TY - GEN
T1 - RDNet
T2 - 2021 IEEE International Conference on Big Data, Big Data 2021
AU - Pham, Vung
AU - Weindorf, David C.
AU - Dang, Tommy
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - deep learning
KW - machine learning
KW - soil Vis-NIR spectra
KW - soil property predictions
UR - http://www.scopus.com/inward/record.url?scp=85125330186&partnerID=8YFLogxK
U2 - 10.1109/BigData52589.2021.9671527
DO - 10.1109/BigData52589.2021.9671527
M3 - Conference article
AN - SCOPUS:85125330186
T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
SP - 3436
EP - 3445
BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
A2 - Chen, Yixin
A2 - Ludwig, Heiko
A2 - Tu, Yicheng
A2 - Fayyad, Usama
A2 - Zhu, Xingquan
A2 - Hu, Xiaohua Tony
A2 - Byna, Suren
A2 - Liu, Xiong
A2 - Zhang, Jianping
A2 - Pan, Shirui
A2 - Papalexakis, Vagelis
A2 - Wang, Jianwu
A2 - Cuzzocrea, Alfredo
A2 - Ordonez, Carlos
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2021 through 18 December 2021
ER -