Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.4K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.4K
Quality Assurance01:19

Quality Assurance

187
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
187

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Guided group reflection versus SNAPPS in enhancing clinical reasoning in final-year MBBS students at a public-sector teaching hospital in Pakistan: a quasi-experimental study.

BMJ open·2026
Same author

Explainable Split-Learning-Based Framework for Accurate Pulmonary Nodule Classification.

Bioengineering (Basel, Switzerland)·2026
Same author

3D Adversarial Segmentation of Kidney-Transplant Across Multiple MRI Sequences Using Probabilistic and Anatomical Priors.

Diagnostics (Basel, Switzerland)·2026
Same author

AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.

Cancers·2026
Same author

Structurally tailored nanocomposite sorbent enabling high-energy-density thermochemical storage in e-thermal banks for electric vehicle applications.

Materials horizons·2026
Same author

Evolving Trends in Organ Donation and Transplantation Rates Across Muslim Majority Countries.

Transplant international : official journal of the European Society for Organ Transplantation·2025

Related Experiment Video

Updated: Aug 28, 2025

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits
09:43

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Published on: January 3, 2025

2.5K

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

Huma Zia1, Hafiza Sundus Fatima2, Muhammad Khurram2

  • 1College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates.

Foods (Basel, Switzerland)
|September 23, 2022
PubMed
Summary

A new computer vision and machine learning system, "National Grain Tech," accurately assesses rice quality. This automated system evaluates seven features across six rice types, offering a faster, more precise alternative to manual inspection.

Keywords:
computer visionfood quality assessmentmachine learningrapid testingrice quality control

More Related Videos

Transverse Sectioning of Mature Rice Oryza sativa L. Kernels for Scanning Electron Microscopy Imaging Using Pipette Tips as Immobilization Support
05:22

Transverse Sectioning of Mature Rice Oryza sativa L. Kernels for Scanning Electron Microscopy Imaging Using Pipette Tips as Immobilization Support

Published on: January 25, 2022

3.8K
High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
05:55

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Published on: June 16, 2018

7.1K

Related Experiment Videos

Last Updated: Aug 28, 2025

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits
09:43

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Published on: January 3, 2025

2.5K
Transverse Sectioning of Mature Rice Oryza sativa L. Kernels for Scanning Electron Microscopy Imaging Using Pipette Tips as Immobilization Support
05:22

Transverse Sectioning of Mature Rice Oryza sativa L. Kernels for Scanning Electron Microscopy Imaging Using Pipette Tips as Immobilization Support

Published on: January 25, 2022

3.8K
High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
05:55

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Published on: June 16, 2018

7.1K

Area of Science:

  • Agricultural Engineering
  • Computer Science
  • Food Science

Background:

  • Accurate food quality assessment is crucial for maintaining standards, shelf life, and consumer satisfaction.
  • Rice is a global staple, and Pakistan is a major exporter, yet relies on outdated manual quality inspection methods.
  • Manual rice quality assessment is labor-intensive, time-consuming, and susceptible to human error.

Purpose of the Study:

  • To develop and evaluate an automated system for rice quality assessment using computer vision and machine learning.
  • To analyze seven key quality features (grain length, width, weight, yellowness, broken, chalky, damaged kernels) for six rice varieties.
  • To provide a more efficient, accurate, and cost-effective solution compared to traditional methods.

Main Methods:

  • Development of a desktop application named 'National Grain Tech'.
  • Implementation of computer vision and machine learning algorithms for image analysis.
  • Testing the system on six rice types (IRRI-6, PK386, 1121 white and Selah, Super kernel basmati brown, and white rice) in industrial settings over three months.

Main Results:

  • Achieved 99% accuracy for size, weight, color, and chalkiness assessment of rice kernels.
  • Demonstrated 98.8% accuracy in classifying damaged/undamaged kernels and 98% for broken kernels.
  • Attained 100% accuracy for paddy kernel identification.

Conclusions:

  • The 'National Grain Tech' system significantly enhances local rice quality testing capabilities.
  • The developed system offers a faster, more accurate, and cost-effective mechanism for rice quality evaluation.
  • This study advances rice quality assessment by analyzing multiple features across diverse rice types, surpassing previous research limitations.