Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra

Abadi Bellon-Maurel, 2010, Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy, Trac-Trends in Analytical Chemistry, 29, 1073, 10.1016/j.trac.2010.05.006 Borras, 2015, Data fusion methodologies for food and beverage authentication and quality assessment - a review, Anal. Chim. Acta, 891, 1, 10.1016/j.aca.2015.04.042 Chang, 2001, Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties, Soil Sci. Soc. Am. J., 65, 480, 10.2136/sssaj2001.652480x Chollet Clairotte, 2016, National calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy, Geoderma, 276, 41, 10.1016/j.geoderma.2016.04.021 Dangal, 2019, Accurate and precise prediction of soil properties from a large mid-infrared spectral library, Soil Systems, 3, 11, 10.3390/soilsystems3010011 Daniel, 2003, Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand), Aust. J. Soil Res., 41, 47, 10.1071/SR02027 Devos, 2009, Support vector machines (SVM) in near infrared (NIR) spectroscopy: focus on parameters optimization and model interpretation, Chemom. Intell. Lab. Syst., 96, 27, 10.1016/j.chemolab.2008.11.005 Dollar, 2009, Integral Channel Features Forrester, 2013, Total petroleum hydrocarbon concentration prediction in soils using diffuse reflectance infrared spectroscopy, Soil Sci. Soc. Am. J., 77, 450, 10.2136/sssaj2012.0201 Griffin, 1984, Signal estimation from modified short-time Fourier transform, IEEE Trans. Acoust. Speech Signal Process., 32, 236, 10.1109/TASSP.1984.1164317 He, 2015, Delving deep into rectifiers: surpassing human-level performance on ImageNet classification, 2015 Ieee International Conference on Computer Vision (Iccv), 1026, 10.1109/ICCV.2015.123 Hobley, 2014, Vertical distribution of charcoal in a sandy soil: evidence from DRIFT spectra and field emission scanning electron microscopy, Eur. J. Soil Sci., 65, 751, 10.1111/ejss.12171 Ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift Islam, 2003, Simultaneous estimation of several soil properties by ultra-violet, visible, and near-infrared reflectance spectroscopy, Aust. J. Soil Res., 41, 1101, 10.1071/SR02137 Jaillais, 2005, Outer-product analysis (OPA) using PCA to study the influence of temperature on NIR spectra of water, Vib. Spectrosc., 39, 50, 10.1016/j.vibspec.2004.10.008 Janik, 1998, Can mid infrared diffuse reflectance analysis replace soil extractions?, Aust. J. Exp. Agric., 38, 681, 10.1071/EA97144 Johnston, 2017, Chapter 9 - infrared studies of clay mineral-water interactions, vol. 8, 288, 10.1016/B978-0-08-100355-8.00009-6 Kingma, 2014 Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks Kuhn Lee, 2013, Random forest as a potential multivariate method for near-infrared (NIR) spectroscopic analysis of complex mixture samples: gasoline and naphtha, Microchem. J., 110, 739, 10.1016/j.microc.2013.08.007 Matsugu, 2003, Subject independent facial expression recognition with robust face detection using a convolutional neural network, Neural Netw., 16, 555, 10.1016/S0893-6080(03)00115-1 McCarty, 2002, Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement, Soil Sci. Soc. Am. J., 66, 640, 10.2136/sssaj2002.6400a Mevik Minasny, 2008, Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy, Chemom. Intell. Lab. Syst., 94, 72, 10.1016/j.chemolab.2008.06.003 Niu, 2016, Ordinal regression with multiple output cnn for age estimation Nocita, 2015, Soil spectroscopy: an alternative to wet chemistry for soil monitoring, Adv. Agron., 132, 139, 10.1016/bs.agron.2015.02.002 O'Rourke, 2016, Synergistic use of Vis-NIR, MIR, and XRF spectroscopy for the determination of soil geochemistry, Soil Sci. Soc. Am. J., 80, 888, 10.2136/sssaj2015.10.0361 Padarian, 2019, Using deep learning to predict soil properties from regional spectral data, Geoderma Regional, 16, 10.1016/j.geodrs.2018.e00198 Python Software Foundation Quinlan, 1993 R Core Team, 2019 Ramsundar, 2015 Ribeiro, 2017, Multi-channel convolutional neural network ensemble for pedestrian detection, Pattern Recognition and Image Analysis (Ibpria 2017), 10255, 122, 10.1007/978-3-319-58838-4_14 Rinnan, 2009, Review of the most common pre-processing techniques for near-infrared spectra, Trac-Trends in Analytical Chemistry, 28, 1201, 10.1016/j.trac.2009.07.007 Ruder, 2017 Sarathjith, 2016, Variable indicators for optimum wavelength selection in diffuse reflectance spectroscopy of soils, Geoderma, 267, 1, 10.1016/j.geoderma.2015.12.031 Savitzky, 1964, Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem., 36, 1627, 10.1021/ac60214a047 Sermanet, 2013 Shepherd, 2002, Development of reflectance spectral libraries for characterization of soil properties, Soil Sci. Soc. Am. J., 66, 988, 10.2136/sssaj2002.9880 Shibusawa, 2001, Soil mapping using the real-time soil spectrophotometer, 497 Sila, 2016, Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties, Chemom. Intell. Lab. Syst., 153, 92, 10.1016/j.chemolab.2016.02.013 Soil Survey Staff, 2014, Kellogg soil survey laboratory methods manual Soriano-Disla, 2014, The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties, Appl. Spectrosc. Rev., 49, 139, 10.1080/05704928.2013.811081 Stenberg, 2010, Chapter five - visible and near infrared spectroscopy in soil science, vol. 107, 163, 10.1016/S0065-2113(10)07005-7 Terhoeven-Urselmans, 2010, Prediction of soil fertility properties from a globally distributed soil mid-infrared spectral library, Soil Sci. Soc. Am. J., 74, 1792, 10.2136/sssaj2009.0218 Terra, 2015, Spectral libraries for quantitative analyses of tropical Brazilian soils: comparing Vis-NIR and mid-IR reflectance data, Geoderma, 255, 81, 10.1016/j.geoderma.2015.04.017 Terra, 2019, Spectral fusion by outer product analysis (OPA) to improve predictions of soil organic C, Geoderma, 335, 35, 10.1016/j.geoderma.2018.08.005 Tinti, 2015, Recent applications of vibrational mid-infrared (IR) spectroscopy for studying soil components: a review, J. Cent. Eur. Agric., 16, 1, 10.5513/JCEA01/16.1.1535 Vesela, 2007, Infrared spectroscopy and outer product analysis for quantification of fat, nitrogen, and moisture of cocoa powder, Anal. Chim. Acta, 601, 77, 10.1016/j.aca.2007.08.039 Viscarra Rossel, 2016, A global spectral library to characterize the world's soil, Earth Sci. Rev., 155, 198, 10.1016/j.earscirev.2016.01.012 Wang, 2015, Synthesized use of VisNIR DRS and PXRF for soil characterization: Total carbon and total nitrogen, Geoderma, 243-244, 157, 10.1016/j.geoderma.2014.12.011 Xu, 2017, Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships, Journal of Chemical Information and Modeling, 57, 2490, 10.1021/acs.jcim.7b00087 Xuemei, 2010, NIR sensitive wavelength selection based on different methods Zhang Zou, 2010, Variables selection methods in near-infrared spectroscopy, Anal. Chim. Acta, 667, 14