International Journal of Emerging Research in Engineering, Science, and Management
Vol. 4, Issue 1, pp. 54-61, Jan-Mar 2025.
https://doi.org/10.58482/ijeresm.v4i1.8

Exploring the Diversity of Rice Grain Variegation: A Review

M. Niranjana, F. Kurus Malai Selvi

Research Scholar, Research Department of Computer Science, Government College for Women (Autonomous), Kumbakonam, (Affiliated to Bharathidasan University, Tiruchirappalli) Tamil Nadu, India. drniranjanasampath@gmail.com

Associate Professor & Head, Research Department of Computer Science, Government College for Women (Autonomous), Kumbakonam, (Affiliated to Bharathidasan University, Tiruchirappalli) Tamil Nadu, India.

Abstract: Challenges persist in developing a suitable method to distinguish high-quality cultivated rice seeds, which can be estimated based on their characteristics. To prevent rice grain varieties from being incorrectly labeled, the quality and type of rice grains must be accurately identified. In this paper, the classification of rice grains is analyzed, and a study is conducted on various algorithms used at each stage. Generally, visual observations are made by specialists using specialized devices that measure various properties. The resultant data are processed through different stages using multiple algorithms, which are discussed in detail. This study reviews machine learning techniques for differentiating rice seeds based on various algorithms. Each stage is analyzed with distinct objectives, and necessary conclusions are drawn to inform the next stage of research.

Keywords: Classification, Deep Learning, Ensemble Model, Machine Learning, Rice Grain.

References:

  1. Charles B. Heiser Jr., Seed to civilization: the story of food. Harvard University Press, 1990.
  2. M. Murshed and M. M. Tanha, “Oil price shocks and renewable energy transition: Empirical evidence from net oil-importing South Asian economies,” Energy Ecology and Environment, vol. 6, no. 3, pp. 183–203, May 2020, doi: 10.1007/s40974-020-00168-0.
  3. I. Chatnuntawech, K. Tantisantisom, P. Khanchaitit, T. Boonkoom, B. Bilgic, and E. Chuangsuwanich, “Rice classification using Spatio-Spectral Deep Convolutional Neural Network,” arXiv.org, May 29, 2018. https://arxiv.org/abs/1805.11491
  4. R. Khanam et al., “Metal(loid)s (As, Hg, Se, Pb and Cd) in paddy soil: Bioavailability and potential risk to human health,” The Science of the Total Environment, vol. 699, p. 134330, Sep. 2019, doi: 10.1016/j.scitotenv.2019.134330.
  5. R. Rajalakshmi, S. Faizal, S. Sivasankaran, and R. Geetha, “RiceSeedNet: Rice seed variety identification using deep neural network,” Journal of Agriculture and Food Research, vol. 16, p. 101062, Feb. 2024, doi: 10.1016/j.jafr.2024.101062.
  6. Susan Hilton, Improving Processing Vegetable Yields Through Improved Production Practices, University of Tasmania, 2018.
  7. D. I. Patrício and R. Rieder, “Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review,” Computers and Electronics in Agriculture, vol. 153, pp. 69–81, Aug. 2018, doi: 10.1016/j.compag.2018.08.001.
  8. Y. Abbaspour-Gilandeh, A. Molaee, S. Sabzi, N. Nabipur, S. Shamshirband, and A. Mosavi, “A combined method of image processing and artificial neural network for the identification of 13 Iranian rice cultivars,” Agronomy, vol. 10, no. 1, p. 117, Jan. 2020, doi: 10.3390/agronomy10010117.
  9. K. Kiratiratanapruk et al., “Development of Paddy Rice Seed Classification Process using Machine Learning Techniques for Automatic Grading Machine,” Journal of Sensors, vol. 2020, pp. 1–14, Jul. 2020, doi: 10.1155/2020/7041310.
  10. T. S. R. Priya, A. R. L. E. Nelson, K. Ravichandran, and U. Antony, “Nutritional and functional properties of coloured rice varieties of South India: a review,” Journal of Ethnic Foods, vol. 6, no. 1, Oct. 2019, doi: 10.1186/s42779-019-0017-3.
  11. R. Alfred, J. H. Obit, C. P. -Y. Chin, H. Haviluddin and Y. Lim, “Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks,” in IEEE Access, vol. 9, pp. 50358-50380, 2021, doi: 10.1109/ACCESS.2021.3069449.
  12. “Rice varieties – IRRI Rice Knowledge Bank.” http://www.knowledgebank.irri.org/step-by-step-production/pre-planting/rice-varieties
  13. S. Mahajan, A. Das, and H. K. Sardana, “Image acquisition techniques for assessment of legume quality,” Trends in Food Science & Technology, vol. 42, no. 2, pp. 116–133, Jan. 2015, doi: 10.1016/j.tifs.2015.01.001.
  14. J. G. A. Barbedo, “A review on the main challenges in automatic plant disease identification based on visible range images,” Biosystems Engineering, vol. 144, pp. 52–60, Feb. 2016, doi: 10.1016/j.biosystemseng.2016.01.017.
  15. J. P. Shah, H. B. Prajapati and V. K. Dabhi, “A survey on detection and classification of rice plant diseases,” 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), Bangalore, India, 2016, pp. 1-8, doi: 10.1109/ICCTAC.2016.7567333.
  16. T. Bera, A. Das, J. Sil, and A. K. Das, “A survey on rice plant disease identification using image processing and data mining techniques,” in Advances in intelligent systems and computing, 2018, pp. 365–376. doi: 10.1007/978-981-13-1501-5_31.
  17. Samy Abu Naser, “An agent based intelligent tutoring system for parameter passing in java programming,” Journal of Theoretical and Applied Information Technology, vol. 4, no. 7, pp. 585-589, 2008.
  18. S. S. A. Naser, “Developing visualization tool for teaching AI searching algorithms,” Information Technology Journal, vol. 7, no. 2, pp. 350–355, Feb. 2008, doi: 10.3923/itj.2008.350.355.
  19. Samy S. Abu Naser, “A Qualitative Study of LP-ITS: Linear Programming Intelligent Tutoring System,” International Journal of Computer Science & Information Technology, vol. 4, no. 1, pp. 209-220, 2012, doi: 10.5121/ijcsit.2012.4116
  20. Abu Naser, S. S. and M. J. Al Shobaki, “Enhancing the use of Decision Support Systems for Re-engineering of Operations and Business-Applied Study on the Palestinian Universities,” Journal of Multidisciplinary Engineering Science Studies 2, no. 5, pp. 505-512, 2016.
  21. A. Sheeba, P. S. Kumar, M. Ramamoorthy, and S. Sasikala, “Microscopic image analysis in breast cancer detection using ensemble deep learning architectures integrated with web of things,” Biomedical Signal Processing and Control, vol. 79, p. 104048, Sep. 2022, doi: 10.1016/j.bspc.2022.104048.
  22. T. G. Devi, P. Neelamegam and S. Sudha, “Machine vision based quality analysis of rice grains,” 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, 2017, pp. 1052-1055, doi: 10.1109/ICPCSI.2017.8391871.
  23. N. Patel, H. Jayswal and A. Thakkar, “Rice quality analysis based on physical attributes using image processing technique,” 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 2017, pp. 42-47, doi: 10.1109/RTEICT.2017.8256555.
  24. Thae Nu Wah, Pann Ei San, Thandar Hlaing, “Analysis on feature extraction and classification of rice kernels for Myanmar rice using image processing techniques,” International Journal of Scientific and Research Publications, vol. 8, no. 8, pp. 603-606, 2018. doi: 10.29322/IJSRP.8.8.2018.p8078.
  25. N. A. Kuchekar, V. V. Yerigeri, “Rice grain quality grading using digital image processing techniques,” IOSR Journal of Electronics and Communication Engineering, vol. 13, no. 3, pp. 84-88, 2018.
  26. N. Hong Son and N. Thai-Nghe, “Deep Learning for Rice Quality Classification,” 2019 International Conference on Advanced Computing and Applications (ACOMP), Nha Trang, Vietnam, 2019, pp. 92-96, doi: 10.1109/ACOMP.2019.00021.
  27. X. Han et al., “Pre-trained models: Past, present and future,” AI Open, vol. 2, pp. 225–250, Jan. 2021, doi: 10.1016/j.aiopen.2021.08.002.
  28. M. Shafiq and Z. Gu, “Deep Residual Learning for Image Recognition: a survey,” Applied Sciences, vol. 12, no. 18, p. 8972, Sep. 2022, doi: 10.3390/app12188972.
  29. Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alexander A. Alemi, “Inception-v4, inception-ResNet and the impact of residual connections on learning,” Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 4278-4284, 2017. doi: 10.5555/3298023.3298188.
  30. S. R. Shah, S. Qadri, H. Bibi, S. M. W. Shah, M. I. Sharif, and F. Marinello, “Comparing Inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A case study on early detection of a rice disease,” Agronomy, vol. 13, no. 6, p. 1633, Jun. 2023, doi: 10.3390/agronomy13061633.
  31. Brett Koonce, Convolutional Neural Networks with Swift for Tensorflow – Image Recognition and Dataset Categorization, Springer, 2021.
  32. D. Sinha and M. El-Sharkawy, “Thin MobileNet: An Enhanced MobileNet Architecture,” 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, 2019, pp. 0280-0285, doi: 10.1109/UEMCON47517.2019.8993089.
  33. Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, Xin Wang, “A comprehensive survey of neural architecture search: Challenges and solutions,” ACM Computing Surveys (CSUR), vol. 54, no.4, pp. 1-34, 2021.
  34. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 1800-1807, doi: 10.1109/CVPR.2017.195.
  35. H. I. Fawaz et al., “InceptionTime: Finding AlexNet for time series classification,” Data Mining and Knowledge Discovery, vol. 34, no. 6, pp. 1936–1962, Sep. 2020, doi: 10.1007/s10618-020-00710-y.
  36. Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, Lucas Beyer, “Scaling vision transformers,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12104-12113, 2022.
  37. Zhi-Hua Zhou, Ensemble Methods: Foundations and Algorithms, ACM Digital Library, Accessed: Oct. 16, 2024. Available: https://dl.acm.org/doi/10.5555/2381019
  38. P. S. Yadav, R. S. Rao, A. Mishra, and M. Gupta, “Ensemble methods with feature selection and data balancing for improved code smells classification performance,” Engineering Applications of Artificial Intelligence, vol. 139, p. 109527, Oct. 2024, doi: 10.1016/j.engappai.2024.109527.
  39. M. Hasan et al., “Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation,” Frontiers in Plant Science, vol. 14, Aug. 2023, doi: 10.3389/fpls.2023.1234555.
  40. S. Tkatek, S. Amassmir, A. Belmzoukia, and J. Abouchabaka, “Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region,” International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, vol. 13, no. 5, p. 5942, Jun. 2023, doi: 10.11591/ijece.v13i5.pp5942-5950
Download PDF