Research Article

Predictive Modeling of Agricultural Yield Using Multi-Source Geospatial Data

Authors

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

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

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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
Document Type Research Article
Views 899
Downloads 366

How to Cite

Siraj, N. ., Swaroop, S. ., Raj, T. ., & Mishra, A. . (2024). Predictive Modeling of Agricultural Yield Using Multi-Source Geospatial Data. Soft Computing Fusion With Applications , 1(4), 253-262. https://doi.org/10.22105/scfa.v1i4.71

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