CFP last date
20 December 2024
Reseach Article

Classification in Cashew Grading System: A Systematic Review

by Sowmya Nag K., Veenadevi S.V.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 25
Year of Publication: 2023
Authors: Sowmya Nag K., Veenadevi S.V.
10.5120/ijca2023923004

Sowmya Nag K., Veenadevi S.V. . Classification in Cashew Grading System: A Systematic Review. International Journal of Computer Applications. 185, 25 ( Jul 2023), 7-10. DOI=10.5120/ijca2023923004

@article{ 10.5120/ijca2023923004,
author = { Sowmya Nag K., Veenadevi S.V. },
title = { Classification in Cashew Grading System: A Systematic Review },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 25 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number25/32846-2023923004/ },
doi = { 10.5120/ijca2023923004 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:01.447137+05:30
%A Sowmya Nag K.
%A Veenadevi S.V.
%T Classification in Cashew Grading System: A Systematic Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 25
%P 7-10
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Quality is the most important aspect in cashew nuts, based on which the price is fixed. This makes classification an even more important process. The purpose of this study is to conduct a systematic literature review to identify existing knowledge in the cashew grading system. The search method discusses the papers based on feature extraction techniques, outcomes, and limitations. Also highlight the research gap or focus on the classification and recognition of defective cashews and computation time.

References
  1. V. Hidellage and U.Samarajeewa.1999.Quality defects in manually processed cashew: incidents, origins and recommendations.Tropical Agricultural Research.vol 11, pp. 61-73.
  2. S.J. Ojolo, O. Damisa, J.I. Orisaleye and C. Ogbonnaya.2010. Design and development of cashew nut shelling machine.Journal of Engineering, Design and Technology.vol 8, no. 2, pp. 146-157.
  3. V. G. Narendra and K. S. Hareesh.2011.Cashew Kernels Classification using Texture Features.International Journal of Machine Intelligence.vol 3, no.2, pp. 45-51.
  4. Z. Lin, Z. Qizhi and X. Hongwen.2011.Research and analysis of classification model based on the shape parameters of cashew nuts.2011 International Conference on Consumer Electronics, Communications and Networks (CECNet), pp. 554-556.
  5. M. Thakkar, M. Bhatt and C.K. Bhensdadia.2011.Performance Evaluation of Classification Techniques for Computer Vision based Cashew Grading System.International Journal of Computer Applications. vol18, pp. 9-12.
  6. P. K. Patel, M.Samvatsar and P.K. Bhanodia.2012.A Survey Paper On Cashew Kernels Classification Using Color Features & Computer Revelation System.International Journal of Engineering Sciences & Research Technology.vol1, no. 6, pp. 328-334.
  7. L. S. Thota et.al.2012.Intelligent Model to Classify Cashew Kernels.International Journal of Engineering and Innovative Technology.vol 2, no. 6, pp. 294-301.
  8. P. K. Patel, D.Jonawal and P.K. Bhanodia.2013Classification of Intact Cashew Grading System with Fuzzy Logic.International Journal of Engineering Sciences and Research Technology.vol 2, no. 6, 1540- 1544.
  9. Karthickumar P, SinijaV.R nas Alagusundaram K.2014.Indian Cashew Processing Industry-An overview.Journal of Food Research and Technology.vol 2, no. 2, pp. 60-66.
  10. P. K. Verma, S. K. Nag and S. K. Patil.2014.Comparative Economics of Cashew Nut Kernel Processing Technology in Bastar Region of India.Bangladesh J. Agril. Res.vol 39, no. 1, pp. 165-172.
  11. E. C. Moreira, R. Freitas, A. Morais and A.S. Sombra.2014.RFID in Cashew Nut Industry.IEEE Brasil RFID, pp. 48-50.
  12. W. Nadar and J.M.Kundargi.2014.Classification of cashew based on the shape parameter.International Journal of Engineering Research & Technology.vol 2, no. 4.
  13. R. Rico, M. Bullo, and J. Salas-Salvado.2015Nutritional composition of raw fresh cashew (Anacardium occidentale L.) kernels from different origin.Food Science & Nutrition.vol 4, no. 2, pp. 329-338.
  14. V. Nagpure.2015.Review on Back Propagation Neural Network Application for Grading of Cashew Nuts.International Journal of Science and Research.vol 4, no. 10, pp. 1958-1962.
  15. V. Nagpure and K. Joshi.2016.Grading of Cashew Nuts on the Bases of Texture, Color and Size.International Journal on Recent and Innovation Trends in Computing and Communication.vol 4, no. 4, pp. 171-173.
  16. M. O. Aran, A. Nath, and A. Shyna.2016.Automated cashew kernel grading using machine vision.International Conference on Next Generation Intelligent Systems (ICNGIS).pp.1-5.
  17. N.V. Ganganagowdar and H.K. Siddaramappa.2016.Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks.Acta Scientiarum. Agronomy.vol 38, no.2, pp. 145-155.
  18. A. Shyna, and R.M. George.2017.Machine vision based real time cashew grading and sorting system using SVM and back propagation neural network.2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT).pp. 1-5.
  19. H. R. Bhoomika and N. Sudha Rani.2018.Problems and Prospects of Cashew Cultivation in India - An Overview. Int. J. Curr. Microbiol. App.Sci.vol 7, no.10, pp. 3687-3694.
  20. L. Bordekar, H. Velingkar, E. Fernandes, H. H. Bandekar, A. G. Harmalkar and B. J. Antonio Pinto.2018.Cashew Nut Grade Identification and Quality Testing Using Machine Learning.2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 661-66413.
  21. A. Sivaranjani, S. Senthilrani, B. Ashokumar and A. S. Murugan. 2018.An Improvised Algorithm For Computer Vision Based Cashew Grading System Using Deep CNN.2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA).pp. 1-5.
  22. J. U. Bailoor, M.C.Sunny, K. R. Anchan, M. J. Tauro and G. Shetty.2018.Segregation of Cashew Kernel and Areca Nut by Using Advanced Color Sorting Mechanism.International Journal of Scientific Development and Research.vol 3, no.5, pp. 566-572.
  23. M. Arora and Veena Devi.2018.A machine vision based approach to Cashew Kernel grading for efficient industry grade application.International Journal of Advance Research, Ideas and Innovations in Technology.vol 4, no. 6, pp. 865-871.
  24. N. Elakkiya, S. Karthikeyan and T. Ravi.2018.Survey of Grading Process for Agricultural Foods by Using Artificial Intelligence Technique.2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA).pp. 1834-1838.
  25. A. Sivaranjani, S. Senthilrani, B. Ashokumar and A. S. Murugan.2019.CashNet-15:An Optimized Cashew Nut Grading Using Deep CNN and Data Augmentation.2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN).pp. 1- 5.
  26. S. K. Vidyarthi,S. K. Singh, R. Tiwari, H.-W. Xiao, and R. Rai.2020.Classification of first quality fancy cashew kernels using four deep convolutional neural network models.Journal of Food Processing Engineering.vol 43, no. 12.
Index Terms

Computer Science
Information Sciences

Keywords

Cashew Grading Image Processing Feature extraction Machine learning Deep learning