An overview and comparison of machine-learning techniques for classification purposes in digital soil map**
Abstract Machine-learning is the automated process of uncovering patterns in large datasets
using computer-based statistical models, where a fitted model may then be used for …
using computer-based statistical models, where a fitted model may then be used for …
[HTML][HTML] Smart applications and digital technologies in viticulture: A review
It is important to continuously monitor the long-term impact of viticultural management
practices and assess opportunities for improving the environmental footprint of vineyard …
practices and assess opportunities for improving the environmental footprint of vineyard …
Spatial prediction of major soil properties using Random Forest techniques-A case study in semi-arid tropics of South India
The purpose of the study is to map the spatial variation of major soil properties in
Bukkarayasamudrum mandal of Anantapur district, India using Random Forest model. The …
Bukkarayasamudrum mandal of Anantapur district, India using Random Forest model. The …
Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals
AP Sergeev, AG Buevich, EM Baglaeva, AV Shichkin - Catena, 2019 - Elsevier
A hybrid approach was proposed to simulate the spatial distribution of a number of heavy
metals in the surface layer of the soil. The idea of the method is to simulate a nonlinear large …
metals in the surface layer of the soil. The idea of the method is to simulate a nonlinear large …
Digital map** of soil particle‐size fractions for Nigeria
There is a growing need for spatially continuous and quantitative soil information for
environmental modeling and management, especially at the national scale. This study was …
environmental modeling and management, especially at the national scale. This study was …
Novel approach for soil classification using machine learning methods
In this study, we have proposed a new classification method for determining different soil
classes based on three machine learning approaches, namely: support vector classification …
classes based on three machine learning approaches, namely: support vector classification …
A self-training semi-supervised machine learning method for predictive map** of soil classes with limited sample data
Numerous machine learning models have been developed for constructing the relationship
between soil classes or properties and its environmental covariates in digital soil map** …
between soil classes or properties and its environmental covariates in digital soil map** …
Digital soil map** using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran
E Mahmoudabadi, A Karimi, GH Haghnia… - Environmental monitoring …, 2017 - Springer
Digital soil map** has been introduced as a viable alternative to the traditional map**
methods due to being fast and cost-effective. The objective of the present study was to …
methods due to being fast and cost-effective. The objective of the present study was to …
Digital soil map** of soil organic carbon stocks in Western Ghats, South India
Spatial information of soil carbon storage at national and global level is essential for soil
quality and environmental management. Improved knowledge on the amount and spatial …
quality and environmental management. Improved knowledge on the amount and spatial …
[HTML][HTML] Digital soil map** of peatland using airborne radiometric data and supervised machine learning–Implication for the assessment of carbon stock
Abstract Peatlands account for approx. 4.23 million km 2 of the land surface of Earth and
between 5% and 20% of the global soil carbon stock, however much uncertainty exists. The …
between 5% and 20% of the global soil carbon stock, however much uncertainty exists. The …