IoT-based Dataset of a Tomato Cultivation Under Different Irrigation Regimes
Bibliographic record
Abstract
This experimental dataset contains IoT-based data collected through Long Range Wide Area Network (LoRaWAN) commercial devices in a tomato (Solanum lycopersicum L. cv. HEINZ 1301) cultivation located at the “Azienda Sperimentale Stuard” in Parma, Italy (lat: 44.80787, lon: 10.27467), and data generated by the Agriware platform. More in detail, the IoT-based irrigation system located in the “Azienda Sperimentale Stuard” to manage the watering of the tomato crop has been organized with 3 experimental lines associated with 3 different watering regimes: (i) Line #1 was irrigated with a water quantity equal to the recommendation of the Italian “Irriframe” platform (https://www.irriframe.it/); (ii) Line #2 was irrigated with a water quantity equal to the 60% of Line #1; (iii) Line #3 was irrigated with a water quantity equal to the 30% of Line #1. The dataset is composed of 4 CSV files. Three of these files contain the following information, generated by IoT devices (environmental sensor, water meters, and soil sensors) and sampled every 10 minutes: timestamp; device identifier; air moisture and temperature; carbon dioxide (CO2) level; barometric pressure; battery percentage; tomato line identifier; water volume; soil electrical conductivity, moisture, and temperature. The remaining CSV file contains daily values of agronomic indicators, calculated through the Agriware platform mainly using the average daily air temperature values, such as: the daily values of Growing Degree Days (GDD) and Heat Units (namely: standard day degree, daily mean temperature, daily maximum temperature above T_base, daily maximum temperature, daily maximum temperature above T_base with reduction of T_cutoff, Ontario units.) A complete description of the CSV file can be found in the README.txt file. The dataset has been generated in the context of the following two projects: (i) Agritech - “National Research Centre for Agricultural Technologies,” project code CN00000022, funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender no. 3138 of 16/12/2021 of Italian Ministry of University and Research funded by the European Union – NextGenerationEU, Concession Decree no. 1032 of 17/06/2022 adopted by the Italian Ministry of University and Research; and (ii) SMALLDERS - “Smart Models for Agrifood Local vaLue chain based on Digital technologies for Enabling covid-19 Resilience and Sustainability,” co-funded by the PRIMA Program - Section 2 Call multi-topics 2021, through the following National Authorities: Ministry of Universities and Research (MUR, Italy), State Research Agency (AEI, Spain), Agence Nationale de la Recherche (ANR, France), Ministry of Higher Education and Scientific Research (Tunisia). The dataset reflects only the authors’ views; the European Commission is not responsible for any use that may be made of the information it contains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".