Systematic review papers of Machine Learning approaches for Soil Organic Carbon estimation in Africa

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agricultureenvironment and energy

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paper

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This review systematically examines the application of machine learning (ML) techniques in estimating Soil Organic Carbon (SOC) across African landscapes. The systematic review process was based on the PRISMA 2020 guidelines, and 11 articles were deemed suitable for in-depth analysis. This RIS file includes the bibliographic references of the 11 articles that were selected.

Estimation Soil Organic Carbon Using Hyperspectral Imaging and Machine Learning: A Case Study in Moroccan Agricultural Soils

Accurate estimation of Soil Organic Carbon (SOC) is essential for sustainable soil management and ca

Extrapolation of Digital Soil Mapping Approaches for Soil Organic Carbon Stock Predictions in an Afromontane Environment

Soil scientists can aid in an essential part of ecological conservation and rehabilitation by quanti

Predictors of Anemia in Ethiopia: A Systematic-Review of Machine Learning Approaches

ABSTRACT Background Anemia remains a criti

Model and measure: comparing soil organic carbon stocks quantification approaches at field scale in South Africa

There is an ongoing debate about the most appropriate approach for quantifying changes in soil or

Estimation of soil temperature for agricultural applications in South Africa using machine-learning methods

This study was undertaken to investigate the potential of using machine-learning approaches as alter

Prediction of Soil Organic Carbon for Ethiopian Highlands Using Soil Spectroscopy

Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia.

Systematic review papers of Machine Learning approaches for Soil Organic Carbon estimation in Africa

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