The client needed an advanced rice inspection system to automate quality control by analyzing grain size, shape, and defects. The system had to accurately classify rice grains as long grains or broken grains, calculate their dimensions, and determine the percentage of each type. It was required to work on both black and white surfaces for reliable defect detection, replacing manual inspection with an AI-driven approach to improve accuracy and efficiency.
🎯 Developed an AI-powered rice inspection system to automate grain classification. 🎯 Implemented a computer vision model to detect and measure total grains, long grains, and broken grains. 🎯 Optimized the system to analyze rice grains on black and white backgrounds for consistent accuracy.
🎯 Integrated a real-time processing pipeline to provide instant grain measurements. 🎯 Designed a detailed reporting system to help in rice grading and quality assessment.
Whether the grains are spread on a black or white background, the system accurately picks up defects and measurements — making it super reliable in different lighting and surface conditions.
The system automatically detects and classifies rice grains — spotting the difference between long and broken grains with precision, so no more manual sorting or guesswork.






John Doe
Codetic
The system successfully automated the rice inspection process, achieving high precision in defect detection and grain classification. By eliminating manual errors and providing real-time analysis, it significantly improved quality control for rice processing. With its ability to inspect grains on multiple surfaces and generate precise statistical reports, the system became a game-changer for rice manufacturers looking for consistent, reliable, and scalable quality assessment solutions.
The system processes images in real-time, giving instant feedback on the grain type and size no delays, just quick and clear results.
Specially optimized to maintain high accuracy whether grains are placed on dark or light backgrounds, removing the need to switch setups.
We added a reporting feature that breaks down the inspection data, making it easy for quality teams to analyze and make grading decisions confidently.
We built a powerful computer vision model that can identify and measure each grain, helping speed up and improve the quality control process.
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