Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges
Abstract
:1. Introduction
2. Background
2.1. Study Area
2.2. Spectral Reflectance
2.3. Convolutional Neural Network
3. Materials and Methods
3.1. Soil Collection and Preparation
3.2. Soil Reflectance Measuring
3.3. Spectral Pre-Processing
3.3.1. Continuum Removal for Normalisation
3.3.2. Spectral Pre-Treatment
3.3.3. Convert Waveform to Spectrogram
3.4. Application of Convolutional Neural Network
4. Results
4.1. ICP-MS Analysis
4.2. Spectral Pre-Processing and CNN Results
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Sample | Sb (ppm) | As (ppm) | Pb (ppm) | Mn (ppm) | Zn (ppm) | Sample | Sb (ppm) | As (ppm) | Pb (ppm) | Mn (ppm) | Zn (ppm) |
---|---|---|---|---|---|---|---|---|---|---|---|
MDL | 0.02 | 0.2 | 0.02 | 1 | 0.2 | MDL | 0.02 | 0.2 | 0.02 | 1 | 0.2 |
RS001 | 13.38 | 25 | 33.49 | 13 | 12.7 | TP004 | 298.55 | 146.9 | 38.91 | 36 | 31.9 |
RS004 | 12.79 | 24.5 | 17.05 | 41 | 25.1 | TP007 | 80.07 | 67.9 | 21.95 | 29 | 42.9 |
RS007 | 10.71 | 18.7 | 22.23 | 68 | 24.5 | TP008 | 184.61 | 189.9 | 35.35 | 24 | 17.8 |
RS013 | 23.4 | 24.6 | 22.19 | 56 | 22.7 | TP009 | 31.39 | 46 | 18.38 | 29 | 34.6 |
RS015 | 14.55 | 16 | 28.89 | 60 | 49.1 | TP011 | 27.01 | 32.5 | 30.96 | 299 | 64.4 |
RS017 | 9.95 | 17.4 | 23.97 | 44 | 23.7 | TP013 | 251 | 167.4 | 24.41 | 17 | 30.8 |
RS019 | 9.5 | 21.3 | 21.08 | 22 | 14.7 | TP016 | 357.04 | 50.7 | 26.71 | 37 | 29.1 |
RS021 | 33.94 | 22.6 | 28.76 | 53 | 36.5 | TP017 | 1446 | 467.8 | 35.95 | 376 | 47.2 |
RS023 | 30.32 | 21 | 26.2 | 68 | 41.1 | TP027 | 52.6 | 70.3 | 25.98 | 23 | 19.7 |
RS027 | 16.87 | 17.9 | 24.53 | 51 | 31.1 | TP033 | 68.45 | 166.5 | 27.97 | 59 | 40.7 |
RS029 | 9.54 | 14.8 | 20.64 | 34 | 29.1 | TP035 | 715.49 | 78 | 49.92 | 142 | 68 |
RS031 | 25.73 | 20.3 | 20.93 | 57 | 29.8 | TP038 | 116.33 | 50 | 25.42 | 27 | 22.1 |
RS034 | 47.03 | 112.9 | 19.54 | 40 | 24.5 | TP040 | 258.4 | 74.5 | 62.37 | 63 | 26.9 |
RS037 | 575.47 | 1431.2 | 47.29 | 76 | 49.8 | TP044 | 259.18 | 65.7 | 42.6 | 844 | 146 |
RS040 | 59.42 | 28.6 | 33.97 | 261 | 50.9 | TP048 | 14.87 | 23.4 | 23.87 | 229 | 54.1 |
RS045 | 19.46 | 47.8 | 17.06 | 13 | 15 | TP049 | 207.65 | 30.9 | 25.6 | 27 | 19.7 |
RS049 | 162.61 | 111 | 24.82 | 35 | 28.3 | TP051 * | >4000 | 571.8 | 325.79 | 35 | 26.4 |
RS051 * | 2653 | 225 | 31.82 | 31 | 26.1 | TP055 | 50.4 | 27 | 25.04 | 51 | 31.3 |
RS052 | 2215 | 120.9 | 40.35 | 95 | 38.6 | TP064 | 103.82 | 34.8 | 33.38 | 106 | 65.3 |
RS055 | 50.68 | 21.2 | 12.64 | 15 | 23.6 | TP065 | 352.47 | 71 | 22.64 | 26 | 26 |
RS057 | 30.04 | 20.8 | 20.98 | 73 | 39.7 | TP068 | 217.94 | 47.9 | 20.33 | 37 | 31 |
RS061 | 82.11 | 30.8 | 25.48 | 24 | 25.9 | TP070 | 891.09 | 99.7 | 25.65 | 132 | 67 |
RS066 * | >4000 | 501.9 | 197.01 | 28 | 17.5 | TP072 | 6.3 | 16.4 | 29.52 | 57 | 33 |
RS067 * | 1103 | 129.6 | 28.5 | 18 | 21 | TP075 | 50.02 | 51.3 | 20.59 | 13 | 22.8 |
RS069 | 170.9 | 29.6 | 23.63 | 36 | 26.3 | TP077 | 258.22 | 27.4 | 22.34 | 26 | 29.4 |
RS073 | 342.44 | 84.8 | 34.84 | 27 | 36.1 | TP079 | 85.29 | 33.8 | 22.7 | 37 | 30.1 |
RS076 | 59.27 | 85.2 | 20.77 | 17 | 14 | TP082 | 31.88 | 22.8 | 14.67 | 51 | 32.7 |
RS078 * | >4000 | 680.5 | 171.89 | 143 | 51.8 | TP084 | 22.03 | 23.9 | 32.51 | 82 | 40.8 |
RS084 | 59.11 | 21.1 | 17.59 | 38 | 22.3 | TP087 | 619.71 | 92.3 | 103.39 | 78 | 38 |
RS088 | 89.74 | 34.8 | 20.79 | 30 | 22.8 | TP091 | 464.24 | 130.1 | 17.55 | 34 | 22.6 |
RS090 | 101.96 | 76.7 | 22.39 | 48 | 25 | TP093 | 38.45 | 19 | 9.26 | 767 | 128.8 |
RS094 | 79 | 91.5 | 24.93 | 29 | 26.5 | TP094 | 13.69 | 21.6 | 29.53 | 72 | 47.9 |
RS096 | 119.56 | 53.9 | 25.81 | 27 | 19.4 | TP099 * | >4000 | 1208.1 | 1040.14 | 143 | 110.8 |
RS100 | 127.62 | 43.8 | 18.54 | 35 | 28.3 | TP106 | 45.26 | 38.8 | 22.23 | 119 | 45.5 |
RS106 | 252.69 | 63.4 | 103.51 | 139 | 95.3 | TP109 * | 3786 | 240.8 | 190.75 | 387 | 55.4 |
RS108 | 61.45 | 72 | 30.65 | 28 | 28.9 | TP111 | 895 | 499 | 29.82 | 197 | 66.7 |
RS112 * | 1712 | 313.1 | 125.76 | 27 | 43.9 | TP115 | 93.15 | 37.9 | 18.36 | 271 | 76.2 |
RS115 | 118.07 | 147 | 25.04 | 49 | 33.4 | TP122 | 33.43 | 21.6 | 24.65 | 124 | 58.4 |
RS118 * | >4000 | 966.6 | 449.03 | 442 | 74.9 | TP125 | 44.84 | 19.4 | 19.12 | 44 | 22 |
RS126 | 131.8 | 48.8 | 23.47 | 22 | 25.5 | TP132 | 10.99 | 23.4 | 28.35 | 139 | 55.2 |
RS128 | 99.13 | 33.6 | 15.16 | 28 | 14.5 | TP133 | 38.25 | 25.5 | 26.92 | 61 | 33.9 |
RS129 | 565.34 | 81.6 | 359.75 | 88 | 26.7 | TP136 | 33.92 | 42 | 27.22 | 72 | 43.5 |
RS131 * | >4000 | 895.5 | 228.86 | 90 | 34.7 | TP138 | 48.68 | 17.7 | 20.54 | 41 | 42.9 |
RS134 | 151.98 | 91.1 | 37.56 | 78 | 30.1 | TP140 | 16.53 | 25.1 | 19.3 | 17 | 18.1 |
RS135 | 103.69 | 60.4 | 30.77 | 35 | 23.3 | TP147 | 26.18 | 30.8 | 43.43 | 116 | 78.4 |
RS143 * | >4000 | 671.9 | 221.68 | 163 | 41.1 | TP154 | 17.61 | 23.3 | 16.89 | 28 | 34.5 |
RS144 | 217.65 | 47.3 | 25.37 | 28 | 20.1 | TP156 | 13.44 | 27 | 15.95 | 33 | 27.4 |
RS148 | 48.3 | 36.2 | 16 | 11 | 11.1 | TP159 | 13.91 | 17.2 | 36.37 | 143 | 74.9 |
RS151 | 45.82 | 37.6 | 12.26 | 9 | 16.6 | TP167 | 11.39 | 20.3 | 12.68 | 15 | 20.6 |
RS156 * | 3500 | 361.7 | 50.46 | 46 | 28.6 | TP173 | 6.53 | 20.8 | 21.16 | 115 | 51.8 |
RS159 | 69.45 | 31 | 15.37 | 18 | 11.3 | TP175 | 16.54 | 17.9 | 15.22 | 16 | 18.2 |
RS162 | 68.91 | 28.6 | 26.37 | 55 | 34.2 | TP177 | 176.98 | 44.5 | 18.68 | 23 | 50.2 |
RS164 | 79.33 | 24.6 | 19.59 | 57 | 31.3 | TP179 | 10.66 | 50.7 | 15.55 | 17 | 23.3 |
RS169 | 72.12 | 20.7 | 15.76 | 21 | 24.8 | BLK | <0.02 | 0.3 | 0.04 | <1 | 0.3 |
Reference Material STD OREAS45H | 1.12 | 16.3 | 11.39 | 416 | 38.9 | ||||||
Reference Material STD OREAS501D | 2.56 | 13.4 | 24.43 | 379 | 81.7 | ||||||
Reference Material STD OREAS45H | 0.84 | 16.1 | 11.49 | 426 | 39.3 | ||||||
Reference Material STD OREAS501D | 2.43 | 11.4 | 24.3 | 371 | 83.2 | ||||||
Soil Pulp RS090 | 101.96 | 76.7 | 22.39 | 48 | 25 | ||||||
Soil Replicate RS090 | 96.97 | 76.5 | 22.25 | 46 | 24.9 | ||||||
Soil Pulp RS037 | 575.47 | 1431.2 | 47.29 | 76 | 49.8 | ||||||
Soil Replicate RS037 | 564.07 | 1426.4 | 48.43 | 77 | 50.8 |
References
- Li, T.; Archer, G.F.; Carapella, S.C., Jr. Antimony and Antimony Alloys. In Kirk-Othmer Encyclopedia of Chemical Technology; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2000; pp. 1–15. [Google Scholar]
- Butterman, W.; Hilliard, H. Mineral Commodity Profiles. Selenium; Rapport US Department of the Interior US Geological Survey: Online Only, 2004; pp. 1–20. [Google Scholar] [CrossRef]
- Wisniak, J. Nicolas Lémery. Rev. CENIC Cienc. Químicas 2005, 36, 123–130. [Google Scholar]
- European Commission; Directorate-General for Internal Market Industry Entrepreneurship and SMES; Grohol, M; Veeh, C. Study on the Critical Raw Materials for the EU 2023–Final Report; Publications Office of the European Union: Luxembourg, 2023; Available online: https://commission.europa.eu/about-european-commission/departments-and-executive-agencies/internal-market-industry-entrepreneurship-and-smes (accessed on 27 May 2024).
- Moolayadukkam, S.; Bopaiah, K.A.; Parakkandy, P.K.; Thomas, S. Antimony (Sb)-Based Anodes for Lithium–Ion Batteries: Recent Advances. Condens. Matter 2022, 7, 27. [Google Scholar] [CrossRef]
- He, J.; Wei, Y.; Zhai, T.; Li, H. Antimony-based materials as promising anodes for rechargeable lithium-ion and sodium-ion batteries. Mater. Chem. Front. 2018, 2, 437–455. [Google Scholar] [CrossRef]
- Kemper, T.; Sommer, S. Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ. Sci. Technol. 2002, 36, 2742–2747. [Google Scholar] [CrossRef] [PubMed]
- Nanni, M.R.; Demattê, J.A.M. Spectral Reflectance Methodology in Comparison to Traditional Soil Analysis. Soil Sci. Soc. Am. J. 2006, 70, 393–407. [Google Scholar] [CrossRef]
- Cheng, H.; Shen, R.; Chen, Y.; Wan, Q.; Shi, T.; Wang, J.; Wan, Y.; Hong, Y.; Li, X. Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy. Geoderma 2019, 336, 59–67. [Google Scholar] [CrossRef]
- Rodríguez-Pérez, J.R.; Marcelo, V.; Pereira-Obaya, D.; García-Fernández, M.; Sanz-Ablanedo, E. Estimating Soil Properties and Nutrients by Visible and Infrared Diffuse Reflectance Spectroscopy to Characterize Vineyards. Agronomy 2021, 11, 1895. [Google Scholar] [CrossRef]
- Pyo, J.; Hong, S.M.; Kwon, Y.S.; Kim, M.S.; Cho, K.H. Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil. Sci. Total Environ. 2020, 741, 140162. [Google Scholar] [CrossRef] [PubMed]
- Guo, B.; Guo, X.; Zhang, B.; Suo, L.; Bai, H.; Luo, P. Using a Two-Stage Scheme to Map Toxic Metal Distributions Based on GF-5 Satellite Hyperspectral Images at a Northern Chinese Opencast Coal Mine. Remote Sens. 2022, 14, 5804. [Google Scholar] [CrossRef]
- Yang, J.; Wang, X.; Wang, R.; Wang, H. Combination of convolutional neural networks and recurrent neural networks for predicting soil properties using Vis–NIR spectroscopy. Geoderma 2020, 380, 114616. [Google Scholar] [CrossRef]
- Mamalakis, A.; Barnes, E.A.; Ebert-Uphoff, I. Investigating the Fidelity of Explainable Artificial Intelligence Methods for Applications of Convolutional Neural Networks in Geoscience. Artif. Intell. Earth Syst. 2022, 1, e220012. [Google Scholar] [CrossRef]
- Wang, Y.; Abliz, A.; Ma, H.; Liu, L.; Kurban, A.; Halik, Ü.; Pietikäinen, M.; Wang, W. Hyperspectral Estimation of Soil Copper Concentration Based on Improved TabNet Model in the Eastern Junggar Coalfield. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–20. [Google Scholar] [CrossRef]
- Padarian, J.; Minasny, B.; McBratney, A.B. Using deep learning to predict soil properties from regional spectral data. Geoderma Reg. 2019, 16, e00198. [Google Scholar] [CrossRef]
- Ng, W.; Minasny, B.; Montazerolghaem, M.; Padarian, J.; Ferguson, R.; Bailey, S.; McBratney, A.B. Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra. Geoderma 2019, 352, 251–267. [Google Scholar] [CrossRef]
- Hunt, G.R. Spectral signatures of particulate minerals in the visible and near infrared. Geophysics 1977, 42, 501–513. [Google Scholar] [CrossRef]
- Clark, R.N. Spectroscopy of rocks and minerals and principles of spectroscopy: Chapter 1. In Remote Sensing for the Earth Sciences: Manual of Remote Sensing, 3rd ed.; Ryerson, R.A., Ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 1999. [Google Scholar]
- Li, Y.; Zhang, H.; Xue, X.; Jiang, Y.; Shen, Q. Deep learning for remote sensing image classification: A survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2018, 8, e1264. [Google Scholar] [CrossRef]
- Neiva, A.M.R.; Andráš, P.; Ramos, J.M.F. Antimony quartz and antimony–gold quartz veins from northern Portugal. Ore Geol. Rev. 2008, 34, 533–546. [Google Scholar] [CrossRef]
- Couto, H.; Roger, G.; Moëlo, Y.; Bril, H. Le district à antimoine-or Dúrico-Beirão (Portugal): Évolution paragénétique et géochimique; implications métallogéniques. Miner. Depos. 1990, 25, S69–S81. [Google Scholar] [CrossRef]
- Couto, M.H.M. As mineralizações de Sb-Au da região Dúrico-Beirã. Ph.D. Thesis, Universidade do Porto, Porto, Portugal, 1993. [Google Scholar]
- Lotze, F. Zur Gliederung der Varisziden der Iberischen Meseta. Geotekt. Forschg. 1945, 6, 78–92. [Google Scholar]
- Julivert, M.; Fontboté, J.; Ribeiro, A.; Conde, L. Mapa tectónico de la Península Ibérica, Canarias y Baleares, escala 1:1.000.000; IGME: Madrid, Spain, 1972. [Google Scholar]
- Carvalho, A. Minas de Antimónio e Ouro de Gondomar. Estudos, Notas e Trabalhos do Serviço de Fomento Mineiro (1969); Serviço de Fomento Mineiro: Lisboa, Portugal, 1969; Volume XIX, pp. 91–170. [Google Scholar]
- Frutuoso, R. Soil Sampling Campaign Report Ribeiro da Serra Mine. 2018; [Unpublished Report of Aureole project (10.54499/ERA-MIN/0005/2018)]. [Google Scholar]
- Schwartz, G.; Eshel, G.; Ben Dor, E. Reflectance spectroscopy as a tool for monitoring contaminated soils. In Soil Contamination; IntechOpen Limited: London, UK, 2011; Volume 6790. [Google Scholar]
- Dramsch, J.S. 70 years of machine learning in geoscience in review. Adv. Geophys. 2020, 61, 1–55. [Google Scholar]
- Ayodele, T.O. Machine learning overview. New Adv. Mach. Learn. 2010, 2, 9–18. [Google Scholar] [CrossRef]
- Cardoso-Fernandes, J.; Teodoro, A.C.; Lima, A.; Roda-Robles, E. Semi-automatization of support vector machines to map lithium (Li) bearing pegmatites. Remote Sens. 2020, 12, 2319. [Google Scholar] [CrossRef]
- Santos, D.; Cardoso-Fernandes, J.; Lima, A.; Müller, A.; Brönner, M.; Teodoro, A.C. Spectral analysis to improve inputs to random forest and other boosted ensemble tree-based algorithms for detecting NYF pegmatites in Tysfjord, Norway. Remote Sens. 2022, 14, 3532. [Google Scholar] [CrossRef]
- Karpatne, A.; Ebert-Uphoff, I.; Ravela, S.; Babaie, H.A.; Kumar, V. Machine learning for the geosciences: Challenges and opportunities. IEEE Trans. Knowl. Data Eng. 2018, 31, 1544–1554. [Google Scholar] [CrossRef]
- Howard, A.G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam, H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv 2017, arXiv:1704.04861. [Google Scholar]
- Kumar Lilhore, U.; Simaiya, S.; Sharma, Y.K.; Kaswan, K.S.; Rao, K.B.; Rao, V.M.; Baliyan, A.; Bijalwan, A.; Alroobaea, R. A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization. Sci. Rep. 2024, 14, 4299. [Google Scholar] [CrossRef] [PubMed]
- Chen, L.; Li, S.; Bai, Q.; Yang, J.; Jiang, S.; Miao, Y. Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sens. 2021, 13, 4712. [Google Scholar] [CrossRef]
- Alzubaidi, L.; Zhang, J.; Humaidi, A.J.; Al-Dujaili, A.; Duan, Y.; Al-Shamma, O.; Santamaría, J.; Fadhel, M.A.; Al-Amidie, M.; Farhan, L. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data 2021, 8, 53. [Google Scholar] [CrossRef] [PubMed]
- ISO/IEC 17025:2005; General Requirements for the Competence of Testing and Calibration Laboratories. 3rd ed. International Organisation for Standardisation: Geneva, Switzerland, 2017.
- Cardoso-Fernandes, J.; Silva, J.; Dias, F.; Lima, A.; Teodoro, A.C.; Barrès, O.; Cauzid, J.; Perrotta, M.; Roda-Robles, E.; Ribeiro, M.A. Tools for Remote Exploration: A Lithium (Li) Dedicated Spectral Library of the Fregeneda–Almendra Aplite–Pegmatite Field. Data 2021, 6, 33. [Google Scholar] [CrossRef]
- Pontual, S.; Merry, N.; Gamson, P. G-Mex Spectral Interpretation Field Manual; AusSpec International: Sydney, Australia, 1997. [Google Scholar]
- Zhou, W.; Yang, H.; Xie, L.; Li, H.; Huang, L.; Zhao, Y.; Yue, T. Hyperspectral inversion of soil heavy metals in Three-River Source Region based on random forest model. Catena 2021, 202, 105222. [Google Scholar] [CrossRef]
- Baíllo, A.; Chacón, J.E. Statistical outline of animal home ranges: An application of set estimation. In Handbook of Statistics; Elsevier: Amsterdam, The Netherlands, 2021; Volume 44, pp. 3–37. [Google Scholar]
- Carvalho, M. Machine Learning Applied to Sb Mineralizations in Northern Portugal. Master’s Thesis, Faculdade de Ciências da Universidade do Porto, Porto, Portugal, 2023. [Google Scholar]
- Rybczak, M.; Kozakiewicz, K. Deep Machine Learning of MobileNet, Efficient, and Inception Models. Algorithms 2024, 17, 96. [Google Scholar] [CrossRef]
- Wang, H.; Qiu, S.; Ye, H.; Liao, X. A Plant Disease Classification Algorithm Based on Attention MobileNet V2. Algorithms 2023, 16, 442. [Google Scholar] [CrossRef]
- Dokl, M.; Van Fan, Y.; Vujanović, A.; Pintarič, Z.N.; Aviso, K.B.; Tan, R.R.; Pahor, B.; Kravanja, Z.; Čuček, L. A waste separation system based on sensor technology and deep learning: A simple approach applied to a case study of plastic packaging waste. J. Clean. Prod. 2024, 450, 141762. [Google Scholar] [CrossRef]
- Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; Chen, L.-C. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 4510–4520. [Google Scholar] [CrossRef]
- Hodson, T.O. Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not. Geosci. Model Dev. 2022, 15, 5481–5487. [Google Scholar] [CrossRef]
- Chicco, D.; Warrens, M.J.; Jurman, G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput. Sci. 2021, 7, e623. [Google Scholar] [CrossRef]
- Géron, A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow; O’Reilly Media, Inc.: Sebastopol, CA, USA, 2022; p. 568. [Google Scholar]
- Oliveira, D.L.B.; Pereira, L.H.d.S.; Schneider, M.P.; Silva, Y.J.A.B.; Nascimento, C.W.A.; van Straaten, P.; Silva, Y.J.A.B.; Gomes, A.d.A.; Veras, G. Bio-inspired algorithm for variable selection in i-PLSR to determine physical properties, thorium and rare earth elements in soils from Brazilian semiarid region. Microchem. J. 2021, 160, 105640. [Google Scholar] [CrossRef]
- Kopačková-Strnadová, V.; Rapprich, V.; McLemore, V.; Pour, O.; Magna, T. Quantitative estimation of rare earth element abundances in compositionally distinct carbonatites: Implications for proximal remote-sensing prospection of critical elements. Int. J. Appl. Earth Obs. Geoinf. 2021, 103, 102423. [Google Scholar] [CrossRef]
- Wu, Y.; Chen, J.; Ji, J.; Gong, P.; Liao, Q.; Tian, Q.; Ma, H. A Mechanism Study of Reflectance Spectroscopy for Investigating Heavy Metals in Soils. Soil Sci. Soc. Am. J. 2007, 71, 918–926. [Google Scholar] [CrossRef]
- Xuemei, L.; Jianshe, L. Using short wave visible–near infrared reflectance spectroscopy to predict soil properties and content. Spectrosc. Lett. 2014, 47, 729–739. [Google Scholar] [CrossRef]
- Gomez, C.; Viscarra Rossel, R.A.; McBratney, A.B. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma 2008, 146, 403–411. [Google Scholar] [CrossRef]
- Viscarra Rossel, R.A.; Walvoort, D.J.J.; McBratney, A.B.; Janik, L.J.; Skjemstad, J.O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 2006, 131, 59–75. [Google Scholar] [CrossRef]
- Bajorski, P.; Kazmierowski, C.; Cierniewski, J.; Piekarczyk, J.; Kusnierek, K.; Królewicz, S.; Terelak, H.; Stuczynski, T.; Maliszewska-Kordybach, B. Use of clustering with partial least squares regression for predictions based on hyperspectral data. In Proceedings of the 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland, 24–27 June 2014. [Google Scholar] [CrossRef]
- Dunn, B.; Batten, G.; Beecher, H.G.; Ciavarella, S. The potential of near-infrared reflectance spectroscopy for soil analysis—A case study from the Riverine Plain of south-eastern Australia. Aust. J. Exp. Agric. 2002, 42, 607–614. [Google Scholar] [CrossRef]
- Wang, C.; Zhang, T.; Pan, X. Potential of visible and near-infrared reflectance spectroscopy for the determination of rare earth elements in soil. Geoderma 2017, 306, 120–126. [Google Scholar] [CrossRef]
- Jung, A.; Vohland, M. Snapshot Hyperspectral Imaging for Soil Diagnostics–Results of a Case Study in the Spectral Laboratory. Photogramm.-Fernerkund.-Geoinf. 2014, 511–522. [Google Scholar] [CrossRef]
- Henseler, J. Partial least squares path modeling: Quo vadis? Qual. Quant. 2018, 52, 1–8. [Google Scholar] [CrossRef]
- Tsakiridis, N.L.; Keramaris, K.D.; Theocharis, J.B.; Zalidis, G.C. Simultaneous prediction of soil properties from VNIR-SWIR spectra using a localized multi-channel 1-D convolutional neural network. Geoderma 2020, 367, 114208. [Google Scholar] [CrossRef]
Element | Sb | As | Pb | Mn | Zn |
---|---|---|---|---|---|
Sb | 1 | - | - | - | - |
As | 0.9 | 1 | - | - | - |
Pb | 0.63 | 0.73 | 1 | - | - |
Mn | 0.15 | 0.28 | 0.42 | 1 | - |
Zn | 0.25 | 0.41 | 0.72 | 0.66 | 1 |
Sb (ppm) | As (ppm) | Pb (ppm) | Mn (ppm) | Zn (ppm) | |
---|---|---|---|---|---|
Mean | 132 | 69 | 30 | 75 | 36 |
Std. Deviation | 184 | 156 | 38 | 123 | 23 |
Minimum | 6.3 | 15 | 9 | 9 | 11 |
Maximum | 895 | 1431 | 360 | 844 | 146 |
Q1 | 26 | 23 | 20 | 27 | 23 |
Q2 | 59 | 34 | 24 | 39 | 30 |
Q3 | 155 | 69 | 29 | 72 | 42 |
Element | R2 | RMSE Train | RMSE Validation | Training Epochs |
---|---|---|---|---|
Sb | 0.7 | 0.0014 | 173 | 1000 |
As | 0.96 | 0.01 | 46 | 1000 |
Pb | 0.83 | 0.04 | 20 | 750 |
Mn | 0.93 | 0.0006 | 41 | 600 |
Zn | 0.78 | 0.0002 | 18 | 1000 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Carvalho, M.; Cardoso-Fernandes, J.; Lima, A.; Teodoro, A.C. Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges. Remote Sens. 2024, 16, 1964. https://doi.org/10.3390/rs16111964
Carvalho M, Cardoso-Fernandes J, Lima A, Teodoro AC. Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges. Remote Sensing. 2024; 16(11):1964. https://doi.org/10.3390/rs16111964
Chicago/Turabian StyleCarvalho, Morgana, Joana Cardoso-Fernandes, Alexandre Lima, and Ana C. Teodoro. 2024. "Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges" Remote Sensing 16, no. 11: 1964. https://doi.org/10.3390/rs16111964