Authors : Abdul Koroma

Volume/Issue : Volume 11 - 2026, Issue 5 - May

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Google Scholar : https://tinyurl.com/3vxtj98f

Scribd : https://tinyurl.com/dwddm582

DOI : https://doi.org/10.38124/ijisrt/26may2119

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : The current research focuses on studying rice production, which accounts for about 62% of the national energy intake, as an important element of food security in Sierra Leone. Currently, there is no objective approach to predicting rice yields in a timely manner. In this work, we propose a machine learning model that uses multi-temporal Sentinel-2 images, the ERA5-Land and CHIRPS weather datasets, and climate forecasting datasets to predict early-season rice yields for Kambia and Bombali districts in Sierra Leone. A training dataset with 1,774 samples of bi-weekly data collected during seven rice-growing seasons (2018-2024) is used to optimize an ensemble consisting of Ridge Regression, Random Forest, and XGBoost.

Keywords : Rice Yield Prediction; Sentinel-2; Machine Learning; Ensemble Model; Sierra Leone; Food Security; Early Warning Systems.

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