A new hyperparameter to random forest: application of remote sensing in yield prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01156-8.pdf
Reference46 articles.
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2. Ashourloo, D, Manafifard M, et al. (2022) Wheat yield prediction based on Sentinel-2, regression and machine learning models in Hamedan, Iran. Scientia Iranica. https://doi.org/10.24200/sci.2022.57809.5429
3. Breiman L (2001) Random Forests. Mach Learn 45(1):5–32
4. Çakır, Y, Kırcı M, et al. (2014) Yield prediction of wheat in south-east region of Turkey by using artificial neural networks. 2014 The Third International Conference on Agro-Geoinformatics, Beijing, China
5. Chaudhary A, Kolhe S et al (2016) An improved random forest classifier for multi-class classification. Inf Process Agric 3(4):215–222
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