RT Journal Article
T1 Data-driven spatio-temporal estimation of soil moisture and temperature based on Lipschitz interpolation
A1 Manzano, José María
A1 Orihuela Espina, Diego Luis
A1 Pacheco, Erid Eulogio
A1 Pereira, Mario
AB This study estimates agricultural soil variables using a non-parametric machine learning technique based on Lipschitz interpolation. This method is adapted for the first time to learn spatio-temporal dynamics, accounting for two-dimensional spatial and one temporal coordinate inputs separately. The estimator is validated on real agricultural data, addressing challenges like measurement noise and quantization. The experimental setup, including an edge layer with measurement devices and a cloud layer for data storage and processing, is detailed. Despite its simplicity, the method presents a compelling alternative to Gaussian processes and neural networks.
PB Elsevier
SN 0019-0578
SN 1879-2022 (electrónico)
YR 2025
FD 2025-01-01
LK https://hdl.handle.net/10272/27104
UL https://hdl.handle.net/10272/27104
LA eng
NO Manzano, J. M., Orihuela, L., Pacheco, E., & Pereira, M. (2025). Data-driven spatio-temporal estimation of soil moisture and temperature based on Lipschitz interpolation. ISA Transactions, 156, 535–550. https://doi.org/10.1016/j.isatra.2024.11.018
NO This work has been partially supported by Agencia Española de Cooperación Internacional al Desarrollo, Spain (Grant 2022/ACDE/000116), Agencia Estatal de Investigación AEI, Spain (Grants PID2020-117800GB-I00 and RYC2021-032919-I) and by Junta de Andalucía, Spain (Grant PY20-RE-017-LOYOLA). The authors would like to thank Fundación Ayesa for their work involved in the development of the cloud technology, and the complete ODS Research group for their effort in designing and testing the nodes.
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026