Deep ensemble model with hybrid intelligence technique for crop yield prediction
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-18354-1.pdf
Reference37 articles.
1. Elavarasan D, Vincent PMDR (2021) A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters. J Ambient Intell Human Comput 12:10009–10022. https://doi.org/10.1007/s12652-020-02752-y
2. Iniyan S, Jebakumar R (2021) Mutual Information Feature Selection (MIFS) based crop yield prediction on corn and soybean crops using Multilayer Stacked Ensemble Regression (MSER). Wireless Pers Commun. https://doi.org/10.1007/s11277-021-08712-9
3. Li T, Wang C, Li H, Sun H (2020) Yield prediction model of rice and wheat crops based on ecological distance algorithm, Environ Technol Innov, vol. 20. https://doi.org/10.1016/j.eti.2020.101132
4. Shidnal S, Latte MV, Kapoor A (2021) Crop yield prediction: two-tiered machine learning model approach. Int j inf tecnol 13:1983–1991. https://doi.org/10.1007/s41870-019-00375-x
5. Khosla E, Dharavath R, Priya R (2020) Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression. Environ Dev Sustain 22:5687–5708. https://doi.org/10.1007/s10668-019-00445-x
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