Precision Farm Sensor Readings
This dataset provides granular, time-stamped IoT sensor readings from precision agriculture systems, including soil moisture, crop health indices, equipment status, and environmental conditions. It enables spatial and temporal analysis for AgriTech tool development, machine learning for yield optimization, and operational monitoring across farms and fields. The comprehensive structure supports research, analytics, and real-world deployment scenarios in smart farming.
Sample rows
preview · 8 of 200 rows · all 16 columns| reading_idstring | sensor_typestring | battery_level_pctfloat | equipment_statusstring | sensor_idstring | farm_idstring | field_idstring | latitudefloat | longitudefloat | timestampdatetime | soil_moisture_pctfloat | crop_health_indexfloat | equipment_idstring | temperature_cfloat | humidity_pctfloat | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RD-000001 | soil_moisture | 91.7 | blank | SN-TYO1-008A | FARM-001 | F1-East | 35.6895 | 139.6917 | 2024-05-01T06:14:23Z | 88.2 | blank | blank | 18.4 | 72.6 | blank |
| RD-000002 | soil_moisture | 87.1 | blank | SN-TYO2-004B | FARM-001 | F1-West | 35.6897 | 139.692 | 2024-05-01T08:47:11Z | 9.7 | blank | blank | 19.5 | 68.3 | Early morning reading, irrigation not started yet. |
| RD-000003 | soil_moisture | 76.5 | blank | SN-LDN1-009C | FARM-002 | LDN-Plot3 | 51.5074 | -0.1278 | 2024-05-02T11:32:49Z | 65.8 | blank | blank | 15.6 | 80 | blank |
| RD-000004 | soil_moisture | 52.8 | blank | SN-LDN2-003D | FARM-002 | LDN-Plot1 | 51.5076 | -0.1276 | 2024-05-02T13:09:02Z | 100 | blank | blank | 12.9 | 91.2 | Edge case: maximum moisture after heavy rain. |
| RD-000005 | soil_moisture | 80.3 | blank | SN-NYC1-002A | FARM-003 | NYC-North | 40.7128 | -74.006 | 2024-05-03T09:21:43Z | 7.8 | blank | blank | 23 | 66.5 | Edge case: dry patch detected. |
| RD-000006 | soil_moisture | 72.9 | blank | SN-NYC2-005B | FARM-003 | NYC-South | 40.7131 | -74.0056 | 2024-05-03T15:18:57Z | 56.1 | blank | blank | 27.8 | 58.9 | blank |
| RD-000007 | soil_moisture | 93.2 | blank | SN-SYD1-007A | FARM-004 | SYD-West | -33.8688 | 151.2093 | 2024-05-05T05:33:12Z | 34.2 | blank | blank | 11.8 | 89.7 | blank |
| RD-000008 | soil_moisture | 86.5 | blank | SN-SYD2-006C | FARM-004 | SYD-East | -33.869 | 151.2095 | 2024-05-05T22:44:01Z | 60.3 | blank | blank | 14.1 | 85.3 | Consistent values after irrigation. |
| RD-000009 | soil_moisture | 0 | blank | SN-POL1-001Z | FARM-005 | Polar-01 | 90 | 0 | 2024-05-06T01:16:25Z | 0 | blank | blank | -50 | 0 | Edge case: polar maximum, instrument reset required. |
| RD-000010 | soil_moisture | 68.9 | blank | SN-RIO1-012Y | FARM-006 | RIO-North | -22.9068 | -43.1729 | 2024-05-07T12:01:38Z | 47.6 | blank | blank | 28.2 | 74.5 | blank |
| RD-000011 | crop_health | 85.7 | blank | SN-TYO3-011D | FARM-001 | F1-East | 35.6898 | 139.6915 | 2024-05-01T07:09:44Z | blank | 0.92 | blank | 18.5 | 76.4 | blank |
| RD-000012 | crop_health | 61.2 | blank | SN-LDN3-010F | FARM-002 | LDN-Plot2 | 51.5072 | -0.1282 | 2024-05-02T10:21:10Z | blank | 0.08 | blank | 13.8 | 83.1 | Edge case: low NDVI, check for pest damage. |
| RD-000013 | crop_health | 77.5 | blank | SN-NYC3-008G | FARM-003 | NYC-East | 40.7132 | -74.0052 | 2024-05-03T10:02:22Z | blank | 0.64 | blank | 19.9 | 68.7 | blank |
| RD-000014 | crop_health | 44.8 | blank | SN-SYD3-007K | FARM-004 | SYD-North | -33.8691 | 151.2096 | 2024-05-05T15:09:51Z | blank | 1 | blank | 13.6 | 86.4 | Edge case: maximum health after fertilization. |
| RD-000015 | crop_health | 67.2 | blank | SN-RIO2-014H | FARM-006 | RIO-South | -22.9065 | -43.1731 | 2024-05-07T13:47:21Z | blank | 0.39 | blank | 30.4 | 79.7 | blank |
| RD-000016 | crop_health | 43.9 | blank | SN-JHB1-013X | FARM-007 | JHB-Central | -26.2041 | 28.0473 | 2024-05-10T11:31:18Z | blank | 0.62 | blank | 22.3 | 66.4 | blank |
| RD-000017 | crop_health | 0 | blank | SN-POL2-010M | FARM-005 | Polar-02 | -90 | 180 | 2024-05-06T01:17:42Z | blank | 0 | blank | -50 | 100 | Edge case: instrument error, NDVI zero. |
| RD-000018 | crop_health | 75.2 | blank | SN-BRL1-015T | FARM-008 | BRL-North | -15.7939 | -47.8828 | 2024-05-11T14:10:33Z | blank | 0.83 | blank | 26.2 | 58.7 | blank |
| RD-000019 | crop_health | 61.8 | blank | SN-TYO4-016Q | FARM-001 | F2-North | 35.6899 | 139.6921 | 2024-05-01T17:55:22Z | blank | 0.54 | blank | 21.2 | 82.5 | blank |
| RD-000020 | crop_health | 53.6 | blank | SN-CHI1-017W | FARM-009 | CHI-East | 41.8781 | -87.6298 | 2024-05-12T15:03:20Z | blank | 0.37 | blank | 23.7 | 53.2 | blank |
What the 200 rows show
from the 200-row sampleEquipment_
- 56.2median battery_
level_ pct - 40farms
- 54.0median soil_
moisture_ pct - 0.64median crop_
health_ index - 21.2median temperature_
c - 66.1median humidity_
pct
Median 56.2, from 0.0 to 100.0.
- string 8
- float 7
- datetime 1
Columns
16 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
reading_id | string | Unique identifier for each sensor reading recordunique | RD-000001 |
sensor_id | string | Unique identifier for the IoT sensor device | SN-TYO1-008A |
sensor_type | string | Type of sensor (e.g., soil_moisture, crop_health, equipment_usage)soil_moisture · crop_health · equipment_usage | soil_moisture |
farm_id | string | Unique identifier for the farm or agricultural site | FARM-001 |
field_id | string | Identifier for the specific field or plot within the farm | F1-East |
equipment_id | string | Identifier for the equipment being monitored (if applicable)optional | EQ-TYO-003 |
equipment_status | string | Operational status of the equipment (e.g., active, idle, maintenance)active · idle · maintenance · offline · optional | active |
notes | string | Additional notes or comments about the readingoptional | Started at 08:30 UTC. |
| Numbers 7 columns | |||
latitude | float | GPS latitude coordinate where the reading was taken-90 to 90 | 35.6895 |
longitude | float | GPS longitude coordinate where the reading was taken-180 to 180 | 139.6917 |
soil_moisture_pct | float | Measured soil moisture percentage (0-100%)0 to 100 · optional | 88.2 |
crop_health_index | float | Crop health index value (e.g., NDVI or similar, typically 0-1)0 to 1 · optional | 0.92 |
temperature_c | float | Ambient temperature at the sensor location in degrees Celsius-50 to 60 · optional | 18.4 |
humidity_pct | float | Relative humidity at the sensor location (0-100%)0 to 100 · optional | 72.6 |
battery_level_pct | float | Sensor device battery level percentage (0-100%)0 to 100 · optional | 91.7 |
| Dates and times 1 column | |||
timestamp | datetime | Date and time when the sensor reading was recorded (UTC) | 2024-05-01T06:14:23Z |
Use it for
An agriculture dashboard
Battery_
level_ pct by sensor_ type and a breakdown of equipment_ status. Excel, Power BI or Tableau. Why do the 69 equipment_
usage rows have a mean battery_ level_ pct of 31.2? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Readings200RD-00000191.7soil_moi…RD-00000287.1soil_moi…RD-00000376.5soil_moi…
A software demo
Believable readings with sensor_
id, sensor_ type and farm_ id to fill a screen in front of a buyer.
This dataset200 rows16 columns
Yours1,000 rows16 columns
blueprint · precision-farm-sensor-readings
Behind this dataset
Same schema. As many rows as you need.
These 200 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 record includes timestamp, sensor ID, field zone
- Moisture values range from 10% to 70%
- Equipment usage logged per hour
- Crop health scores normalized 0-1
- Missing values randomly injected at 2% rate
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
- precision-farm-sensor-readings