RDNet: Deep Learning Model for Predicting pH H20 and pHKClfrom Soil Vis-NIR Spectra

Vung Pham, David C. Weindorf, Tommy Dang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Soil properties are vital to profiling and utilizing soil resources. Conventional approaches to measurements of soil properties often involve costly, environmental-unfriendly, and time-consuming laboratory procedures. Conversely, machine learning (ML) and deep learning (DL) are gaining traction in giving rapid, non-destructive, and cost-saving alternatives to predictions of soil properties. These ML/DL models are convenient and fast because they utilize spectral data, such as visible and near-infrared (Vis-NIR) spectra, that can be easily collected using proximal sensors for their training and prediction purposes. However, existing ML/DL approaches to this problem pose several limitations, such as having small sample sizes, needing to divide the sample data into local areas to increase accuracy, and having relatively low accuracy. Therefore, this work experiments various ML/DL methods that leverage Vis-NIR spectra collected from a rather large number of soil samples distributed all over the world to predict pH H2O and pHKCl. We then propose a DL method, called RDNet, that outperforms the other existing approaches. We also utilize visualizations to verify if the proposed model learns legitimate information from the training data.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3436-3445
Number of pages10
ISBN (Electronic)9781665439022
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
Duration: Dec 15 2021Dec 18 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference2021 IEEE International Conference on Big Data, Big Data 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/15/2112/18/21

Keywords

  • deep learning
  • machine learning
  • soil Vis-NIR spectra
  • soil property predictions

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