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Crop Recommendation System Using Soil Content and Weather

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
P. Phanindra kumar Reddy
V. Madhu Sudhan Reddy
K. Nithin
P. Visweswara Rao
K. S. Maajid Hussain
Published in
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the transformative potential of machine learning models in agriculture, focusing on crop recommendation systems that leverage soil content and weather data. The study explores the use of Random Forest, Decision Trees, and Support Vector Machines, each achieving impressive accuracy rates of 99%, 96.7%, and 92% respectively. The integration of weather forecasting through the PyOWM library adds a dynamic layer to the system, providing real-time and five-day weather predictions. The chapter also highlights the development of a user-friendly interface using Streamlit, making the system accessible and practical for farmers. The results underscore the reliability and accuracy of these models in aiding farmers to make informed decisions, ultimately contributing to sustainable and efficient agricultural practices.
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Title
Crop Recommendation System Using Soil Content and Weather
Authors
P. Phanindra kumar Reddy
V. Madhu Sudhan Reddy
K. Nithin
P. Visweswara Rao
K. S. Maajid Hussain
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering

Print ISBN: 978-981-9502-68-4

Electronic ISBN: 978-981-9502-69-1

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-0269-1_8
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