Ruthika et al. (2026) Crop Yield Prediction using Machine Learning

Identification

• Journal: Open MIND
• Year: 2026
• Date: 2026-04-20
• Authors: Doli Ruthika, Dr. Prashant Bachanna, Katha Rithvika, R. Laxmi Narayana
• DOI: 10.5281/zenodo.19661900

Research Groups

• Institute of Aeronautical Engineering

Short Summary

This study proposes a machine-learning-based framework that integrates weather, soil, and satellite data to provide more accurate crop yield predictions than traditional manual methods.

Objective

• To develop a holistic predictive system capable of capturing both linear and complex nonlinear relationships in agricultural data to enhance crop yield forecasting for precision farming.

Study Configuration

• Spatial Scale: Not specified
• Temporal Scale: Not specified

Methodology and Data

• Models used: Linear Regression, Decision Trees, Random Forests, and Artificial Neural Networks (ANN).
• Data sources: Weather patterns, soil characteristics, satellite imagery, and historical crop performance datasets.

Main Results

• The integration of real-time environmental inputs and data-driven algorithms improves the accuracy of yield forecasts compared to traditional observation-based methods.
• The system enables more informed decision-making regarding planting schedules, irrigation management, and harvest timing.

Contributions

• The research provides a comprehensive technological framework that synthesizes diverse agricultural data streams (satellite, soil, and weather) with multiple machine learning architectures to support sustainable agricultural practices and smart farming.

Funding

• Not specified

Citation

@article{Ruthika2026Crop,
  author = {Ruthika, Doli and Bachanna, Dr.  Prashant and Rithvika, Katha and Narayana, R.  Laxmi},
  title = {Crop Yield Prediction using Machine Learning},
  journal = {Open MIND},
  year = {2026},
  doi = {10.5281/zenodo.19661900},
  url = {https://doi.org/10.5281/zenodo.19661900}
}