International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Analyzing the Best Ways of Optimizing Rice Production through Machine Learning Technologies

Dr. Samantha RajapakshaDepartment of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri LankaMr. S.M.B. HarshanathDepartment of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri LankaD.A. WatawalaDepartment of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri LankaA.G.D.J. PremarathneDepartment of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri LankaP.G. Lakindu RansikaDepartment of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri LankaG.K. LiyanarachchiDepartment of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Vol 7 No 11 (2023): Volume 7, Issue 11, November 2023 | Pages: 436-443

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 17-11-2023

doi Logo doi.org/10.47001/IRJIET/2023.711059

pdf iconFull Text PDF
Abstract

Rice can be considered the main staple food for billions of people worldwide. However, the annual production of rice yield has remarkably decreased due to various reasons such as climatic changes and weather patterns, water scarcity, rice plant-based diseases, lack of maintenance, etc. The use of machine learning technology in optimizing rice production operations has attracted a lot of attention in recent years. The different ways that machine learning techniques may be used to improve the yield, quality, and efficiency of rice production and promotion are examined throughout this research paper. Rice plant-based diseases are also a major problem for local farmers which reduce the yield. Through this website, the farmers were able to find out the best solutions for their existing matters easily with the use of new technologies. The local farmers were able to get new ideas through this website. With the help of this study, farmers will be given a comprehensive solution to problems including resource scarcity, market fluctuations, and climatic uncertainty. The website aims to provide farmers with useful information for making educated decisions by combining real-time data, predictive analytics, and user-friendly interfaces. This study investigates how technology might change rice farming, enhancing productivity, environmental sustainability, and ultimately, food security around the world. The main goal of this research is to develop a website and mobile application to optimize the rice production process effectively and productively by addressing all the issues that the local farmers faced during their cultivation process.

Keywords

rice plant-based diseases, machine learning, classification, image processing, weather, climatic changes, prediction, and promotion.


Citation of this Article

Dr. Samantha Rajapaksha, Mr. S.M.B. Harshanath, D.A. Watawala, A.G.D.J. Premarathne, P.G. Lakindu Ransika, G.K. Liyanarachchi, “Analyzing the Best Ways of Optimizing Rice Production through Machine Learning Technologies” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 11, pp 436-443, November 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.711059

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. S. K. S. Durai and M. D. Shamili, “Smart farming using Machine Learning and Deep Learning techniques,” Decision Analytics Journal, vol. 3, p. 100041, Jun. 2022, doi: 10.1016/j.dajour.2022.100041.
  2. M. Harshith, A. Sahu, S. Indrakanti, R. K. Reddy, and S. Bhutada, “Optimizing Crop Yields through Machine Learning-Based Prediction,” JSRR, vol. 29, no. 4, pp. 27–33, Apr. 2023, doi: 10.9734/jsrr/2023/v29i41741.
  3. P. P. Courtenay and G. G. Marten, “Traditional Agriculture in Southeast Asia: A Human Ecology Perspective.,” Pacific Affairs, vol. 60, no. 1, p. 137, 1987, doi: 10.2307/2758861.
  4. M. A. A. Parviz Koohafkan, “Enduring Farms: Climate Change, Smallholders and Traditional Farming Communities”, [Online]. Available: Altieri, M. A., & Koohafkan, P. (2008). Enduring farms: Climate change, smallholders, and traditional farming communities. Penang: Third World Network (TWN).
  5. D. Gosai, C. Raval, R. Nayak, H. Jayswal, and A. Patel, “Crop Recommendation System using Machine Learning,” IJSRCSEIT, pp. 558–569, Jun. 2021, doi: 10.32628/CSEIT2173129.
  6. O. C. Doering, J. C. Randolph, J. Southworth, and R. A. Pfeifer, Eds., Effects of Climate Change and Variability on Agricultural Production Systems. Boston, MA: Springer US, 2002. doi: 10.1007/978-1-4615-0969-1.
  7. V. K. Vishnoi, K. Kumar, and B. Kumar, “Crop Disease Classification Through Image Processing and Machine Learning Techniques Using Leaf Images,” in 2021 First International Conference on Advances in Computing and Future Communication Technologies (ICACFCT), Meerut, India: IEEE, Dec. 2021, pp. 27–32. doi: 10.1109/ICACFCT53978.2021.9837353.
  8. V. Rajpoot, A. Tiwari, and A. S. Jalal, “Automatic early detection of rice leaf diseases using hybrid deep learning and machine learning methods,” Multimed Tools Appl, Mar. 2023, doi: 10.1007/s11042-023-14969-y.
  9. Rohit Kumar Awasthi and Srikant Singh, “An Overview of Machine Learning Methods for the Detection of Diseases in Rice Plants in Agricultural Research,” IJSRST, pp. 837–846, Jun. 2023, doi: 10.32628/IJSRST523103150.
  10. B. Chakraborty et al., “Detection of Rice Blast Disease (Magnaporthe grisea) Using Different Machine Learning Techniques,” IJECC, vol. 13, no. 8, pp. 2256–2264, Jun. 2023, doi: 10.9734/ijecc/2023/v13i82190.
  11. C. Bowden, T. Foster, and B. Parkes, “Identifying links between monsoon variability and rice production in India through machine learning,” Sci Rep, vol. 13, no. 1, p. 2446, Feb. 2023, doi: 10.1038/s41598-023-27752-8.
  12. J. Zhang et al., “Optimizing rice in-season nitrogen topdressing by coupling experimental and modeling data with machine learning algorithms,” Computers and Electronics in Agriculture, vol. 209, p. 107858, Jun. 2023, doi: 10.1016/j.compag.2023.107858.
  13. Shah Abdul Latif University and I. A. Supro, “Rice yield prediction and optimization using association rules and neural network methods to enhance agribusiness,” IJST, vol. 13, no. 13, pp. 1367–1379, Apr. 2020, doi: 10.17485/IJST/v13i13.79.
  14. A.Thammastitkul and J. Petsuwan, “Thai Hom Mali rice grading using machine learning and deep learning approaches,” IJ-AI, vol. 12, no. 2, p. 815, Jun. 2023, doi: 10.11591/ijai.v12.i2.pp815-822.
  15. L. De Oliveira Carneiro et al., “Characterizing and Predicting the Quality of Milled Rice Grains Using Machine Learning Models,” Agri Engineering, vol. 5, no. 3, pp. 1196–1215, Jul. 2023, doi: 10.3390/agriengineering5030076.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.