Soil profile analysis using interactive visualizations, machine learning, and deep learning

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dc.creatorPham, Vung
dc.creatorWeindorf, David C.
dc.creatorDang, Tommy (TTU)
dc.date.accessioned2022-06-02T17:43:13Z
dc.date.available2022-06-02T17:43:13Z
dc.date.issued2021
dc.description© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.description.abstractSoil is an essential element of life, and soil properties are crucial in analyzing soil health. Recent developments of proximal sensor technologies, such as portable X-ray fluorescence (pXRF) spectroscopy or visible and near-infrared (Vis–NIR) spectroscopy, offer rapid and non-destructive alternatives for quantifying data from soil profiles. While the data collection time using these technologies decreases significantly, the subsequent analysis remains time-consuming, and current analysis solutions only provide basic visualizations. Furthermore, the use of collected data from proximal sensors to predict high-level soil properties has garnered worldwide attention in the past decade, owing to its convenience. Therefore, this paper discusses the objectives for software solutions in this area, consolidated from interviewing 102 stakeholders. Following these requirements, data visualizers work closely with soil scientists to propose a set of interactive visualizations for analyzing soil profiles using pXRF data. These interactive visualizations receive positive feedback from the domain experts. This project also explores various machine learning and deep learning approaches to predict soil properties from spectral data. This work then proposes a deep learning model called RDNet that achieves state-of-the-art results in predicting pHH2O and pHKCl from Vis–NIR spectra acquired from a set of globally distributed soil samples.en_US
dc.identifier.citationPham, V., Weindorf, D. C., & Dang, T. (2021). Soil profile analysis using interactive visualizations, machine learning, and Deep Learning. Computers and Electronics in Agriculture, 191, 106539. https://doi.org/10.1016/j.compag.2021.106539en_US
dc.identifier.urihttps://doi.org/10.1016/j.compag.2021.106539
dc.identifier.urihttps://hdl.handle.net/2346/89412
dc.language.isoengen_US
dc.subjectChemical Measurement Data Analysisen_US
dc.subjectIntelligent Visual Analyticsen_US
dc.subjectpXRF Data Visualizationen_US
dc.subjectSoil Property Predictionsen_US
dc.subjectVis-NIR Spectraen_US
dc.subjectMachine Learning and Deep Learningen_US
dc.titleSoil profile analysis using interactive visualizations, machine learning, and deep learningen_US
dc.typeArticleen_US

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