IoT Precision Irrigation Sensor Data
This dataset provides granular, time-stamped readings from distributed IoT soil sensors, capturing soil moisture, temperature, electrical conductivity, irrigation events, and environmental conditions across multiple farms and fields. It enables agritech startups, sustainability officers, and farm managers to optimize irrigation schedules, detect anomalies, and improve crop yield forecasts using advanced analytics and AI models.
Sample rows
preview · 8 of 121 rows · all 16 columns| sensor_idstring | soil_moisture_percentfloat | irrigation_eventboolean | farm_idstring | field_idstring | timestampdatetime | soil_temperature_celsiusfloat | soil_ec_ds_per_mfloat | irrigation_volume_litersfloat | rainfall_mmfloat | battery_voltagefloat | signal_strength_dbmfloat | latitudefloat | longitudefloat | crop_typestring | anomaly_detectedboolean |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SN_001A | 32.7 | false | FARM01 | FIELD_A1 | 2024-06-10T07:24:03Z | 19.6 | 1.4 | blank | 1.7 | 3.97 | -87 | 36.8772 | -121.7627 | Wheat | false |
| sensor-004b | 83.1 | true | FARM02 | FIELD_B2 | 2024-06-10T15:11:40Z | 22.5 | 3.8 | 212 | 7.2 | 4.03 | -78 | 35.6449 | -120.635 | Rice | false |
| SN_002B | 2.1 | false | FARM01 | FIELD_A2 | 2024-06-11T09:02:19Z | 16.4 | 0.8 | blank | 0 | 3.65 | -95 | 36.8763 | -121.762 | Corn | true |
| sn_jkl99 | 41.3 | false | FARM03 | FIELD_C1 | 2024-06-11T16:37:52Z | 25.2 | 1.6 | blank | 3.1 | 3.84 | -62 | 37.2735 | -120.0428 | Soybean | false |
| sensor-xx12 | 97 | false | FARM04 | FIELD_D1 | 2024-06-11T20:18:03Z | 55.6 | 14.2 | blank | 78.6 | 1.9 | -119 | 34.9821 | -119.5632 | Watermelon | true |
| SENSOR_005A | 27.5 | false | FARM05 | FIELD_E1 | 2024-06-12T05:53:41Z | 12.3 | 0.9 | blank | 2.5 | 3.77 | -85 | 33.8764 | -118.3215 | Potato | false |
| SN_006C | 45.7 | false | FARM06 | FIELD_F1 | 2024-06-12T14:19:29Z | 26.9 | 2.1 | blank | 5.4 | 3.88 | -67 | 32.8327 | -117.2712 | Tomato | false |
| sensor-021x | 38.2 | false | FARM07 | FIELD_G1 | 2024-06-12T21:48:06Z | 17.9 | blank | blank | 0 | 4.11 | -73 | 34.0412 | -118.247 | Cassava | false |
| SENSOR_007B | 56.7 | true | FARM08 | FIELD_H1 | 2024-06-13T10:29:18Z | 39.2 | 5.6 | 130.5 | 13.9 | 4.14 | -70 | 38.5766 | -121.4944 | Lettuce | false |
| sn_abc88 | 6.5 | false | FARM09 | FIELD_I1 | 2024-06-13T17:55:27Z | -2.7 | 18.9 | blank | 60.4 | 0.8 | -113 | 40.7128 | -74.006 | Carrot | true |
| sensor-101a | 33.5 | false | FARM10 | FIELD_J1 | 2024-06-14T08:15:51Z | 20.1 | 1.7 | blank | 1.9 | 3.75 | -83 | 39.9526 | -75.1652 | Corn | false |
| SN_008C | 78.2 | true | FARM11 | FIELD_K1 | 2024-06-14T15:58:37Z | 31.3 | 4.2 | 305.8 | 19.1 | 3.98 | -58 | 41.8781 | -87.6298 | Avocado | false |
| sensor-102b | 95.3 | false | FARM12 | FIELD_L1 | 2024-06-14T22:42:19Z | 50.7 | 24.7 | blank | 89.3 | 0.5 | -120 | 42.3601 | -71.0589 | Watermelon | true |
| SENSOR_009D | 29.8 | false | FARM13 | FIELD_M1 | 2024-06-15T06:37:24Z | 14.7 | blank | blank | 7.6 | 3.98 | -78 | 43.6532 | -79.3832 | Soybean | false |
| sn_xyz77 | 58.9 | true | FARM14 | FIELD_N1 | 2024-06-15T14:11:09Z | 37.2 | 4.7 | 440.2 | 15.2 | 4.11 | -62 | 45.4215 | -75.6996 | Kiwi | false |
| sensor-103c | 4.3 | false | FARM15 | FIELD_O1 | 2024-06-15T22:42:56Z | -10.2 | 0.6 | blank | 52.7 | 2.1 | -105 | 51.0447 | -114.0719 | Carrot | true |
| SENSOR_010E | 35.9 | false | FARM16 | FIELD_P1 | 2024-06-16T09:24:18Z | 23.8 | 1.9 | blank | 2.3 | 3.79 | -82 | 53.5461 | -113.4938 | Potato | false |
| sn_mno33 | 75.2 | true | FARM17 | FIELD_Q1 | 2024-06-16T17:56:41Z | 36.5 | 3.6 | 371.4 | 17 | 3.96 | -53 | 45.5017 | -73.5673 | Lettuce | false |
| sensor-104d | 100 | false | FARM18 | FIELD_R1 | 2024-06-16T23:13:57Z | 60 | 25 | blank | 90 | 0 | -121 | 48.4284 | -123.3656 | Cassava | true |
| SENSOR_011F | 22 | false | FARM19 | FIELD_S1 | 2024-06-17T07:34:32Z | 11.2 | 1.2 | blank | 5.5 | 3.77 | -92 | 49.2827 | -123.1207 | Wheat | false |
What the 121 rows show
from the 121-row sample- 33%irrigation_
event = true - 44.2median soil_
moisture_ percent - 15crop types
- 22.3median soil_
temperature_ celsius - 3.6median soil_
ec_ ds_ per_ m - 267.1median irrigation_
volume_ liters
Median 44.2, from 0.0 to 100.0.
- string 4
- float 9
- datetime 1
- boolean 2
Columns
16 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 4 columns | |||
sensor_id | string | Unique identifier for each IoT soil sensor device | SN_001A |
farm_id | string | Unique identifier for the farm where the sensor is deployed | FARM01 |
field_id | string | Unique identifier for the specific field or plot within the farm | FIELD_A1 |
crop_type | string | Type of crop currently planted in the sensor's fieldoptional | Wheat |
| Numbers 9 columns | |||
soil_moisture_percent | float | Measured soil moisture as a percentage (0-100%)0 to 100 | 32.7 |
soil_temperature_celsius | float | Measured soil temperature in degrees Celsius-30 to 60 | 19.6 |
soil_ec_ds_per_m | float | Measured soil electrical conductivity in deciSiemens per meter (dS/m)0 or more · optional | 1.4 |
irrigation_volume_liters | float | Volume of water applied during the irrigation event, in liters0 or more · optional | 212 |
rainfall_mm | float | Rainfall measured at the sensor location since last reading, in millimeters0 or more · optional | 1.7 |
battery_voltage | float | Current battery voltage of the sensor device0 or more · optional | 3.97 |
signal_strength_dbm | float | Wireless signal strength of the sensor device in dBmoptional | -87 |
latitude | float | Latitude coordinate of the sensor's location-90 to 90 | 36.8772 |
longitude | float | Longitude coordinate of the sensor's location-180 to 180 | -121.7627 |
| Dates and times 1 column | |||
timestamp | datetime | Date and time when the sensor reading was recorded (UTC) | 2024-06-10T07:24:03Z |
| True or false 2 columns | |||
irrigation_event | boolean | Indicates if an irrigation event occurred at the time of measurement | false |
anomaly_detected | boolean | Indicates if an anomaly was detected in the sensor readingoptional | false |
Use it for
An agriculture dashboard
The irrigation_
event rate, soil_ moisture_ percent and a breakdown of farm_ id. Excel, Power BI or Tableau. Why do 40 of 121 rows have irrigation_
event = true? A class exercise
Hand out the rows and one question. Everyone works from the same 121 rows.
- Sensors121SN_001A32.7falsesensor-004b83.1trueSENSOR_007B56.7true
A software demo
Believable sensors with farm_
id, field_ id and timestamp to fill a screen in front of a buyer.
This dataset121 rows16 columns
Yours10,000 rows16 columns
blueprint · iot-precision-irrigation-sensor-data
Behind this dataset
Same schema. As many rows as you need.
These 121 rows came out of a blueprint — 16 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 sensor reading event
- Include timestamp, soil moisture, temperature, and sensor ID
- At least 30% of readings flagged for below-threshold moisture
- Every sensor must report at least twice per day
- Include edge device status (active/faulty) for each event
Open the blueprint in Data Factory
1 credit per row. New accounts start with 25 free credits.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- iot-precision-irrigation-sensor-data