Citation: Zia, H.; Fatima, H.S.; Khurram, M.; Hassan, I.U.; Ghazal, M. Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection. Foods 2022, 11, 2723. https://doi.org/10.3390/ foods11182723 Academic Editors: Amin Reza Rajabzadeh and Syed Rahin Ahmed Received: 2 August 2022 Accepted: 31 August 2022 Published: 6 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. 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/). foods Article Rapid Testing System for Rice Quality Control through Comprehensive Feature and Kernel-Type Detection Huma Zia 1, * , Hafiza Sundus Fatima 2 , Muhammad Khurram 2 , Imtiaz Ul Hassan 2 and Mohammed Ghazal 1 1 College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates 2 National Center of Artificial Intelligence-Smart City Lab, NED University of Engineering and Technology, Karachi 75270, Pakistan * Correspondence: huma.zia@adu.ac.ae Abstract: 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. Keywords: food quality assessment; rice quality control; machine learning; computer vision; rapid testing 1. Introduction Rice is a staple food for more than half of the world’s population [1]. As a primary crop, it contributes a dominant proportion of the balanced diet of humans, predominantly in the diet of Asians, where most of the world’s rice is consumed and grown [2]. Pakistan is the 3rd largest exporter (80%) and the 12th largest producer of rice worldwide. Rice constitutes the second largest cash crop, and about 10% of its agricultural land is dominated by rice crops [3]. Therefore, high-quality rice production is a significant source of food, income, and economic growth [2]. Considering the continuing advancement of knowledge and technology in quality control, coupled with consumers’ food quality expectations, the need for precision and transparency in quality monitoring has become more important. Therefore, evaluating rice based on quality attributes is an extremely pivotal step to achieve and maintain high quality for both consumption and to maximize economic return [4]. There are typically seven physical parameters associated with rice quality: damaged, broken, paddy, colored (yellow and white), chalky, stonesand foreign objects, and dimen- sions/weight of the rice kernel [5]. The quality of rice kernels is often compromised by the quality of the seed and working parts of the post-harvest agricultural processing machinery. Generally, in parts of the world where rice is cultivated, and more specifically in Pakistan, the investigation of the type of grain, grading, and gauging quality attributes Foods 2022, 11, 2723. https://doi.org/10.3390/foods11182723 https://www.mdpi.com/journal/foods