Predictive Crop Yield Error Logs

This dataset provides detailed anomaly logs from AI-powered crop yield prediction systems, integrating real-time sensor data, weather conditions, and soil metrics. It enables agri-businesses and researchers to identify error patterns, optimize predictive models, and reduce operational risks through actionable insights and traceable resolution workflows.

  • last updated 24 Jan 2026
  • by GoMask
The brief that made it

Error pattern analysis for improving crop yield prediction models

Sample rows

preview · 8 of 72 rows · all 19 columns
log_idstringerror_typestringsensor_valuefloatresolvedbooleanresolved_bystringtimestampdatetimesensor_idstringlocation_latitudefloatlocation_longitudefloatfarm_idstringcrop_typestringerror_severitystringexpected_valuefloatweather_temperaturefloatweather_humidityfloatsoil_phfloatmodel_versionstringresolution_timestampdatetimeerror_messagestring
ERR-8A2B7C01sensor-failureblanktruetech_ops_012024-05-10T09:15:22ZSEN-100339.7321-94.2938FARM-ALPHAcorncriticalblank21.563.26.8v2.1.02024-05-10T10:47:39ZSensor stopped transmitting data unexpectedly.
ERR-539C8D12prediction-outlier14.2falseblank2024-05-11T07:34:11ZSEN-101141.9086-95.8274FARM-BRAVOwheathigh1917.854.67.1v2.1.0blankYield prediction deviated significantly from historical pattern.
ERR-6D41E7ABdata-missingblanktruesystem_auto2024-05-13T12:01:27ZSEN-101639.1205-92.1739FARM-CHARLIEsoybeanmedium23.623.251.76.4v1.9.22024-05-13T16:20:14ZMissing soil moisture readings for last 3 hours.
ERR-29B5A98Fweather-anomaly28.7falseblank2024-05-15T14:48:55ZSEN-102238.8234-93.1127FARM-DELTAricecritical19.335.241.95.7v2.2.3blankSudden temperature spike detected during irrigation.
ERR-7DA3B4C8soil-anomaly5.2trueagronomist_022024-05-17T16:19:42ZSEN-103037.0091-91.4215FARM-ECHOcornhigh6.820.558.35.2v2.2.32024-05-17T17:10:23ZSoil pH dropped below optimal range.
ERR-13F5D2E4sensor-failure2.6truetech_ops_032024-05-19T11:07:16ZSEN-104542.879-96.5011FARM-FOXTROTwheatmedium4.216.970.2blankv2.0.72024-05-19T12:32:41ZBattery voltage below minimum threshold.
ERR-6A7C9B53prediction-outlier25.1falseblank2024-05-20T08:57:33ZSEN-105340.2583-94.8284FARM-GOLFsoybeanlow22.722.847.26.9v2.2.3blankYield prediction slightly exceeds historical maximum.
ERR-1B2D3F67data-missingblanktruesystem_auto2024-05-22T06:31:40ZSEN-106036.7894-92.0156FARM-HOTELricehighblankblankblank6.1v1.9.22024-05-22T08:03:11ZRain event not captured by weather sensors.

What the 72 rows show

from the 72-row sample

Soil-anomaly (error type) stands out: mean sensor_value is 7.6, against 19.4 for the rest.

  • 51%resolved = true
  • 12.7median sensor_value
  • 4error severities
  • 6crop types
  • 12model versions
  • 17.6median expected_value
Mean sensor_value by error_type41 rows
020402.9sensor-f…3 rows18.4predicti…15 rows26.5weather-…9 rows7.6soil-ano…14 rows
sensor_value41 rows, in bands of 10
091817146300104070sensor_value →

Median 12.7, from 2.6 to 60.4.

resolved_by37 rows with a value · 35 left blank
  1. system-auto12
  2. system_auto7
  3. tech_ops_033
  4. tech_ops_012
  5. agronomist_022
  6. agronomist_032
  7. agronomist_042
  8. tech_ops_021
  9. J.Hopkins1
  10. M.Singh1
19 columns by typefrom the column list below
  • string 9
  • float 7
  • datetime 2
  • boolean 1

Columns

19 columns in four groups
blueprint · 19 columns
columntypedescriptionexample
Text 9 columns
log_idstringUnique identifier for each error log entryuniqueERR-8A2B7C01
sensor_idstringUnique identifier for the IoT sensor reporting the anomalySEN-1003
farm_idstringUnique identifier for the farm or fieldFARM-ALPHA
crop_typestringType of crop being monitored (e.g., wheat, corn, rice)6 typescorn
error_typestringCategory of the anomaly (e.g., sensor-failure, prediction-outlier, data-missing, weather-anomaly)6 valuessensor-failure
error_severitystringSeverity level of the error (e.g., low, medium, high, critical)low · medium · high · criticalcritical
error_messagestringDetailed description of the error or anomalySensor stopped transmitti…
model_versionstringVersion identifier of the predictive model used12 versionsv2.1.0
resolved_bystringIdentifier or name of the person/system that resolved the erroroptionaltech_ops_01
Numbers 7 columns
location_latitudefloatLatitude coordinate of the sensor location-90 to 9039.7321
location_longitudefloatLongitude coordinate of the sensor location-180 to 180-94.2938
sensor_valuefloatSensor reading at the time of error (e.g., soil moisture, temperature)optional14.2
expected_valuefloatExpected sensor value or model prediction at the time of erroroptional19
weather_temperaturefloatAmbient temperature at the time of error (Celsius)optional21.5
weather_humidityfloatAmbient humidity at the time of error (%)0 to 100 · optional63.2
soil_phfloatMeasured soil pH value at the time of error0 to 14 · optional6.8
Dates and times 2 columns
timestampdatetimeDate and time when the anomaly was detected2024-05-10T09:15:22Z
resolution_timestampdatetimeDate and time when the error was resolved (if applicable)optional2024-05-10T10:47:39Z
True or false 1 column
resolvedbooleanIndicates whether the error has been addressed/resolvedtrue

Use it for

  • resolved51%37 of 72 rowsmean sensor value by …2.9sens…18.4pred…26.5weat…7.6soil…

    An agriculture dashboard

    The resolved rate, sensor_value by error_type and a breakdown of resolved_by. Excel, Power BI or Tableau.

  • Why do the 14 soil-anomaly rows have a mean sensor_value of 7.6?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable logs with timestamp, sensor_id and location_latitude to fill a screen in front of a buyer.

Not quite right?

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This dataset72 rows19 columns
Yours10,000 rows19 columnslocation_latitude: UK only

blueprint · predictive-crop-yield-error-logs

Behind this dataset

Same schema. As many rows as you need.

These 72 rows came out of a blueprint — 19 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, field ID, predicted vs. actual yield, error code, and sensor data snapshot.
  • Log entries only generated when prediction error exceeds set threshold.
  • Weather data (e.g. rainfall, temperature) auto-linked to each log occurrence.
  • Soil data (moisture, pH) included for contextual analysis.
  • Flag extreme anomalies for agronomist review.
Rows
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
predictive-crop-yield-error-logs

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