MANZANO CRESPO, JOSÉ MARÍA, ORIHUELA ESPINA, DIEGO LUIS, PACHECO VIANA, ERID EULOG, PEREIRA MARTÍN, MARIO
No
ISA Trans.
Article
Científica
01/01/2025
001406833300001
2-s2.0-85210718306
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.
Spatio-temporal estimation; Lipschitz interpolation; Non-parametric learning; Agriculture soil monitoring; Experimental validation