Greenhouse IoT Water Optimization Logs
This dataset provides detailed logs of water usage, environmental conditions, and irrigation events from IoT sensors deployed in greenhouses. It enables AgTech professionals and sustainability analysts to monitor, optimize, and benchmark water consumption while maintaining crop health, supporting precision irrigation strategies and resource conservation.
Sample rows
preview · 8 of 120 rows · all 14 columns| log_idstring | sensor_statusstring | soil_moisture_percentfloat | irrigation_eventboolean | irrigation_methodstring | timestampdatetime | sensor_idstring | greenhouse_idstring | zone_idstring | crop_typestring | water_usage_litersfloat | ambient_temperature_celsiusfloat | humidity_percentfloat | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GH1-Zone-A-20240601 | active | 67.8 | true | drip | 2024-06-01T06:15:00Z | SENSOR-GH1-A | GH1 | Zone-A | Tomato | 35.2 | 17.3 | 58.4 | Morning irrigation cycle completed. |
| GH2-Zone-B-20240601 | active | 72.5 | true | sprinkler | 2024-06-01T08:30:00Z | SENSOR-GH2-B | GH2 | Zone-B | Cucumber | 29.8 | 20.5 | 65.7 | Sprinkler irrigation in Zone-B. |
| GH3-Zone-C-20240601 | active | 69.1 | true | drip | 2024-06-01T05:50:00Z | SENSOR-GH3-C | GH3 | Zone-C | Lettuce | 25.5 | 16.2 | 70.3 | Pre-dawn drip irrigation. |
| GH4-Zone-D-20240601 | active | 70.2 | true | sprinkler | 2024-06-01T11:20:00Z | SENSOR-GH4-D | GH4 | Zone-D | Spinach | 28.6 | 23.4 | 62.9 | Midday irrigation for Spinach. |
| GH5-Zone-E-20240601 | active | 74.3 | true | drip | 2024-06-01T18:05:00Z | SENSOR-GH5-E | GH5 | Zone-E | Carrot | 24.7 | 21.9 | 68 | Evening irrigation cycle. |
| GH1-Zone-F-20240601 | active | 68.7 | true | sprinkler | 2024-06-01T15:45:00Z | SENSOR-GH1-F | GH1 | Zone-F | Eggplant | 32.1 | 25.2 | 60.1 | Heavy irrigation due to high temperature. |
| GH2-Zone-G-20240601 | active | 71.6 | true | drip | 2024-06-01T20:10:00Z | SENSOR-GH2-G | GH2 | Zone-G | Pepper | 27.3 | 22.5 | 59.2 | Routine irrigation. |
| GH3-Zone-H-20240601 | active | 64.8 | true | sprinkler | 2024-06-01T22:00:00Z | SENSOR-GH3-H | GH3 | Zone-H | Strawberry | 20 | 19.1 | 72.8 | Cool evening, low water usage. |
| GH4-Zone-I-20240601 | active | 76.1 | true | drip | 2024-06-01T04:30:00Z | SENSOR-GH4-I | GH4 | Zone-I | Basil | 33.7 | 17.9 | 73.5 | Early morning, high humidity. |
| GH5-Zone-J-20240601 | active | 69.9 | true | drip | 2024-06-01T10:45:00Z | SENSOR-GH5-J | GH5 | Zone-J | Rose | 26.4 | 20.7 | 67.2 | Rose bed drip irrigation. |
| GH1-Zone-K-20240601 | active | 70.6 | true | sprinkler | 2024-06-01T09:20:00Z | SENSOR-GH1-K | GH1 | Zone-K | Mint | 30.9 | 18.5 | 71.4 | Mint zone evenly watered. |
| GH2-Zone-L-20240601 | active | 68.4 | true | drip | 2024-06-01T07:55:00Z | SENSOR-GH2-L | GH2 | Zone-L | Parsley | 23 | 19.8 | 74.6 | Parsley irrigation, optimal moisture. |
| GH3-Zone-M-20240601 | active | 66.2 | true | sprinkler | 2024-06-01T17:30:00Z | SENSOR-GH3-M | GH3 | Zone-M | Radish | 22.6 | 24.4 | 61.2 | Evening radish irrigation. |
| GH4-Zone-N-20240601 | active | 73.1 | true | drip | 2024-06-01T14:15:00Z | SENSOR-GH4-N | GH4 | Zone-N | Chili | 34.5 | 26 | 63.4 | Chili plants watered at peak temp. |
| GH5-Zone-O-20240601 | active | 65.8 | true | sprinkler | 2024-06-01T16:25:00Z | SENSOR-GH5-O | GH5 | Zone-O | Kale | 21.7 | 22.1 | 69 | Kale zone irrigation. |
| GH1-Zone-P-20240601 | active | 69.6 | true | drip | 2024-06-01T13:10:00Z | SENSOR-GH1-P | GH1 | Zone-P | Arugula | 28.8 | 23.7 | 66.9 | Arugula irrigation routine. |
| GH2-Zone-Q-20240601 | active | 99.9 | true | manual | 2024-06-01T12:00:00Z | SENSOR-GH2-Q | GH2 | Zone-Q | Watermelon | 999 | 30.3 | 48.2 | Outlier: watering-intensive crop. |
| GH3-Zone-X-20240601 | active | 0.1 | true | other | 2024-06-01T23:59:00Z | SENSOR-GH3-X | GH3 | Zone-X | Orchid | 0.1 | -20 | 0.1 | Boundary values for sensor test. |
| GH4-Zone-Y-20240601 | active | 20.8 | true | manual | 2024-06-01T02:35:00Z | SENSOR-GH4-Y | GH4 | Zone-Y | Lily | 5.1 | 14.5 | 30.3 | Manual irrigation due to system check. |
| GH5-Zone-Z-20240601 | active | 33.7 | true | other | 2024-06-01T19:45:00Z | SENSOR-GH5-Z | GH5 | Zone-Z | Rose | 10.2 | 29.2 | 35.5 | Experimental irrigation method. |
What the 120 rows show
from the 120-row sampleActive (sensor status) stands out: 83 of its 100 rows have irrigation_
- 69%irrigation_
event = true - 65.5median soil_
moisture_ percent - 6greenhouses
- 20crop types
- 24.6median water_
usage_ liters - 23.4median ambient_
temperature_ celsius
Active (sensor status)'s mean soil_
Median 65.5, from 0.0 to 100.0.
- string 8
- float 4
- datetime 1
- boolean 1
Columns
14 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
log_id | string | Unique identifier for each water usage log entryunique | GH1-Zone-A-20240601 |
sensor_id | string | Unique identifier for the IoT sensor reporting the data | SENSOR-GH1-A |
greenhouse_id | string | Identifier for the greenhouse where the sensor is located6 greenhouses | GH1 |
zone_id | string | Identifier for the specific zone or section within the greenhouseoptional | Zone-A |
crop_type | string | Type of crop present in the monitored zone | Tomato |
irrigation_method | string | Method of irrigation used (e.g., drip, sprinkler, manual)drip · sprinkler · manual · other · optional | drip |
sensor_status | string | Operational status of the sensor at the time of loggingactive · inactive · error · maintenance | active |
notes | string | Additional comments or observations relevant to the log entryoptional | Routine irrigation. |
| Numbers 4 columns | |||
water_usage_liters | float | Amount of water used in liters during the logged period0 or more | 35.2 |
soil_moisture_percent | float | Soil moisture level as a percentage at the time of logging0 to 100 | 67.8 |
ambient_temperature_celsius | float | Ambient temperature in degrees Celsius at the time of logging-20 to 60 | 17.3 |
humidity_percent | float | Ambient humidity as a percentage at the time of logging0 to 100 | 58.4 |
| Dates and times 1 column | |||
timestamp | datetime | Date and time when the sensor reading was recorded | 2024-06-01T06:15:00Z |
| True or false 1 column | |||
irrigation_event | boolean | Indicates whether an irrigation event occurred during this log entry | true |
Use it for
An agriculture dashboard
The irrigation_
event rate, soil_ moisture_ percent by sensor_ status and a breakdown of irrigation_ method. Excel, Power BI or Tableau. Why do 83 of 120 rows have irrigation_
event = true? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Logs120GH1-Zone-A-2024060167.8activeGH2-Zone-B-2024060172.5activeGH1-Zone-B-2024060224active
A software demo
Believable logs with timestamp, sensor_
id and greenhouse_ id to fill a screen in front of a buyer.
This dataset120 rows14 columns
Yours1,000 rows14 columns
blueprint · greenhouse-iot-water-optimization-logs
Behind this dataset
Same schema. As many rows as you need.
These 120 rows came out of a blueprint — 14 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Each row represents a unique timestamped sensor reading.
- Include soil moisture, humidity, and temperature alongside water usage.
- Flag anomaly readings based on historical deviation thresholds.
- Map each reading to a specific greenhouse zone and plant type.
- Calculate water savings versus baseline consumption per zone.
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- greenhouse-iot-water-optimization-logs