Prediction of heavy metal concentrations in soil using machine learning models

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Machine learning for regional modelling of soil depth in the Western Cape of South Africa by Du Plessis, Juan

Subjects: “…Digital soil mapping…”
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Efficacy of machine learning, earth observation and geomorphometry for mapping salt-affected soils in irrigation fields by Vermeulen, Divan

Subjects: “…Soil management -- Geology -- Statistical methods…”
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Robustness of machine learning algorithms to skewness, spatial dependence, and sample size compared to BME in soil fertility prediction

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The effect of measurement error and land management covariates on machine learning derived digital soil maps by Malatji, Boitumelo Raymond

Subjects: “…Machine learning…”
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Driving force analysis of soil quality across China through an explainable deep learning framework

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Machine learning approach for site classification by Brits, Laurence Armand

Subjects: “…Machine learning…”
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Indirect soil salinity detection in irregated areas using earth observation methods by Muller, Sybrand Jacobus

Subjects: “…Soil salinity…”
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Object-based geomorphometry and soil spectroscopy in a fluvial terrace dominated landscape by Louw, Gerrit Jacobus

Subjects: “…Machine Learning…”
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Digital soil mapping techniques across multiple landscape scales in South Africa by Trevan, Flynn

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Evaluating soil and terrain variables in a production environment: implications for agricultural land assessment by Barichievy, Kurt Russell

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Evaluation of erosion models and field assessment methods as tools for monitoring and evaluation of soil erosion in landcare

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Exploring methodologies for assessing the outcome of soil management practices in Unilever's Sustainable Agriculture Code by Day, Mark

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Fruit detection in an orchard using deep learning approaches by Koech, Kiprono Elijah

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Seasonal patterns of evapotranspiration, soil moisture, rainfall, and normalized difference vegetation index in a Nama-Karoo ecosystem by Gqaleni, Nosipho Onwabile

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Predicting tomato crop yield from weather data using statistical learning techniques by De Villiers, Margaret

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InSAR time series analysis and machine learning for ground subsidence monitoring and susceptibility mapping in Midvaal, South Africa by Maluleka, Thobani

“…Spatio-temporal analyses revealed that ground subsidence and uplift patterns were strongly influenced by land use, hydrological factors, vegetation cover, soil moisture, precipitation, and ground water storage anomalies. …”
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Predictive models for smart vineyards by Luttich, F. R.

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Evaluating the suitability of UAV data for mapping dominant plant species in a heterogenous fynbos seep wetland by Musungu, Kevin

“…Random Forest proved the most reliable, with generally better overall and per- class accuracies than the other machine learning classifiers. Lastly, the study assessed the effect of seasonal variability on the per-class performance of the machine learning classifiers and identified Spring as the optimum time of year for the classification of most of the plant species. …”
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Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia by Hirko, Deme Betele

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