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 …

Pedology focuses on understanding soil genesis in the field and includes soil classification
and mapping. Digital soil mapping (DSM) has evolved from traditional soil classification and …

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use
and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial …

Precision agriculture represents the new age of conventional agriculture. This is made
possible by the advancement of various modern technologies such as the internet of things …

Salinization and alkalinization are the most important land degradation processes in central
Iran. In this study we modelled the vertical and lateral variation of soil salinity (measured as …

This study was conducted to evaluate the efficacy of the ensemble machine learning model
to predict the spatial variation of soil organic carbon (SOC) concentration in a deciduous …

Demand for accurate soil information is increasing for various applications. This paper
investigates the history of soil survey in Iran, particularly more recent developments in the …

Salinization and alkalization are predominant environmental problem world-wide which their
accurate assessment is essential for determining appropriate ways to deal with land …

Traditional soil salinity studies, especially over large areas, are expensive and time-
consuming. Therefore, it is necessary to employ new methods to examine salinity of large …

The efficiency of different digital and conventional soil mapping approaches to produce
categorical maps of soil types is determined by cost, sample size, accuracy and the selected …