Grading of Arecanut Using Machine Learning Techniques

Authors

  • Jyothi K  Professor, Department of ISE, JNNCE, Shivamogga, Karnataka, India
  • Sindhu M Hegde  Department of ISE, JNNCE, Shivamogga, Karnataka, India
  • Sumedha K S  Department of ISE, JNNCE, Shivamogga, Karnataka, India
  • Sushma C R  Department of ISE, JNNCE, Shivamogga, Karnataka, India
  • Thanushree D C  Department of ISE, JNNCE, Shivamogga, Karnataka, India

DOI:

https://doi.org//10.32628/CSEIT2283119

Keywords:

Arecanut grading, Image processing, Local ternary pattern, Support vector machine

Abstract

Arecanut is a commercial crop that grows well in areas with a lot of rain. Arecanuts have economic, cultural, and therapeutic value, and are classified into many varieties depending on the region where they are grown and consumed. An effort at grading Arecanut is discussed here. This method extracts features using a global textural characteristic called Local Binary Pattern. The Support Vector Machine classifier is used to grade Arecanut. Finally, measurements such as accuracy and precision are used to assess the grading system's performance.

References

  1. Bharadwaj N K, Dinesh R, N Vinay Kumar, “Classification And Grading of Areca nut Using Texture Based Block-Wise Local Binary Patterns”, Turkish Journal of Computer and Mathematics Education, Vol.12 No. 11, pp. 606- 616, 2021.
  2. S Siddesha, S K Niranjan, “Different Classifiers in Classification of Raw Arecanut”, International Journal of Engineering and advanced Technology, Vol.8 Issue-6, pp. 1671- 1676, 2019.
  3. Pushparani M.K, Dr. D Vinod Kumar, Dr. Abdulla Gubbi, “Arecanut Grade Analysis using Image Processing Techniques”, International Journal of Engineering Research and Technology Vol. 7, Issue 10, pp. 1-6, 2019.
  4. S Siddesha, S K Niranjan, V N Manjunath Aradhya, “Color Features and KNN in Classification of Raw Arecanut images”, In proceedings of 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 504-509, 2018.
  5. Kuo-Yi Huang, “Detection and classification of areca nuts with machine vision”, Computer and Mathematics with Aplplication, Vol. 64, Issue 5, pp. 739–746, 2012.
  6. Tong Liu, Jianbin Xie, Yizheng He, Ming Xu, and Chengang Qin, “An Automatic Classification Method for Betel Nut Based on Computer Vision”, In proceedings of the 2009 IEEE International Conference on Robotics and Biomimetics, Guilin, China, pp. 1264-1267, 2009.
  7. Ajit Danti, Suresha, “Segmentation and classification of raw arecanuts based on three sigma control limits”, Elsevier, Vol.4, pp. 215- 219, 2012.
  8. Ajit Danti, Suresha, “Texture Based Decision Tree Classification for Arecanut”, In proceedings of the CUBE International Information Technology Conference, pp. 113- 117, 2012.
  9. Meghana R, Ramya D Shetty, “Segregation of dry areca nut using digital image processing and machine learning techniques”, 2020.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2022-07-30

Issue

Volume 8, Issue 4, July-August-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Jyothi K, Sindhu M Hegde, Sumedha K S, Sushma C R, Thanushree D C, " Grading of Arecanut Using Machine Learning Techniques, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.33-39, July-August-2022. Available at doi : https://doi.org/10.32628/CSEIT2283119