Adamo et al. (2026) Soil Moisture, Irrigation Actuator and Weather Dataset from a Multi-Sector Precision-Irrigation

Identification

• Journal: Mendeley Data
• Year: 2026
• Date: 2026-09-06
• Authors: Tommaso Adamo, Lucio Colizzi, Giovanni Dimauro, Emanuela Guerriero, Nunzia Lomonte
• DOI: 10.17632/c837v6p8ph

Research Groups

• University of Bari Aldo Moro (Bari, Apulia, Italy)
• University of Salento (Lecce, Apulia, Italy)

Short Summary

This dataset provides high-resolution soil moisture, weather, and irrigation actuator telemetry from a multi-crop precision-irrigation farm in Italy to facilitate the development of actionable irrigation management models.

Objective

• To provide a comprehensive, validated dataset that links soil-moisture sensor readings with actual irrigation actuator states and weather forcing across different crop types and substrates.

Study Configuration

• Spatial Scale: Local farm scale (Arnesano, Lecce, Italy) comprising five cultivated sectors: open-field tomato ($\times 2$), potted tomato, zucchini, and blueberry.
• Temporal Scale: One full growing season from February to September 2025.

Methodology and Data

• Models used: Python-based preprocessing pipeline (utilizing pandas, numpy, and requests) for data cleaning and feature engineering; ERA5 reanalysis for weather data.
• Data sources: IoT-instrumented sensors (soil moisture, pH, and electrical conductivity probes), valve actuator telemetry (state records), and ERA5 reanalysis weather data.

Main Results

• Raw Data: Collection of 486,894 sensor measurements and 3,055,944 valve state records.
• Processed Data: Reconstruction and agronomic validation of 789 irrigation and fertigation events.
• Time Series: Generation of five per-sector time series on a 10-minute grid (33,259 rows per sector) integrating sensor data, applied water volume, and weather forcing.
• Analysis Ready: Provision of engineered features and 24-hour-ahead soil-moisture targets.

Contributions

• Provides actual actuator telemetry alongside sensor readings, ensuring that applied water is a measured variable rather than an assumption.
• Includes raw, unmodified data (including dropouts and spikes) to allow for the benchmarking of different data-cleaning strategies.
• Enables cross-sector generalization studies by providing data from four different crop/substrate configurations sharing the same weather forcing.

Funding

• Not specified

Citation

@article{Adamo2026Soil,
  author = {Adamo, Tommaso and Colizzi, Lucio and Dimauro, Giovanni and Guerriero, Emanuela and Lomonte, Nunzia},
  title = {Soil Moisture, Irrigation Actuator and Weather Dataset from a Multi-Sector Precision-Irrigation},
  journal = {Mendeley Data},
  year = {2026},
  doi = {10.17632/c837v6p8ph},
  url = {https://doi.org/10.17632/c837v6p8ph}
}