, ST25, Jasmine 85, and Dai Thom 8 in 2 minutes without DNA testing.

Ultra-high-speed industrial line-scan vision combined with embedded Edge AI and solenoid pneumatic valves with sub-10ms response times, accurately rejecting yellow, moldy, and foreign grains on milling chutes.