Students From NED University Develop Pakistan’s First AI-based Rice Quality Analyzer

A team of students from the National Centre of Artificial Intelligence (NCAI) at the NED University in partnership with Rice Lab Pakistan have developed Pakistan’s first-ever AI-based ‘Rice Quality Analyser’. It is a software that applies Machine Learning to examine the quality of rice grain in less than one minute. The analysis is done on the basis of seven main characteristics of rice grain which include length, thickness, average weight, percentage of broken grains etc.

Students From NED University Develop Pakistan’s First AI-based Rice Quality Analyzer

This Rice Quality Analyser designed in Pakistan has an accuracy rate of almost 99%, which is identical to modern US and Japenese rice quality analysers. Though, the Pakistani version of rice quality analyzer is much more effective than its Japanese counterparts and much cheaper than the one designed in the US.

As Pakistan is the world’s 10th biggest rice producer in the whole world therefore the quality of rice grain has a major impact on its price and export-potential. Priorly, the quality of rice grain has been assessed via a traditional process, which takes in a sample of 8 kg rice and determines its quality using the human eye. This traditional process costs too much in terms of time and capital and is mostly regarded as ineffective.

Rather, this locally designed Rice Quality Analyser grows the local rice testing capacity via the use of AI technology. It is also quicker, more durable and less expensive. In addition to that, this new technology takes into account Pakistan’s atmospheric conditions and complies with national rice industry standards.

On the development, a research associate at the NCAI and a member of the Rice Quality Analyzer Software Development Team, Hafiz Ahsan said,

This new AI software is a key achievement for Pakistan’s rice sector. This state-of-the-art software will reduce the time taken to complete rice export orders by decreasing the cost of rice classification and increasing testing capacity.

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