Designing A Neural Network Model in Grading Malaysian Rice

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Un-consistency of rice evaluating rehearses in Malaysia came about numerous methodologies utilized by zones which effectively creating rice in Malaysia. Understanding the significance of rice reviewing process in guaranteeing rice quality can be controlled, it is critical to have a standard rice evaluating approach for the referenced reason. To accomplish this, there are two essential viewpoints that should be considered in structuring rice reviewing model; evaluating system and variables to be utilized for evaluating (generally alluded as rice characteristics). This article proposes a Neural Network (NN) demonstrate for evaluating Malaysian rice. To apply the model, twenty one rice highlights are proposed to be utilized. Mix of broad writing survey and arrangement of meeting were utilized in deciding the highlights. To assess the model, master survey was led including area specialists and skill of NN. The proposed model is accepted to be gainful for BERNAS as well as to different specialists in a similar space.. The NN model can be utilized as direction or reference for comparative reviewing works.

     

     


  • Keywords


    Neural Network, rice grading, Malaysian rice

  • References


      [1] IRRI, 2006. [Online]. Available: IRRI Rice Knowledge Bank: http://www.knowledgebank.irri.org/ricebreedingcourse/Grain_quality.ht m. [Accessed 20 April 2012].

      [2] BERNAS, "Background," 2011. [Online]. Available: http://www.bernas.com.my/index.php. [Accessed 11 February 2012].

      [3] M. Gummert and J. Rickman, "Rice Quality," 2011. [Online]. Available: http://www.knowledgebank.irri.org/factsheetsPDFs/postharvestFS_Rice _Quality.pdf. [Accessed 2012 May 2012].

      [4] Shakeel PM, Baskar S, Dhulipala VS, Mishra S, Jaber MM., “Maintaining security and privacy in health care system using learning based Deep-Q-Networks”, Journal of medical systems, 2018 Oct 1;42(10):186.https://doi.org/10.1007/s10916-018-1045-z

      [5] H. M. G. GAZETT, "Essential (Control of Supply of Rice) Regulations 1974, Rice (Grade and Price Control) Order 1992," 1992.

      [6] M. Mustaffa, Interviewee, Rice Quality and Rice Grading. [Interview]. 29 April 2012.

      [7] Shakeel PM, Baskar S, Dhulipala VS, Jaber MM., “Cloud based framework for diagnosis of diabetes mellitus using K-means clustering”, Health information science and systems, 2018 Dec 1;6(1):16.https://doi.org/10.1007/s13755-018-0054-0

      [8] L. Pabamalie and H. L. Premaratne, "A Grain Quality Classification System," in IEEE Conference Publications, 2010.

      [9] S. J. Rad, F. A. Tab and K. Mollazade, "Classification of rice varieties using optimal color and texture features and BP Neural networks," in IEEE Conference Publications, 2011.

      [10] O. C. Agustin and B. -J. Oh, "Automatic Milled Rice Quality Analysis," in IEEE Conference Publications, 2008.

      [11] B. Verma, "Image Processing Techniques for Grading & Classification of Rice," in IEEE Conference Publications, 2010.

      [12] J. D. Guzman and E. K. Peralta, "Classification of Philippine Rice Grains Using Machine Vision and Artificial Neural Networks," in World Conference On Agricultural Information And It, 2008.

      [13] L. Guangrong, "Detection of Chalk Degree of Rice Based on Image Processing Technique," in IEEE Conference Publications, 2011.

      [14] Selvakumar S, Inbarani H, Shakeel PM. A Hybrid Personalized Tag Recommendations for Social E-Learning System. International Journal of Control Theory and Applications. 2016;9(2):1187-99.

      [15] Q. Yao, J. Chen, Z. Guan, C. Sun and Z. Zhu, "Inspection of rice appearance quality using machine vision," in IEEE Conference Publications, 2009.

      [16] D. M. Shiddiq, Y. Y. Nazaruddin and S. Raharja, "Estimation of Rice Milling Degree using Image Processing and Adaptive Network Based Fuzzy Inference System (ANFIS)," in IEEE Conference Publications, 2011

      [17] Shakeel PM. Neural Networks Based Prediction Of Wind Energy Using Pitch Angle Control. International Journal of Innovations in Scientific and Engineering Research (IJISER). 2014;1(1):33-7.

      [18] C. -Y. Wee, R. Paramesran and F. Takeda, "Fast Computation of Zernike Moments For Rice Sorting System," in IEEE Conference Publications, 2007.

      [19] R. C. Chakraborty, "Fundamentals of Neural Networks Artificial Intelligent," 2010. [Online]. Available: http://www.myreaders.info/html/artificial_intelligence.html. [Accessed 07 Mac 2012].

      [20] J. M. Bishop and R. J. Mitchell, "Neural Networks An Introduction," in IET Conference Publications, 1991.

      [21] R. E. Uhrig, "Introduction to Artificial Neural Networks," in IEEE Conference publication, 1995.

      [22] J. Paliwal, N. S. Visen and D. S. Jayas, "Evaluation of Neural Networks Architectures for Cereal Grain Grading Using Morphological Features," 2001.

      [23] M. Gummert, "Measuring the Moisture Content," 2011. [Online]. Available: http://www.knowledgebank.irri.org/factsheetsPDFs. [Accessed 30 April 2012].

      [24] Alief (personal communication, April 29, 2012)

      [25] Mushidah (personal communication, April 15, 2012)

      [26] Sidnal, N., Patil, V. U., & Patil, P. 2013. Grading and Quality Testing of Food Grains Using Neural Network. International Journal of Research in Engineering and Technology.

      [27] Effendi, Z., Ramli, R., & Ghani, J. A. (2010). A Back Propagation Neural Networks for Grading Jatropha curcas Fruits Maturitiy. American Journal of Applied Sciences, 7(3), 390.

      [28] M. A. Zainal, Interviewee, Rice Quality and Rice Grading. [Interview]. 29 April 2012.

      [29] Baskar, S., & Dhulipala, V. R., “Biomedical Rehabilitation: Data Error Detection and Correction Using Two Dimensional Linear Feedback Shift Register Based Cyclic Redundancy Check”, Journal of Medical Imaging and Health Informatics, 2018, 8(4), 805-808.

      [30] BERNAS, "Rice Types In Malaysia," 2011. [Online]. http://www.bernas.com.my/index.php?option=com_content&view=artic le&id=90&Itemid=103. [Accessed 11 February 2012].

      [31] MuhammedShafi. P,Selvakumar.S*, Mohamed Shakeel.P, “An Efficient Optimal Fuzzy C Means (OFCM) Algorithm with Particle Swarm Optimization (PSO) To Analyze and Predict Crime Data”, Journal of Advanced Research in Dynamic and Control Systems, Issue: 06,2018, Pages: 699-707

      [32] W. Zhong, C. Liu, Y. Zhang and L. Wu, "Classification of Rice Kernels Using Wavelet Packet.," 2011.

      [33] M. Yao, M. Liu and H. Zheng, "Exterior Quality Inspection of Rice Based on Computer Vision," in IEEE Conference Publications, 2010.


 

View

Download

Article ID: 27341
 
DOI: 10.14419/ijet.v7i3.20.27341




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.