Prediction of Rice Using Random Forest Algorithm with Geographical Information System (GIS)

Author:

Suarez Jane Kristine G.ORCID,Abamo Jenny Lyn V.ORCID

Publisher

Springer Nature Singapore

Reference17 articles.

1. Saiz-Rubio V, Rovira-Más F (2020) From smart farming towards agriculture 5.0: a review on crop data management. Agronomy 10(2):207

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3. Li H, Zhang C, Zhang S, Atkinson PM (2020) Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the random forest algorithm. Int J Appl Earth Obs Geoinf 87:102032

4. Roux YL, Grobler J (2020) Investigating the use of machine learning for South African edible garnish yield prediction. In: International conference on innovative techniques and applications of artificial intelligence, Dec 2020. Springer, Cham, pp 202–214

5. Ahmed GN, Kamalakkannan S (2022) Micronutrient classification in IoT based agriculture using machine learning (ML) algorithm. In: 2022 4th international conference on smart systems and inventive technology (ICSSIT), pp 1–9. https://doi.org/10.1109/ICSSIT53264.2022.9716293

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