Machine learning in agriculture: A comprehensive updated review
The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …
artificial intelligent systems for the sake of making value from the ever-increasing data …
Machine learning for digital soil map**: Applications, challenges and suggested solutions
The uptake of machine learning (ML) algorithms in digital soil map** (DSM) is
transforming the way soil scientists produce their maps. Within the past two decades, soil …
transforming the way soil scientists produce their maps. Within the past two decades, soil …
Selecting appropriate machine learning methods for digital soil map**
Digital soil map** (DSM) increasingly makes use of machine learning algorithms to
identify relationships between soil properties and multiple covariates that can be detected …
identify relationships between soil properties and multiple covariates that can be detected …
Predicting and map** of soil organic carbon using machine learning algorithms in Northern Iran
Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding
the chemical, physical, and biological functions of the soil. This study proposes machine …
the chemical, physical, and biological functions of the soil. This study proposes machine …
Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates
In the digital soil map** (DSM) framework, machine learning models quantify the
relationship between soil observations and environmental covariates. Generally, the most …
relationship between soil observations and environmental covariates. Generally, the most …
[HTML][HTML] Machine learning and remote sensing techniques applied to estimate soil indicators–review
The demand for food based on intensive agriculture has decreased soil quality, posing great
challenges such as increasing agricultural productivity and promoting environmental …
challenges such as increasing agricultural productivity and promoting environmental …
Digital soil map** algorithms and covariates for soil organic carbon map** and their implications: A review
This article reviews the current research and applications of various digital soil map**
(DSM) techniques used to map Soil Organic Carbon (SOC) concentration and stocks …
(DSM) techniques used to map Soil Organic Carbon (SOC) concentration and stocks …
Land suitability assessment and agricultural production sustainability using machine learning models
Land suitability assessment is essential for increasing production and planning a
sustainable agricultural system, but such information is commonly scarce in the semi-arid …
sustainable agricultural system, but such information is commonly scarce in the semi-arid …
Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI
Accurate assessment of soil salinization is considered as one of the most important steps in
combating global climate change, especially in arid and semi-arid regions. Multi-spectral …
combating global climate change, especially in arid and semi-arid regions. Multi-spectral …
High-resolution digital map** of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning …
Soil organic carbon (SOC) and soil total nitrogen (STN) are important indicators of soil
health and play a key role in the global carbon and nitrogen cycles. High-resolution radar …
health and play a key role in the global carbon and nitrogen cycles. High-resolution radar …