A Hybrid CFS Filter and RF-RFE Wrapper-Based Feature Extraction for Enhanced Agricultural Crop Yield Prediction Modeling
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- R. Srinivasan & C.P. Lohith, 2017. "Strategic Marketing and Innovation for Indian MSMEs," India Studies in Business and Economics, Springer, number 978-981-10-3590-6, January.
- Torres, Alfonso F. & Walker, Wynn R. & McKee, Mac, 2011. "Forecasting daily potential evapotranspiration using machine learning and limited climatic data," Agricultural Water Management, Elsevier, vol. 98(4), pages 553-562, February.
- Saeid Hamzeh & Marzieh Mokarram & Azadeh Haratian & Harm Bartholomeus & Arend Ligtenberg & Arnold K. Bregt, 2016. "Feature Selection as a Time and Cost-Saving Approach for Land Suitability Classification (Case Study of Shavur Plain, Iran)," Agriculture, MDPI, vol. 6(4), pages 1-13, October.
- Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
- Bommert, Andrea & Sun, Xudong & Bischl, Bernd & Rahnenführer, Jörg & Lang, Michel, 2020. "Benchmark for filter methods for feature selection in high-dimensional classification data," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
- Saikai, Yuji & Patel, Vivak & Mitchell, Paul, 2020. "Machine learning for optimizing complex site-specific management," 2020 Conference (64th), February 12-14, 2020, Perth, Western Australia 305238, Australian Agricultural and Resource Economics Society.
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- Priya Brata Bhoi & Veeresh S. Wali & Deepak Kumar Swain & Kalpana Sharma & Akash Kumar Bhoi & Manlio Bacco & Paolo Barsocchi, 2021. "Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach," Agriculture, MDPI, vol. 11(9), pages 1-27, August.
- Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
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Keywords
correlation filter; crop yield prediction; hybrid feature extraction; machine learning; recursive feature elimination wrapper; precision agriculture;All these keywords.
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