What needed solving
High dependence on operator skill and subjective judgment
Long inspection times for large samples (100 g–1 kg)
How Qualitas solved it
An end-to-end AI-driven grain analysis platform was developed — covering image acquisition, real-time inference, quality analytics, reporting, and hardware control — to deliver automated, high-throughput rice quality assessment.
Qualitas delivered an AI-powered vision system with real-time grain detection, tracking, and dual-model segmentation for precise per-grain analysis. The platform measures length, width, area, chalkiness percentage, and whiteness metrics on a per-grain basis — enabling objective quality grading.
ABSTRACT
This case study presents the design, development, and deployment of an AI-powered Grain Analyzer that combines computer vision, deep-learning detection and segmentation, real-time processing, and seamless hardware integration to deliver objective, granular, and actionable insights into rice quality at industrial throughput.
Executive Summary
This case study presents the design, development, and deployment of an AI-powered Grain Analyzer application built to automate large-scale rice quality assessment. The system replaces slow, subjective manual inspection and low-throughput scanner-based analyzers with a high-speed, vision-based inspection platform capable of analyzing 100 g to 1 kg of rice per run with high accuracy, repeatability, and detailed reporting. By combining computer vision, deep learning-based detection and segmentation, real-time processing, and seamless hardware integration, the Grain Analyzer enables mill operators, laboratories, and quality teams to obtain objective, granular, and actionable insights into rice quality — at industrial throughput.
Industry Context
Rice quality assessment plays a critical role across procurement, milling, grading, and export. Traditional methods rely heavily on manual inspection by skilled technicians, small sample sizes (typically 20–50 g), and static scanner-based grain analyzers.
The Challenge
- High dependence on operator skill and subjective judgment
- Long inspection times for large samples (100 g–1 kg)
- Inconsistent and non-repeatable results across shifts
- Inability to scale to real production volumes
- Cost-prohibitive imported commercial systems limiting adoption
The Solution
An end-to-end AI-driven grain analysis platform was developed — covering image acquisition, real-time inference, quality analytics, reporting, and hardware control — to deliver automated, high-throughput rice quality assessment.
The Cost of the Current Solution
Manual and legacy approaches impose hidden costs on every batch — measured in time, repeatability, and lost throughput. The three pressures below define the gap that automated machine vision is built to close.
Objective
The Grain Analyzer was engineered to replace fragmented manual workflows with a single integrated platform. The functional objectives were defined upfront to ensure the system would meet production-grade demands from day one.
- Analyze 100 g to 1 kg of rice per batch with consistent accuracy
- Handle grains on a moving conveyor without stopping production
- Accurately classify broken, chalky, discolored, and damaged grains
- Generate traceable, auditable reports automatically
- Integrate tightly with mechanical and control systems
How We Help
Qualitas delivered an AI-powered vision system with real-time grain detection, tracking, and dual-model segmentation for precise per-grain analysis. The platform measures length, width, area, chalkiness percentage, and whiteness metrics on a per-grain basis — enabling objective quality grading. Comprehensive PDF reports — individual, batch-level, machine-level, and trial-based — are generated automatically without manual intervention.
Implementation
AI and machine vision technologies were applied to automate the grain inspection process. The desired outputs were achieved using the Qualitas 4I methodology — Install, Instruct, Inspect, Improve — which progresses each deployment from hardware setup through model training, live inference, and continuous refinement.
Hardware Setup & Mechanical Integration Integration with the mechanical conveyor system and vibration feeders. Industrial camera INSTALL placement with controlled backlighting for consistent imaging. Control interfaces wired for motors, vibration intensity, and illumination.
Model Training & Annotation A solution was developed using acquired images of various grain types and defects. Each INSTRUCT defect category was trained with a specialized set of annotated images covering good, broken, chalky, and discolored grains.
Real-Time Inference & Per-Grain Validation Real-time inference on live video streams with per-grain validation using an 80% visibility INSPECT threshold rule — ensuring each grain is processed exactly once, eliminating duplicates and partial measurements.
