Hybrid Classification Model with Tuned Weights for Crop Yield Prediction
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10781-x.pdf
Reference29 articles.
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2. Elavarasan, D., & Vincent, P. M. D. R. (2021). A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters. Journal of Ambient Intelligence and Humanized Computing, 12, 10009–10022. https://doi.org/10.1007/s12652-020-02752-y
3. Bi, L., & Hu, G. (2021). A genetic algorithm-assisted deep learning approach for crop yield prediction. Soft Computing, 25, 10617–10628. https://doi.org/10.1007/s00500-021-05995-9
4. Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., & Khan, N. (2021). A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction. IEEE Access, 9, 63406–63439. https://doi.org/10.1109/ACCESS.2021.3075159
5. Prasad, N. R., Patel, N. R., & Danodia, A. (2021). Crop yield prediction in cotton for regional level using random forest approach. Spatial Information Research, 29, 195–206. https://doi.org/10.1007/s41324-020-00346-6
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