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An Ensemble Machine Learning Approach for Pre-Harvest Rice Yield Forecasting in Sierra Leone Using Sentinel-2 and Multi-Source Climate Data

Domaine:

agriculturegeospatialclimate

Type de record:

paper
Créateur:
Abd
Éditeur:
Int
Hôte:
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.

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