Soil moisture and temperature data in agricultural soil

Published: 27 July 2022| Version 1 | DOI: 10.17632/fpbfmc9vnm.1
ODS Research Group Universidad Loyola


This data set contains measurements of soil moisture and temperature collected at a sugar cane crop field in Paraguay, 2022. The details of the experiments have been sent as a possible publication to the journal Computers and Electronics in Agriculture, under the title Data-driven Spatio-temporal estimation of soil moisture and temperature based on Lipschitz interpolation, by J.M. Manzano, L. Orihuela, E. Pacheco and M. Pereira. Data collected from 13:00:00 (UTC) on March 09th 2022 until 03:00:00 March 31st 2022 is presented. In the database, the time coordinate is expressed in hours, relative to the first measurement. It contains 15776 data points. The data is structured in two CSV files, defined as follows - First table: measurement data, with the following fields: - Timestamp 't', with format DD-MMM-YYYY HH:mm:ss. - Node identifier 'Node_ID', which is an integer from 1 to 12. - Temperature 'z_T', measured in Celsius degrees. - Soil moisture 'z_M', measured as a percentage. - Second table: context data, with the following fields: - Node identifier 'Node_ID'. - Coordinate x measured in meters. - Coordinate y measured in meters. According to the manufacturer's specifications, the noise in the measurements is less than 2%. The dataset is open to be used by any researcher, in studies related to machine learning, agricultural soil monitoring or estimation, and Spatio-temporal dynamics or processes governed by partial differential equations, among others. Citing the aforementioned work is appreciated.



Universidad Loyola


Machine Learning, Data Acquisition, Agricultural Soil, Differential Equation Estimate, Spatio-Temporal Model