Precision Farming Sensor Feeds
This dataset provides granular, time-stamped sensor readings from precision agriculture deployments, including spatial coordinates, device metadata, and crop context. It enables advanced analytics for crop yield optimization, anomaly detection, and sustainability research, making it highly valuable for agri-tech startups and researchers focused on smart farming solutions.
Sample rows
preview · 8 of 200 rows · all 14 columns| sensor_feed_idstring | sensor_typestring | sensor_valuefloat | is_anomalyboolean | crop_typestring | timestampdatetime | sensor_idstring | field_idstring | latitudefloat | longitudefloat | unitstring | battery_levelfloat | signal_strengthfloat | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SF-0001 | soil_moisture | 67.5 | false | wheat | 2024-04-10T08:12:00Z | S-1001 | F-01 | 38.1243 | -95.4576 | % | 76.2 | -52.3 | blank |
| SF-0002 | temperature | 21.6 | false | rice | 2024-04-10T09:30:00Z | S-1002 | F-02 | 27.4502 | 78.3021 | °C | 81.9 | -60.7 | blank |
| SF-0003 | humidity | 59.7 | false | corn | 2024-04-10T09:45:00Z | S-1003 | F-03 | 41.2321 | -93.8872 | % | 74.5 | -46 | blank |
| SF-0004 | ph | 6.7 | false | soybean | 2024-04-10T10:05:00Z | S-1004 | F-04 | 39.5012 | -89.2317 | pH | 68.7 | -68.8 | blank |
| SF-0005 | light_intensity | 65483 | false | tomato | 2024-04-10T11:21:00Z | S-1005 | F-05 | 32.9213 | -111.6543 | lux | 90.3 | -67.2 | Morning sun, clear sky |
| SF-0006 | other | -3.12 | true | potato | 2024-04-10T12:33:00Z | S-1006 | F-06 | 46.2311 | -119.7832 | mV | 55 | -93.6 | Root voltage below threshold |
| SF-0007 | soil_moisture | 23.4 | false | lettuce | 2024-04-10T13:51:00Z | S-1007 | F-07 | 36.8602 | -121.6457 | % | 89.6 | -44.7 | Pre-irrigation check |
| SF-0008 | temperature | 15.3 | false | barley | 2024-04-10T14:03:00Z | S-1008 | F-08 | 53.5423 | -1.3402 | °C | 77.8 | -49.2 | blank |
| SF-0009 | humidity | 81.1 | false | grape | 2024-04-10T15:14:00Z | S-1009 | F-09 | 41.9056 | 12.4536 | % | 70.2 | -73.5 | blank |
| SF-0010 | ph | 5.2 | false | cassava | 2024-04-10T16:22:00Z | S-1010 | F-10 | -2.1621 | 42.1221 | pH | 61.7 | -85 | Post-fertilization |
| SF-0011 | light_intensity | 0 | false | pepper | 2024-04-10T16:44:00Z | S-1011 | F-11 | 37.7517 | -97.8221 | lux | 45.8 | -84.2 | Nighttime reading |
| SF-0012 | other | -9.3 | true | blank | 2024-04-10T17:08:00Z | S-1012 | F-12 | -90 | 0 | VRMS | 2.5 | -126.7 | Pole location, calibration anomaly |
| SF-0013 | soil_moisture | 98.7 | true | carrot | 2024-04-11T06:20:00Z | S-1013 | F-13 | 56.7212 | 37.6128 | % | 18 | -101.2 | Possible flooding detected |
| SF-0014 | temperature | 48.1 | true | eggplant | 2024-04-11T07:31:00Z | S-1014 | F-14 | 36.1532 | -95.9928 | °C | 11.7 | -88.9 | Heat wave |
| SF-0015 | humidity | 99.9 | true | onion | 2024-04-11T07:55:00Z | S-1015 | F-15 | 29.4241 | -98.4936 | % | 12.5 | -131 | Humidity spike, possible rain |
| SF-0016 | ph | 8.9 | true | spinach | 2024-04-11T08:12:00Z | S-1016 | F-16 | 47.6062 | -122.3321 | pH | 9.2 | -123.4 | Soil pH out of range |
| SF-0017 | light_intensity | 700000 | true | blank | 2024-04-11T08:40:00Z | S-1017 | F-17 | 89.9999 | -179.9999 | lux | 3.2 | -129 | Edge-case: solstice light peak |
| SF-0018 | soil_moisture | 0.1 | true | wheat | 2024-04-11T09:11:00Z | S-1018 | F-01 | 38.1243 | -95.4576 | % | 0 | -120.1 | Critical dryness detected |
| SF-0019 | temperature | -28.3 | true | rice | 2024-04-11T09:36:00Z | S-1019 | F-02 | 27.4502 | 78.3021 | °C | 0 | -130 | Sensor freeze event |
| SF-0020 | humidity | 0 | true | corn | 2024-04-11T10:02:00Z | S-1020 | F-03 | 41.2321 | -93.8872 | % | 0 | -127.2 | Sensor battery depleted |
What the 200 rows show
from the 200-row sampleLight_
- 38%is_
anomaly = true - 23.1median sensor_
value - 6units
- 46.0median battery_
level - -75.5median signal_
strength
Median 23.1, from -29.2 to 710,000.
- string 7
- float 5
- datetime 1
- boolean 1
Columns
14 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 7 columns | |||
sensor_feed_id | string | Unique identifier for each sensor data feed recordunique | SF-0001 |
sensor_id | string | Unique identifier for the sensor device | S-1001 |
sensor_type | string | Type of sensor (e.g., soil_moisture, temperature, humidity, pH, light_intensity)6 values | soil_moisture |
field_id | string | Identifier for the agricultural field or plot where the sensor is located | F-01 |
crop_type | string | Type of crop being monitored at the sensor locationoptional | wheat |
unit | string | Unit of measurement for the sensor value (e.g., % for moisture, °C for temperature)6 units | % |
notes | string | Optional notes or comments about the reading (e.g., manual observations, weather events)optional | Morning sun, clear sky |
| Numbers 5 columns | |||
latitude | float | Latitude coordinate of the sensor location-90 to 90 | 38.1243 |
longitude | float | Longitude coordinate of the sensor location-180 to 180 | -95.4576 |
sensor_value | float | Measured value from the sensor at the given timestamp | 67.5 |
battery_level | float | Current battery level of the sensor device (percentage)0 to 100 · optional | 76.2 |
signal_strength | float | Wireless signal strength of the sensor device (dBm)optional | -52.3 |
| Dates and times 1 column | |||
timestamp | datetime | Date and time when the sensor reading was captured (UTC recommended) | 2024-04-10T08:12:00Z |
| True or false 1 column | |||
is_anomaly | boolean | Flag indicating if the sensor reading is considered anomalousoptional | false |
Use it for
An agriculture dashboard
The is_
anomaly rate, sensor_ value by sensor_ type and a breakdown of crop_ type. Excel, Power BI or Tableau. Why do the 33 light_
intensity rows have a mean sensor_ value of 257,551? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Sensor feeds200SF-000167.5soil_moi…SF-0006-3.12otherSF-0012-9.3other
A software demo
Believable sensor feeds with timestamp, sensor_
id and sensor_ type to fill a screen in front of a buyer.
blueprint · precision-farming-sensor-feeds
Behind this dataset
Same schema. As many rows as you need.
These 200 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.
- Sensors log hourly soil moisture, temperature, pH.
- Each record linked to specific field coordinates.
- Outliers flagged automatically.
- Missing data gaps under 3%.
- Timestamped in UTC.
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-farming-sensor-feeds