GIS-Enhanced Crop Yield Modeling with Machine Learning

Authors

  • Venkatesh S.D. Dayananda Sagar Academy of Technology and Management, Karnataka, India
  • Chitra K. Dayananda Sagar Academy of Technology and Management, Karnataka, India
  • Harilakshami V.M. Dayananda Sagar Academy of Technology and Management, Karnataka, India

Keywords:

Classification Algorithms, Decision Tree, KNN, Machine Learning in Agriculture

Abstract

India, with its vast population and agrarian society, faces challenges in agricultural practices.
Many farmers continue to grow the same crops repeatedly without experimenting with new
varieties. To address these issues, we have developed a system using machine learning
algorithms aimed at helping farmers. Our system recommends the most suitable crops for
specific lands based on soil content and weather conditions. It also provides information on
the appropriate type and number of fertilizers and the necessary seeds for cultivation. By using our system, farmers can diversify their crops, potentially increase their profit margins, and reduce soil pollution.

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Published

2024-12-04

How to Cite

S.D., V., K., C., & V.M., H. (2024). GIS-Enhanced Crop Yield Modeling with Machine Learning. Journal of Innovation and Technology, 2024. Retrieved from https://iuojs.intimal.edu.my/index.php/joit/article/view/623