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 …
Iterative integration of deep learning in hybrid Earth surface system modelling
Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …
processes. Deep learning (DL), with the data-driven strength of neural networks, has …
Soil salinity: A global threat to sustainable development
A Singh - Soil Use and Management, 2022 - Wiley Online Library
Soil is a vital resource for feeding the burgeoning global population, and it is also essential
for realizing most of the 'United Nations Sustainable Development Goals (SDGs)'. For …
for realizing most of the 'United Nations Sustainable Development Goals (SDGs)'. For …
Updated soil salinity with fine spatial resolution and high accuracy: The synergy of Sentinel-2 MSI, environmental covariates and hybrid machine learning approaches
Soil salinization is the main source of global soil degradation. It has impeded progress
towards sustainable development goals (SDGs) by threatening 20% of irrigated areas …
towards sustainable development goals (SDGs) by threatening 20% of irrigated areas …
Capability of Sentinel-2 MSI data for monitoring and map** of soil salinity in dry and wet seasons in the Ebinur Lake region, ** using machine learning algorithms with the Sentinel-2 MSI in arid areas, China
J Wang, J Peng, H Li, C Yin, W Liu, T Wang, H Zhang - Remote Sensing, 2021 - mdpi.com
Accurate monitoring of soil salinization plays a key role in the ecological security and
sustainable agricultural development of arid regions. As a branch of artificial intelligence …
sustainable agricultural development of arid regions. As a branch of artificial intelligence …
Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy
The spectral preprocessing method has become an integral component of soil analysis
through visible and near-infrared (Vis-NIR) spectroscopy. Various spectral pretreatment …
through visible and near-infrared (Vis-NIR) spectroscopy. Various spectral pretreatment …
Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case …
In arid and semi-arid regions, water-quality problems are crucial to local social demand and
human well-being. However, the conventional remote sensing-based direct detection of …
human well-being. However, the conventional remote sensing-based direct detection of …
Estimating agricultural soil moisture content through UAV-based hyperspectral images in the arid region
Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important
monitoring technology for the soil moisture content (SMC) of agroecological systems in arid …
monitoring technology for the soil moisture content (SMC) of agroecological systems in arid …
New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in …
X Wang, F Zhang, VC Johnson - Remote Sensing of Environment, 2018 - Elsevier
This study aimed to improve the potential of Analytical Spectral Devices (ASD) hyperspectral
and Landsat Operational Land Imager (OLI) data in predicting soil organic matter content …
and Landsat Operational Land Imager (OLI) data in predicting soil organic matter content …