Weather Sense: Scraping and Deep Learning for Weather Analysis and Prediction

Authors

  • Devidas Bhat Assistant Professor, Department of Information Science& Eng., Nitte University (Deemed to be),NMAM Institute of Technology, Nitte, India
  • Balasubramani R. Professor, Department of Information Science& Eng., Nitte University (Deemed to be),NMAM Institute of Technology, Nitte, India

Keywords:

Deep learning, Agriculture, Web Scraping, Machine learning, Weather data analysis, Crop yield prediction

Abstract

Agriculture is essential in ensuring food security and development in the country. Maximizing scarce arable land is a pressing challenge in today's urbanization era. Agriculture can be made more efficient using technology and information science. This article presents an integrated approach to education in Indian agriculture that uses climate data to accurately analyze environmental factors such as temperature, soil, wind speed, and precipitation. The framework chooses the most accurate algorithm based on analysis and comparison. By providing accurate weather information, farmers can make informed decisions about planting, pest and disease management, and other factors affecting crop growth. The ultimate goal is to increase farmers' profits and promote sustainable agriculture. Capacity can be further developed by integrating features that help farmers use sustainable technologies in specific climate models

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References

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Published

05.12.2023

How to Cite

Bhat, D. ., & R., B. . (2023). Weather Sense: Scraping and Deep Learning for Weather Analysis and Prediction. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 123–128. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4045

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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