Automate Rice Grain Quality Analysis

The Story

The client approached FiftyFive Technologies to build an automated computer-vision system that could replace slow, manual rice grain quality inspections. Their goal was to eliminate human dependency, accelerate lab throughput, and establish a repeatable, data-driven testing workflow. FiftyFive deployed a dedicated engineering team, AI/ML, backend, desktop development, and QA to design and deliver a complete custom software solution. The engagement focused on accuracy, speed, and scalability while ensuring the client fully owned and operated the final system.

The client is a leading European water solutions company offering 5000+ pump products, partnering with major courier networks and distributing globally recognized industrial brands with full compliance to regional standards.

Amit Jangid

Founder & CTO

Challenges

• > Training a machine learning model to detect and segment individual grains from scanned images with high precision.

• > Extracting structured parameters (height, width, color values, location coordinates) for each grain.

• > Automating image cropping, metadata generation, and per-grain storage.

• > Building a fast, stable workflow for producing validated CSV reports within strict time limits.

• > Ensuring system accuracy across varying grain colors, shapes, and image inconsistencies.

The Solution

• > Implemented and fine-tuned a Mask R-CNN–based segmentation model for pixel-level grain detection.

• > Engineered a desktop application using Python, .NET Core 6, and SQLite for end-to-end processing.

• > Built automated pipelines for grain measurement extraction, color analysis, and individual image generation.

• > Developed a structured CSV reporting engine delivering grain-level metadata in under 2 minutes.

• > Integrated QA validation workflows to ensure consistent segmentation accuracy and repeatable test results.

• > Delivered the complete solution through a dedicated AI engineer, backend developer, desktop developer, and QA specialist team.

The Outcome

The automated system reduced analysis time from hours of manual evaluation to 1–2 minutes per batch, improving operational efficiency by over 90%. The client achieved higher measurement accuracy, consistent reporting, and reduced dependency on manual lab workflows. The solution strengthened their credibility in quality assurance while lowering operational effort and enabling scalable, repeatable rice grain testing across their processes.