Predictive Modeling of Agricultural Yield Using Multi-Source Geospatial Data

Authors

  • Nomaan Siraj Department of Computer Sciencekalinga, Institute of Industrial Technology (KIIt) University, Bhubaneswar, Odisha, India.
  • Swayam Swaroop * Department of Computer Sciencekalinga, Institute of Industrial Technology (KIIt) University, Bhubaneswar, Odisha, India.
  • Tanya Raj Department of Computer Sciencekalinga, Institute of Industrial Technology (KIIt) University, Bhubaneswar, Odisha, India.
  • Anubhav Mishra Department of Computer Sciencekalinga, Institute of Industrial Technology (KIIt) University, Bhubaneswar, Odisha, India.

https://doi.org/10.22105/scfa.v1i4.71

Abstract

Accurate prediction of agricultural yield is crucial for ensuring food security and optimizing resource allocation. This project aims to develop a robust predictive model that leverages the power of remote sensing, weather data, and soil information to estimate crop yield accurately. By integrating advanced machine learning and deep learning techniques with geospatial analysis, we strive to improve the precision and reliability of yield forecasts. The proposed methodology involves several key steps: 1) data acquisition and preprocessing, 2) model development and training, and 3) deployment and visualization.

Keywords:

Agricultural yield prediction, Remote sensing, Machine learning, Deep learning, Geospatial analysis, Satellite imagery, Predictive modeling

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Published

2024-12-26

Issue

Vol. 1 No. 4 (2024): Soft Computing Fusion with Applications

Section

Articles

How to Cite

Predictive Modeling of Agricultural Yield Using Multi-Source Geospatial Data. (2024). Soft Computing Fusion With Applications , 1(4), 253-262. https://doi.org/10.22105/scfa.v1i4.71

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