Using deep neural networks for evaluation of soil quality based on VIS–NIR spectroscopy-Reference-Cited by-同舟云学术

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Using deep neural networks for evaluation of soil quality based on VIS–NIR spectroscopy

Published:2023-11-27 Issue:1 Volume:17 Page:271-281
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Safaie Mehdi,Hosseinpour-Zarnaq Mohammad,Omid Mahmoud,Sarmadian Fereydoon,Ghasemi-Mobtaker Hassan

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s12145-023-01168-4.pdf

Reference37 articles.

1. Bai Z, Chen S, Hong Y et al (2023) Estimation of soil inorganic carbon with visible near-infrared spectroscopy coupling of variable selection and deep learning in arid region of China. Geoderma 437:116589. https://doi.org/10.1016/j.geoderma.2023.116589

2. Baumgardner MF, Silva LF, Biehl LL, Stonery ER (1985) Reflectance properties of soils. Adv Agron 38:1–44. https://doi.org/10.1016/S0065-2113(08)60672-0

3. Castaldi F, Palombo A, Santini F et al (2016) Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sens Environ 179:54–65. https://doi.org/10.1016/j.rse.2016.03.025

4. Chang C, Laird D, Mausbach MJ, Hurburgh CR (2001) Analyses of soil properties. Soil Sci Soc Am J 65:480–490. https://doi.org/10.2136/sssaj2001.652480x

5. Demattê JAM, Ramirez-Lopez L, Marques KPP, Rodella AA (2017) Chemometric soil analysis on the determination of specific bands for the detection of magnesium and potassium by spectroscopy. Geoderma 288:8–22. https://doi.org/10.1016/j.geoderma.2016.11.013

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1. Multi-Scale Spatial Attention-Based Multi-Channel 2D Convolutional Network for Soil Property Prediction;Sensors;2024-07-21

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