More Web Proxy on the site http://driver.im/

research-article

Evaluation of Chalkiness Based on Image Processing Approach: A Case Study in Vietnam

Authors:

Thuan Van Tran,

Hoang Ngoc Tran,

Viet Hoang TranAuthors Info & Claims

ICIIT '23: Proceedings of the 2023 8th International Conference on Intelligent Information Technology

Pages 223 - 228

https://doi.org/10.1145/3591569.3591608

Published: 13 July 2023 Publication History

Abstract

Rice grains with chalkiness will affect the quality and lower the price of rice, affecting the competitiveness of rice-exporting countries, and Vietnam is no exception. However, rice quality inspection is done manually, which takes time and money. The research proposes using image processing methods to determine the chalkiness, average width and percentage of whole grains (without broken) of each type of rice by image processing methods, especially the determination of grain grade. The level of chalkiness will be initially assessed according to Vietnamese standards. The study used 10 rice samples. The number of samples corresponding to 10 different types of rice that had been manually tested in the laboratory by humans was used as data to analyze the above indicators. The image processing methods are applied to analyze the color difference between the rice grain's chalkiness and the clear white area. Measured quality indicators are compared with methods in the laboratory by humans, and the reliability is much higher. This result facilitates the development of an assessment and verification tool to replace manual methods.

References

[1]

B. K. Yadav and V. K. Jindal, “Changes in head rice yield and whiteness during milling of rough rice (Oryza sativa L.),” J. Food Eng., vol. 86, no. 1, pp. 113–121, May 2008.

[2]

Z. Lin, “Chalky part differs in chemical composition from translucent part of japonica rice grains as revealed by a notched‐belly mutant with white‐belly,” J. Sci. Food Agric., vol. 96, no. 11, pp. 3937–3943, Aug. 2016.

[3]

De, Nguyen Ngoc, Giáo trình cây lúa (Rice plant curriculum). Cantho University, 2008.

[4]

Khush, Gurdev S., C. M. Paule, and Normita M. De la Cruz, “Rice grain quality evaluation and improvement at IRRI.,” International Rice Research Institute, Los Banos, Philippines.

[5]

Piramli, M.M., Rahman, A.F.N.A. and Abdullah, S.F., “Rice Grain grading classification based on perimeter using moore-neighbor tracing method,” J. Telecommun. Electron. Comput. Eng. JTEC, vol. 8, no. 2, pp. 23–27, 2016.

[6]

B. Mahale and S. Korde, “Rice quality analysis using image processing techniques,” in International Conference for Convergence for Technology-2014, Pune, India, Apr. 2014, pp. 1–5.

[7]

B. Arora, N. Bhagat, L. R. Saritha, and S. Arcot, “Rice Grain Classification using Image Processing & Machine Learning Techniques,” in 2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, Feb. 2020, pp. 205–208.

[8]

N. Duong-Trung, L.-D. Quach, M.-H. Nguyen, and C.-N. Nguyen, “Classification of Grain Discoloration via Transfer Learning and Convolutional Neural Networks,” in Proceedings of the 3rd International Conference on Machine Learning and Soft Computing - ICMLSC 2019, Da Lat, Viet Nam, 2019, pp. 27–32.

Digital Library

[9]

N. Duong-Trung, L.-D. Quach, and C.-N. Nguyen, “Learning Deep Transferability for Several Agricultural Classification Problems,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 1, 2019.

[10]

Z. Qiu, J. Chen, Y. Zhao, S. Zhu, Y. He, and C. Zhang, “Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network,” Appl. Sci., vol. 8, no. 2, p. 212, Jan. 2018.

[11]

Standard Technical Committee 10TC-02 Grains and pulses, “TCVN 8372: 2010 – Vietnam National Standard White rice - Determination of the ratio of clear white, silvery white and silvery whiteness.” Ministry of Agriculture and Rural Development in Vietnam.

[12]

W. Hu, “Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography,” Plant Phenomics, vol. 2020, pp. 1–12, May 2020.

[13]

D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar, and V. Osuna, “A Multilevel Thresholding algorithm using electromagnetism optimization,” Neurocomputing, vol. 139, pp. 357–381, Sep. 2014.

Digital Library

[14]

B. Pan, Z. Lu, and H. Xie, “Mean intensity gradient: An effective global parameter for quality assessment of the speckle patterns used in digital image correlation,” Opt. Lasers Eng., vol. 48, no. 4, pp. 469–477, Apr. 2010.

[15]

D. V. Tropin, S. A. Ilyuhin, D. P. Nikolaev, and V. V. Arlazarov, “Approach for Document Detection by Contours and Contrasts,” in 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, Jan. 2021, pp. 9689–9695.

[16]

Khan, M., Chakraborty, S., Astya, R., & Khepra, S. (2019, October). Face detection and recognition using OpenCV. In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 116-119). IEEE.

[17]

Sayeed, M. A., Ayesha, N., & Sayeed, M. A. (2020). Detecting Crows on Sowed Crop Fields using Simplistic Image processing Techniques by OpenCV in comparison with TensorFlow Image Detection API.

Cited By

Nguyen ATran MNguyen QHuynh HLe LQuach L(2024)Classification of Rice Plant Disease Based on Descriptive Information with DistilBERT's ArchitectureProceedings of the 2024 9th International Conference on Intelligent Information Technology10.1145/3654522.3654568(155-163)Online publication date: 23-Feb-2024
https://dl.acm.org/doi/10.1145/3654522.3654568

Index Terms

Evaluation of Chalkiness Based on Image Processing Approach: A Case Study in Vietnam
1. Information systems
  1. Information systems applications
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Models of learning

Recommendations

Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis

Successful identification and mapping of different cropping patterns under cloudy conditions of a specific crop through remote sensing provides important baseline information for planning and monitoring. In Vietnam, this information is either missing or ...
A monitoring indicator system for remote sensing of grassland vegetation growth and suitability evaluation – a case study of the Xilingol Grassland in Inner Mongolia, China
Fourth International Symposium on Recent Advances in Quantitative Remote Sensing

Grassland vegetation growth directly reflects plant growth conditions, and growth processes are an important component of ecological status assessments of grasslands and can provide timely guidance for agricultural production. In this study, the ...
Buffering-based approach to fluctuation analysis of glacier calving fronts using LANDSAT-7 ETM+, with a case study of Jakobshavn Isbræ

The annual or seasonal fluctuation of glacier calving fronts has been carefully mapped and measured, due to this property's close correlation with overall glacier behavior. For that purpose, remote sensing data is the most useful tool; however, LANDSAT-...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICIIT '23: Proceedings of the 2023 8th International Conference on Intelligent Information Technology

February 2023

310 pages

ISBN:9781450399616

DOI:10.1145/3591569

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Research
Refereed limited

Conference

ICIIT 2023

ICIIT 2023: 2023 8th International Conference on Intelligent Information Technology

February 24 - 26, 2023

Da Nang, Vietnam

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
25
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)3

Reflects downloads up to 22 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Nguyen ATran MNguyen QHuynh HLe LQuach L(2024)Classification of Rice Plant Disease Based on Descriptive Information with DistilBERT's ArchitectureProceedings of the 2024 9th International Conference on Intelligent Information Technology10.1145/3654522.3654568(155-163)Online publication date: 23-Feb-2024
https://dl.acm.org/doi/10.1145/3654522.3654568

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents