[HTML][HTML] Basic and deep learning models in remote sensing of soil organic carbon estimation: A brief review
Understanding soil organic carbon (SOC) is critical to, among others, atmospherics and
terrestrial carbon balance and climate change mitigation. This has necessitated …
terrestrial carbon balance and climate change mitigation. This has necessitated …
A critical systematic review on spectral-based soil nutrient prediction using machine learning
Abstract The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in
addressing persistent starvation and working towards zero hunger by 2030 through global …
addressing persistent starvation and working towards zero hunger by 2030 through global …
[HTML][HTML] The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data
The number of samples used in the calibration data set affects the quality of the generated
predictive models using visible, near and shortwave infrared (VIS–NIR–SWIR) spectroscopy …
predictive models using visible, near and shortwave infrared (VIS–NIR–SWIR) spectroscopy …
Soil properties: Their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks
L Zhong, X Guo, Z Xu, M Ding - Geoderma, 2021 - Elsevier
Soil, as a non-renewable resource, should be monitored continuously to prevent its
degradation and promote sustainable agriculture. Soil spectroscopy in the visible-near …
degradation and promote sustainable agriculture. Soil spectroscopy in the visible-near …
[HTML][HTML] Spectral fusion modeling for soil organic carbon by a parallel input-convolutional neural network
Abstract Visible-to-near-infrared (vis–NIR) and mid-infrared (MIR) spectroscopy have been
widely utilized for the quantitative estimation of soil organic carbon (SOC). The fusion of vis …
widely utilized for the quantitative estimation of soil organic carbon (SOC). The fusion of vis …
An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran
Traditional soil salinity studies are time-consuming and expensive, especially over large
areas. This study proposed an innovative deep learning convolutional neural network (DL …
areas. This study proposed an innovative deep learning convolutional neural network (DL …
From spectra to plant functional traits: Transferable multi-trait models from heterogeneous and sparse data
Large-scale information on several vegetation properties ('plant traits') is critical to assess
ecosystem functioning, functional diversity and their role in the Earth system. Hyperspectral …
ecosystem functioning, functional diversity and their role in the Earth system. Hyperspectral …
Estimation of sugar content in wine grapes via in situ VNIR–SWIR point spectroscopy using explainable artificial intelligence techniques
Spectroscopy is a widely used technique that can contribute to food quality assessment in a
simple and inexpensive way. Especially in grape production, the visible and near infrared …
simple and inexpensive way. Especially in grape production, the visible and near infrared …
Semi-supervised DNN regression on airborne hyperspectral imagery for improved spatial soil properties prediction
A number of algorithms have been developed for soil organic matter (SOM) or soil heavy
metal detection in airborne hyperspectral imagery with high spatial and spectral resolutions …
metal detection in airborne hyperspectral imagery with high spatial and spectral resolutions …
[HTML][HTML] Near-infrared spectroscopy and machine learning for classification of food powders during a continuous process
In food production environments, the wrong powder material is occasionally loaded onto a
production line which impacts food safety, product quality, and production economics. The …
production line which impacts food safety, product quality, and production economics. The …