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https://doi.org/10.2139/ssrn.4668776

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inferredcites →https://doi.org/10.1002/cem.2977openalex-extracted
One‐dimensional convolutional neural networks for spectroscopic signal regression
inferredcites →https://doi.org/10.1002/cem.3122openalex-extracted
End‐to‐end quantitative analysis modeling of near‐infrared spectroscopy based on convolutional neural network
inferredcites →https://doi.org/10.1007/s10661-013-3109-3openalex-extracted
The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union
inferredcites →https://doi.org/10.1007/s10812-019-00891-5openalex-extracted
Prediction Results of Different Modeling Methods in Soil Nutrient Concentrations Based on Spectral Technology
inferredcites →https://doi.org/10.1016/j.aca.2019.01.002openalex-extracted
DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis
inferredcites →https://doi.org/10.1016/j.chemolab.2007.10.001openalex-extracted
A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra
inferredcites →https://doi.org/10.1016/j.ecolind.2019.106002openalex-extracted
Estimating the spatial distribution of soil total nitrogen and available potassium in coastal wetland soils in the Yellow River Delta by incorporating multi-source data
inferredcites →https://doi.org/10.1016/j.envsoft.2011.11.002openalex-extracted
Soil erodibility estimation using LUCAS point survey data of Europe
inferredcites →https://doi.org/10.1016/j.geoderma.2018.07.004openalex-extracted
Spectral features extraction for estimation of soil total nitrogen content based on modified ant colony optimization algorithm
inferredcites →https://doi.org/10.1016/j.geoderma.2020.114208openalex-extracted
Simultaneous prediction of soil properties from VNIR-SWIR spectra using a localized multi-channel 1-D convolutional neural network
inferredcites →https://doi.org/10.1016/j.geoderma.2020.114616openalex-extracted
Combination of Convolutional Neural Networks and Recurrent Neural Networks for predicting soil properties using Vis–NIR spectroscopy
inferredcites →https://doi.org/10.1016/j.geodrs.2018.e00198openalex-extracted
Using deep learning to predict soil properties from regional spectral data
inferredcites →https://doi.org/10.1016/j.ifacol.2018.08.121openalex-extracted
Prediction of Total Nitrogen in Soil Based on Random Frog Leaping Wavelet Neural Network
inferredcites →https://doi.org/10.1016/j.soilbio.2013.10.022openalex-extracted
Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach
inferredcites →https://doi.org/10.1016/s1002-0160(08)60004-1openalex-extracted
Estimation of Some Chemical Properties of an Agricultural Soil by Spectroradiometric Measurements
inferredcites →https://doi.org/10.1038/nature14539openalex-extracted
Deep learning
inferredcites →https://doi.org/10.1109/crv.2015.15openalex-extracted
Deep Learning Architectures for Soil Property Prediction
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-extracted
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvpr.2018.00716openalex-extracted
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01055openalex-extracted
MetaFormer is Actually What You Need for Vision
inferredcites →https://doi.org/10.1111/ejss.12499openalex-extracted
LUCAS Soil, the largest expandable soil dataset for Europe: a review
inferredcites →https://doi.org/10.1111/j.1365-2389.2012.01443.xopenalex-extracted
Quantitative analysis of soil nitrogen and carbon at a farm scale using visible and near infrared spectroscopy coupled with wavelength reduction
inferredcites →https://doi.org/10.1177/0003702820987847openalex-extracted
Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra
inferredcites →https://doi.org/10.1255/jnirs.1219openalex-extracted
Validation of the near Infrared Spectroscopy Method for Determining Soil Organic Carbon by Employing a Proficiency Assay for Fertility Laboratories
inferredcites →https://doi.org/10.1371/journal.pone.0066409openalex-extracted
Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy
inferredcites →https://doi.org/10.3390/analytica3030020openalex-extracted
Deep Learning for Raman Spectroscopy: A Review
inferredcites →https://doi.org/10.3390/s18093169openalex-extracted
Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery
inferredcites →https://doi.org/10.3390/s20247078openalex-extracted
Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy
inferredcites →https://doi.org/10.48550/arxiv.1403.1687openalex-extracted
Rigid-Motion Scattering for Texture Classification
inferredcites →https://doi.org/10.48550/arxiv.1602.07360openalex-extracted
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
inferredcites →https://doi.org/10.48550/arxiv.1808.05517openalex-extracted
Network Decoupling: From Regular to Depthwise Separable Convolutions
inferredcites →https://doi.org/10.48550/arxiv.2105.01601openalex-extracted
MLP-Mixer: An all-MLP Architecture for Vision
inferredcites →https://doi.org/10.5445/ir/1000089586openalex-extracted
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
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