NED Students Major Achievement as Developed “Rice Quality Analyzer”

The first Pakistan IA-based software for the quality analysis of rice grain is successfully developed by students from the National Center for Artificial Intelligence (NCAI) at NED University, in collaboration with Rice Lab Pakistan.

A software called a “Rice Quality Analyser”, the software use Machine Learning in order to determine 7 main characteristics of rice grain in less than 60 seconds, including length, thickness, and average weight, and percentage of broken grains.

NED Students Major Achievement as Developed “Rice Quality Analyzer”

Hafiz Ahsan-ur-Rehman has stated that this achievement as a key milestone in the Pakistani rice sector by a research associate at the NCAI and a member of the Rice Quality Analyzer software development team.

He said that the Rice Quality Analyzer marketing campaign is underway and that the reactions of major rice-producing countries such as China, India, Indonesia, and Bangladesh have been positive.

Rice Quality Analyzer’s accuracy is 99%, which equals two modern Japanese and American. However, compared to the Japanese version the Pakistan version of the app is much more effective and cheaper than the U.S. version. More specifically, Rice Quality Analyzer has been established in view of Pakistan’s atmospheric conditions and complies with national rice industry requirements.

Pakistan is the world’s 10th largest rice producer and crop are a significant foreign exchange source. The price of rice is worldwide dependent on its production, which means that the higher the price depends on the quality of the product.

The quality of rice grains in Pakistan has been determined by the human eye and other instruments manually for decades. The traditional process includes the study of the 8 kg rice lot sample. This process not only takes time and is ineffective but also costs thousands of rupees. On the other hand, Rice Quality Analyzer would improve local rice industry test capacity by ensuring that the quality of full samples of rice is measured in minimal time and expense.

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