| Issue |
E3S Web Conf.
Volume 591, 2024
International Conference on Renewable Energy Resources and Applications (ICRERA-2024)
|
|
|---|---|---|
| Article Number | 08002 | |
| Number of page(s) | 7 | |
| Section | Communication and Signal Processing | |
| DOI | https://doi.org/10.1051/e3sconf/202459108002 | |
| Published online | 14 November 2024 | |
Millet Crop Yield Variation through Feature Extraction Using XGBoost
1,2,4,5 Department of CSE(AI&ML), Institute of aeronautical engineering, Hyderabad, Telangana, India.
3 Department of ECE, Malla Reddy Engineering College, Hyderabad, Telangana, India.
* Corresponding author: dr.skjakeerhussain@gmail.com
Agriculture, being a vital industry, relies significantly on predicting and improving crop yields for sustainable food production. In this context, millet, a staple crop in various regions globally, holds immense agricultural importance due to its nutritional value and resilience to harsh environmental conditions. The approach outlined in this study revolves around the utilization of advanced machine learning techniques, specifically XGBoost, which is a robust gradient boosting algorithm known for its effectiveness in handling structured data and making accurate predictions. This algorithm is employed to create a predictive model for forecasting millet yields.
Key words: Intrusion Detection Systems / Network Security / Cybersecurity / Network-based IDS / Host-based IDS / Detection Techniques / Threat Detection / Security Challenges / Future Trends
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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