What needed solving
Rice grading depended entirely on operator skill and subjective judgment — long inspection times for 100g–1kg samples, inconsistent results across shifts, and inability to scale to real production volumes.
Cost-prohibitive imported commercial systems limited adoption, while buyers demanded objective, repeatable, auditable quality metrics.
How Qualitas solved it
An end-to-end AI-driven grain analysis platform was developed covering image acquisition, real-time inference, quality analytics, and hardware control. Per-grain metrics — length, width, area, chalkiness %, and whiteness — are measured automatically with an 80% visibility threshold to eliminate duplicate measurements.
The system classifies broken, chalky, discolored, and damaged grains automatically and generates traceable, auditable PDF reports without manual intervention.
The full case study covers detailed system architecture, hardware configuration, algorithm pipeline, integration approach, validation data, and a step-by-step deployment timeline with ROI calculations from live production environments.
