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

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.

Country : Sri Lanka

1 Dr. Samantha Rajapaksha2 Mr. S.M.B. Harshanath3 D.A. Watawala4 A.G.D.J. Premarathne5 P.G. Lakindu Ransika6 G.K. Liyanarachchi

  1. Department of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Department of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  3. Department of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  4. Department of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  5. Department of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  6. Department of Information Technology, University of Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 7, Issue 11, November 2023 pp. 436-443

doi.org/10.47001/IRJIET/2023.711059

Full Paper
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