Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

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

Domain:

agriculturegeospatialclimate

Record type:

paper
Creator:
Abd
Publisher:
Int
Host:
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.

Visit

doi.org

Languages

Themne

Similar

Farm-level yield prediction for maize, rice, and beans in Tanzania using machine learning and multi-source agricultural dataSimulating rice yield in the major rice-growing environments in sub-Saharan Africa using an ensemble machine learning approachSentinel-1 and Sentinel-2 data fusion for wheat and rice yield forecasting in the Nile DeltaEarly cereal yield prediction using machine learning and Sentinel-1 & Sentinel-2 satellite dataRetrieval of rice biophysical parameters from Sentinel‑2 using parsimonious multi‑output machine learningMachine Learning Models for Climate Prediction and Adaptation in Sierra Leone

Farm-level yield prediction for maize, rice, and beans in Tanzania using machine learning and multi-source agricultural data

Simulating rice yield in the major rice-growing environments in sub-Saharan Africa using an ensemble machine learning approach

Food security is a major issue in Sub-Saharan Africa (SSA) as a result of interrelated challenges su

Sentinel-1 and Sentinel-2 data fusion for wheat and rice yield forecasting in the Nile Delta

Early cereal yield prediction using machine learning and Sentinel-1 & Sentinel-2 satellite data

International audience Forecasting cereal production is crucial for food security, es

Retrieval of rice biophysical parameters from Sentinel‑2 using parsimonious multi‑output machine learning

Abstract Rice ( Oryza sativa

Machine Learning Models for Climate Prediction and Adaptation in Sierra Leone

Climate change poses significant challenges to Sierra Leone's agricultural productivity and