On the public citation graph

Detection of soil chemical composition by using near-infrared spectroscopy and visual coding techniques coupled with an improved deep learning model

https://doi.org/10.2139/ssrn.6172773
0 signed · 11 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/0169-7439(95)80098-topenalex-snapshot
Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data
inferredcites →https://doi.org/10.1016/j.biosystemseng.2006.11.012openalex-snapshot
Contaminant Classification of Poultry Hyperspectral Imagery using a Spectral Angle Mapper Algorithm
inferredcites →https://doi.org/10.1016/j.biosystemseng.2021.01.003openalex-snapshot
Improved prediction of protein content in wheat kernels with a fusion of scatter correction methods in NIR data modelling
inferredcites →https://doi.org/10.1016/j.compag.2022.106802openalex-snapshot
Deep learning assisted continuous wavelet transform-based spectrogram for the detection of chlorophyll content in potato leaves
inferredcites →https://doi.org/10.1016/j.geoderma.2006.07.004openalex-snapshot
Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy
inferredcites →https://doi.org/10.1016/j.geoderma.2019.06.016openalex-snapshot
Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra
inferredcites →https://doi.org/10.1021/ac011177uopenalex-snapshot
Wavelength Interval Selection in Multicomponent Spectral Analysis by Moving Window Partial Least-Squares Regression with Applications to Mid-Infrared and Near-Infrared Spectroscopic Data
inferredcites →https://doi.org/10.1021/cr50029a004openalex-snapshot
The Chemical Constituents Of Tobacco And Tobacco Smoke
inferredcites →https://doi.org/10.1029/jb095ib08p12653openalex-snapshot
High spectral resolution reflectance spectroscopy of minerals
inferredcites →https://doi.org/10.1109/tii.2021.3076513openalex-snapshot
Feedback Convolutional Network for Intelligent Data Fusion Based on Near-Infrared Collaborative IoT Technology
inferredcites →https://doi.org/10.1186/s13213-022-01699-zopenalex-snapshot
Effects of tobacco plant residue return on rhizosphere soil microbial community
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.