Crop Irrigation Sensor Schedule
This dataset provides comprehensive, real-time irrigation scheduling information for agricultural fields, integrating sensor readings, crop details, and actionable recommendations to optimize water usage. Designed for agritech startups and farm managers, it enables automated, data-driven irrigation management to boost crop yields and reduce waste. The dataset supports advanced analytics, operational monitoring, and precision agriculture initiatives.
Sample rows
preview · 8 of 68 rows · all 17 columns| schedule_idstring | sensor_typestring | sensor_readingfloat | is_irrigation_completedboolean | field_idstring | sensor_idstring | reading_unitstring | reading_timestampdatetime | scheduled_irrigation_startdatetime | scheduled_irrigation_enddatetime | irrigation_duration_minutesinteger | irrigation_volume_litersfloat | crop_typestring | manager_idstring | field_location_latitudefloat | field_location_longitudefloat | actionable_insightstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SCH_B1_001 | soil-moisture | 21.7 | false | FIELD_NW_01 | SNS_SM_101 | % | 2024-05-02T07:13:00 | 2024-05-02T08:00:00 | 2024-05-02T08:45:00 | 45 | 800.5 | corn | MGR_AK_01 | 41.321 | -93.204 | Increase irrigation due to low soil moisture |
| SCH_B1_002 | humidity | 75.3 | true | FIELD_SW_02 | SNS_HM_114 | % | 2024-05-02T09:20:00 | 2024-05-02T10:00:00 | 2024-05-02T10:30:00 | 30 | 415 | wheat | MGR_JL_04 | 37.672 | -122.367 | blank |
| SCH_B1_003 | temperature | 29.8 | false | FIELD_NE_03 | SNS_TP_231 | °C | 2024-05-03T06:55:00 | 2024-05-03T07:20:00 | 2024-05-03T08:00:00 | 40 | 680.7 | soybean | MGR_RB_02 | 40.412 | -95.122 | Decrease irrigation; temperature high but moisture sufficient |
| SCH_B1_004 | rainfall | 0 | true | FIELD_NW_01 | SNS_RF_103 | mm | 2024-05-02T08:00:00 | 2024-05-02T09:00:00 | 2024-05-02T09:30:00 | 30 | 399.2 | corn | MGR_AK_01 | 41.321 | -93.204 | No rainfall detected; maintain irrigation |
| SCH_B1_005 | soil-moisture | 37.9 | true | FIELD_SE_04 | SNS_SM_141 | % | 2024-05-01T11:01:00 | 2024-05-01T12:00:00 | 2024-05-01T12:25:00 | 25 | 350 | lettuce | MGR_PM_07 | 35.556 | -83.112 | blank |
| SCH_B1_006 | soil-moisture | 19.4 | false | FIELD_SW_02 | SNS_SM_115 | % | 2024-05-02T11:44:00 | 2024-05-02T12:30:00 | 2024-05-02T13:10:00 | 40 | 480.9 | wheat | MGR_JL_04 | 37.672 | -122.367 | Increase irrigation; soil moisture critically low |
| SCH_B1_007 | humidity | 68.9 | false | FIELD_NE_03 | SNS_HM_232 | % | 2024-05-03T08:12:00 | 2024-05-03T09:00:00 | 2024-05-03T09:40:00 | 40 | 710 | soybean | MGR_RB_02 | 40.412 | -95.122 | blank |
| SCH_B1_008 | temperature | 23.2 | true | FIELD_SE_04 | SNS_TP_142 | °C | 2024-05-01T13:19:00 | 2024-05-01T14:00:00 | 2024-05-01T14:20:00 | 20 | 295.6 | lettuce | MGR_PM_07 | 35.556 | -83.112 | blank |
| SCH_B1_009 | soil-moisture | 27.5 | false | FIELD_NW_01 | SNS_SM_102 | % | 2024-05-02T14:03:00 | 2024-05-02T15:00:00 | 2024-05-02T15:50:00 | 50 | 900 | corn | MGR_AK_01 | 41.321 | -93.204 | blank |
| SCH_B1_010 | rainfall | 1.7 | true | FIELD_SW_02 | SNS_RF_116 | mm | 2024-05-02T16:59:00 | 2024-05-02T17:30:00 | 2024-05-02T18:00:00 | 30 | 430.2 | wheat | MGR_JL_04 | 37.672 | -122.367 | Reduce irrigation; rainfall detected |
| SCH_B1_011 | soil-moisture | 44.2 | false | FIELD_NE_03 | SNS_SM_233 | % | 2024-05-03T10:50:00 | 2024-05-03T11:30:00 | 2024-05-03T12:10:00 | 40 | 610.3 | soybean | MGR_RB_02 | 40.412 | -95.122 | blank |
| SCH_B1_012 | rainfall | 0.4 | true | FIELD_SE_04 | SNS_RF_145 | mm | 2024-05-01T15:31:00 | 2024-05-01T16:00:00 | 2024-05-01T16:20:00 | 20 | 298.1 | lettuce | MGR_PM_07 | 35.556 | -83.112 | blank |
| SCH_B1_013 | humidity | 60.9 | true | FIELD_NW_01 | SNS_HM_104 | % | 2024-05-02T17:55:00 | 2024-05-02T18:30:00 | 2024-05-02T19:00:00 | 30 | 405 | corn | MGR_AK_01 | 41.321 | -93.204 | blank |
| SCH_B1_014 | temperature | 26.8 | false | FIELD_SW_02 | SNS_TP_117 | °C | 2024-05-02T19:23:00 | 2024-05-02T20:00:00 | 2024-05-02T20:35:00 | 35 | 470.6 | wheat | MGR_JL_04 | 37.672 | -122.367 | blank |
| SCH_B1_015 | rainfall | 1.2 | true | FIELD_NE_03 | SNS_RF_234 | mm | 2024-05-03T13:05:00 | 2024-05-03T13:40:00 | 2024-05-03T14:10:00 | 30 | 400.7 | soybean | MGR_RB_02 | 40.412 | -95.122 | Rainfall detected; reduce irrigation |
| SCH_B1_016 | humidity | 82.1 | true | FIELD_SE_04 | SNS_HM_146 | % | 2024-05-01T17:41:00 | 2024-05-01T18:00:00 | 2024-05-01T18:20:00 | 20 | 280.2 | lettuce | MGR_PM_07 | 35.556 | -83.112 | blank |
| SCH_B1_017 | temperature | 25.1 | false | FIELD_NW_01 | SNS_TP_105 | °C | 2024-05-02T20:40:00 | 2024-05-02T21:00:00 | 2024-05-02T21:45:00 | 45 | 770 | corn | MGR_AK_01 | 41.321 | -93.204 | blank |
| SCH_B1_018 | soil-moisture | 31.6 | false | FIELD_SW_02 | SNS_SM_118 | % | 2024-05-02T22:30:00 | 2024-05-02T23:00:00 | 2024-05-02T23:30:00 | 30 | 420.9 | wheat | MGR_JL_04 | 37.672 | -122.367 | blank |
| SCH_B1_019 | temperature | 27.3 | true | FIELD_NE_03 | SNS_TP_235 | °C | 2024-05-03T15:25:00 | 2024-05-03T16:00:00 | 2024-05-03T16:50:00 | 50 | 900.2 | soybean | MGR_RB_02 | 40.412 | -95.122 | blank |
| SCH_B1_020 | soil-moisture | 45.8 | true | FIELD_SE_04 | SNS_SM_147 | % | 2024-05-01T19:11:00 | 2024-05-01T20:00:00 | 2024-05-01T20:30:00 | 30 | 390 | lettuce | MGR_PM_07 | 35.556 | -83.112 | blank |
What the 68 rows show
from the 68-row sampleHumidity (sensor type) stands out: mean sensor_
- 54%is_
irrigation_ completed = true - 27.9median sensor_
reading - 4reading units
- 8crop types
- 14managers
- 30median irrigation_
duration_ minutes
Median 27.9, from 0.0 to 87.4.
- string 8
- integer 1
- float 4
- datetime 3
- boolean 1
Columns
17 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
schedule_id | string | Unique identifier for each irrigation schedule entryunique | SCH_B1_001 |
field_id | string | Unique identifier for the agricultural field or plot | FIELD_NW_01 |
sensor_id | string | Unique identifier for the sensor providing field data | SNS_SM_101 |
sensor_type | string | Type of sensor used (e.g., soil-moisture, temperature, humidity)soil-moisture · temperature · humidity · rainfall · other | soil-moisture |
reading_unit | string | Unit of measurement for the sensor reading (e.g., %, °C, mm)4 units | % |
crop_type | string | Type of crop planted in the field8 types | corn |
actionable_insight | string | Short recommendation or insight generated from sensor data (e.g., 'Increase irrigation due to low soil moisture')optional | Increase irrigation due t… |
manager_id | string | Identifier for the farm manager or responsible partyoptional | MGR_AK_01 |
| Numbers 5 columns | |||
sensor_reading | float | Latest sensor reading value relevant to irrigation decision0 or more | 21.7 |
irrigation_duration_minutes | integer | Duration of scheduled irrigation in minutes1 or more | 45 |
irrigation_volume_liters | float | Planned volume of water to be dispensed during irrigation (in liters)0 or more | 800.5 |
field_location_latitude | float | Latitude coordinate of the field-90 to 90 · optional | 41.321 |
field_location_longitude | float | Longitude coordinate of the field-180 to 180 · optional | -93.204 |
| Dates and times 3 columns | |||
reading_timestamp | datetime | Timestamp when the sensor reading was taken | 2024-05-02T07:13:00 |
scheduled_irrigation_start | datetime | Scheduled start time for irrigation based on sensor data | 2024-05-02T08:00:00 |
scheduled_irrigation_end | datetime | Scheduled end time for irrigation | 2024-05-02T08:45:00 |
| True or false 1 column | |||
is_irrigation_completed | boolean | Indicates whether the scheduled irrigation has been completed | false |
Use it for
An agriculture dashboard
The is_
irrigation_ completed rate, sensor_ reading by sensor_ type and a breakdown of field_ id. Excel, Power BI or Tableau. Why do the 16 humidity rows have a mean sensor_
reading of 74.7? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Schedules68SCH_B1_00121.7soil-moi…SCH_B1_00275.3humiditySCH_B1_0040rainfall
A software demo
Believable schedules with field_
id, sensor_ id and sensor_ type to fill a screen in front of a buyer.
This dataset68 rows17 columns
Yours1,000 rows17 columnsfield_location_latitude: UK only
blueprint · crop-irrigation-sensor-schedule
Behind this dataset
Same schema. As many rows as you need.
These 68 rows came out of a blueprint — 17 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 field sensor event
- Irrigation time is calculated based on soil moisture and weather forecast
- Include anomaly detection for faulty sensor readings
- Schedule adjustments allowed only with approved override reason
- Log both automated and manual irrigation actions
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
- crop-irrigation-sensor-schedule