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Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection

The assessment of food quality is of significant importance as it allows control over important features, such as ensuring adherence to food standards, longer shelf life, and consistency and quality of taste. Rice is the predominant dietary source of half the world’s population, and Pakistan contrib...

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Autores principales: Zia, Huma, Fatima, Hafiza Sundus, Khurram, Muhammad, Hassan, Imtiaz Ul, Ghazal, Mohammed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498175/
https://www.ncbi.nlm.nih.gov/pubmed/36140851
http://dx.doi.org/10.3390/foods11182723
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author Zia, Huma
Fatima, Hafiza Sundus
Khurram, Muhammad
Hassan, Imtiaz Ul
Ghazal, Mohammed
author_facet Zia, Huma
Fatima, Hafiza Sundus
Khurram, Muhammad
Hassan, Imtiaz Ul
Ghazal, Mohammed
author_sort Zia, Huma
collection PubMed
description The assessment of food quality is of significant importance as it allows control over important features, such as ensuring adherence to food standards, longer shelf life, and consistency and quality of taste. Rice is the predominant dietary source of half the world’s population, and Pakistan contributes around 80% of the rice trade worldwide and is among the top three of the largest exporters. Hitherto, the rice industry has depended on antiquated methods of rice quality assessment through manual inspection, which is time consuming and prone to errors. In this study, an efficient desktop-application-based rice quality evaluation system, ‘National Grain Tech’, based on computer vision and machine learning, is presented. The analysis is based on seven main features, including grain length, width, weight, yellowness, broken, chalky, and/or damaged kernels for six different types of rice: IRRI-6, PK386, 1121 white and Selah, Super kernel basmati brown, and white rice. The system was tested in rice factories for 3 months and demonstrated 99% accuracy in determining the size, weight, color, and chalkiness of rice kernels. An accuracy of 98.8% was achieved for the classification of damaged and undamaged kernels, 98% for determining broken kernels, and 100% for paddy kernels. The results are significant because the developed system improves the local rice quality testing capacity through a faster, more accurate, and less expensive mechanism in comparison to previous research studies, which only evaluated four features of the singular rice type, rather than the seven features achieved in this study for six rice types.
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spelling pubmed-94981752022-09-23 Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection Zia, Huma Fatima, Hafiza Sundus Khurram, Muhammad Hassan, Imtiaz Ul Ghazal, Mohammed Foods Article The assessment of food quality is of significant importance as it allows control over important features, such as ensuring adherence to food standards, longer shelf life, and consistency and quality of taste. Rice is the predominant dietary source of half the world’s population, and Pakistan contributes around 80% of the rice trade worldwide and is among the top three of the largest exporters. Hitherto, the rice industry has depended on antiquated methods of rice quality assessment through manual inspection, which is time consuming and prone to errors. In this study, an efficient desktop-application-based rice quality evaluation system, ‘National Grain Tech’, based on computer vision and machine learning, is presented. The analysis is based on seven main features, including grain length, width, weight, yellowness, broken, chalky, and/or damaged kernels for six different types of rice: IRRI-6, PK386, 1121 white and Selah, Super kernel basmati brown, and white rice. The system was tested in rice factories for 3 months and demonstrated 99% accuracy in determining the size, weight, color, and chalkiness of rice kernels. An accuracy of 98.8% was achieved for the classification of damaged and undamaged kernels, 98% for determining broken kernels, and 100% for paddy kernels. The results are significant because the developed system improves the local rice quality testing capacity through a faster, more accurate, and less expensive mechanism in comparison to previous research studies, which only evaluated four features of the singular rice type, rather than the seven features achieved in this study for six rice types. MDPI 2022-09-06 /pmc/articles/PMC9498175/ /pubmed/36140851 http://dx.doi.org/10.3390/foods11182723 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zia, Huma
Fatima, Hafiza Sundus
Khurram, Muhammad
Hassan, Imtiaz Ul
Ghazal, Mohammed
Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection
title Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection
title_full Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection
title_fullStr Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection
title_full_unstemmed Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection
title_short Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection
title_sort rapid testing system for rice quality control through comprehensive feature and kernel-type detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498175/
https://www.ncbi.nlm.nih.gov/pubmed/36140851
http://dx.doi.org/10.3390/foods11182723
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