Agri-Product IoT Traceability Logs
This dataset provides comprehensive, event-level traceability logs for agricultural products, integrating IoT sensor readings, supply chain events, geolocations, and compliance data. It enables detailed analysis of product journeys, supports transparency and sustainability initiatives, and facilitates regulatory compliance and operational optimization across the agri-food supply chain.
Sample rows
preview · 8 of 160 rows · all 20 columns| log_idstring | sensor_typestring | sensor_reading_valuefloat | location_statestring | product_idstring | batch_idstring | sensor_idstring | sensor_reading_unitstring | event_typestring | event_timestampdatetime | location_latitudefloat | location_longitudefloat | location_street_addressstring | location_citystring | location_postal_codestring | location_countrystring | operator_idstring | compliance_statusstring | sustainability_certificationstring | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LOG-0001 | temperature | 7.8 | CA | APPLE-101 | BATCH-A-2024 | SENSOR-TEMP-0001 | Celsius | harvested | 2024-01-08T09:16:23Z | 37.7749 | -122.4194 | 123 Orchard Lane | Fresno | 93722 | USA | OP-APPLE-CA-01 | compliant | organic | Harvested in optimal conditions with temperature maintained at 7.8°C. |
| LOG-0002 | humidity | 83.2 | CA | APPLE-101 | BATCH-A-2024 | SENSOR-HUM-0002 | %RH | packaged | 2024-01-09T15:27:45Z | 37.8044 | -122.2712 | 222 Packing Ave | Oakland | 94607 | USA | OP-APPLE-CA-01 | pending | organic | Packaging occurred with high humidity; pending final compliance review. |
| LOG-0003 | gps | 17.4 | CA | APPLE-101 | BATCH-A-2024 | SENSOR-GPS-0003 | km/h | shipped | 2024-01-10T10:45:12Z | 34.0522 | -118.2437 | 456 Transport Rd | Los Angeles | 90021 | USA | OP-APPLE-CA-01 | compliant | organic | Shipment speed recorded by GPS sensor; within safe limits. |
| LOG-0004 | vibration | 183.5 | NY | APPLE-101 | BATCH-A-2024 | SENSOR-VIB-0004 | m/s² | received | 2024-01-11T11:32:30Z | 40.7128 | -74.006 | 789 Warehouse Blvd | New York | 10007 | USA | OP-APPLE-NY-02 | compliant | organic | Received with minimal vibration; product integrity confirmed. |
| LOG-0005 | temperature | 10.2 | MI | GRAPE-204 | BATCH-G-2024 | SENSOR-TEMP-0005 | Celsius | harvested | 2024-02-05T09:01:17Z | 45.4642 | 9.19 | Via Vigneto 1 | Milan | 20100 | Italy | OP-GRAPE-IT-01 | compliant | organic | Harvested in Milan vineyards; temperature optimal for grapes. |
| LOG-0006 | humidity | 72.3 | RM | GRAPE-204 | BATCH-G-2024 | SENSOR-HUM-0006 | %RH | packaged | 2024-02-06T14:25:59Z | 41.9028 | 12.4964 | Via Roma 16 | Rome | 00100 | Italy | OP-GRAPE-IT-01 | pending | organic | Humidity during packaging slightly below optimal; pending review. |
| LOG-0007 | pressure | 32.5 | blank | GRAPE-204 | BATCH-G-2024 | SENSOR-PRES-0007 | kPa | stored | 2024-02-07T17:34:08Z | 48.8566 | 2.3522 | Rue des Vins 5 | Paris | 75001 | France | OP-GRAPE-FR-02 | compliant | organic | Pressure monitored during storage; maintained within safe range. |
| LOG-0008 | gps | 23.6 | blank | GRAPE-204 | BATCH-G-2024 | SENSOR-GPS-0008 | km/h | shipped | 2024-02-08T08:15:27Z | 52.3676 | 4.9041 | Logisticsweg 3 | Amsterdam | 1012 | Netherlands | OP-GRAPE-NL-03 | compliant | organic | Shipment in progress; GPS speed within expected range. |
| LOG-0009 | other | 0.01 | blank | GRAPE-204 | BATCH-G-2024 | SENSOR-OTHER-0009 | ppm | inspected | 2024-02-10T13:31:00Z | 51.5074 | -0.1278 | Inspection Road 7 | London | EC1A | UK | OP-GRAPE-UK-04 | compliant | organic | Inspection complete; all readings within normal parameters. |
| LOG-0010 | temperature | 27.5 | blank | BANANA-302 | BANANA-LOT-302 | SENSOR-TEMP-0010 | Celsius | harvested | 2024-03-12T07:59:28Z | -1.2921 | 36.8219 | 123 Plantation Way | Nairobi | 00100 | Kenya | OP-BANANA-KE-01 | compliant | none | Banana harvest in Kenya; temperature at 27.5°C, typical for the region. |
| LOG-0011 | humidity | 98.7 | blank | BANANA-302 | BANANA-LOT-302 | SENSOR-HUM-0011 | %RH | packaged | 2024-03-13T15:42:31Z | -1.286389 | 36.817223 | 456 Packing St | Nairobi | 00100 | Kenya | OP-BANANA-KE-01 | pending | none | High humidity during packaging; review required. |
| LOG-0012 | vibration | 9998.2 | blank | BANANA-302 | BANANA-LOT-302 | SENSOR-VIB-0012 | m/s² | shipped | 2024-03-14T11:23:10Z | -23.5505 | -46.6333 | 789 Export Ave | São Paulo | 01000 | Brazil | OP-BANANA-BR-02 | non-compliant | none | Excessive vibration detected during shipment; flagged for review. |
| LOG-0013 | gps | 0 | blank | BANANA-302 | BANANA-LOT-302 | SENSOR-GPS-0013 | m/s | received | 2024-03-15T14:55:17Z | -34.6037 | -58.3816 | 101 Receiving Rd | Buenos Aires | C1000 | Argentina | OP-BANANA-AR-03 | pending | none | No movement detected upon receiving; further inspection needed. |
| LOG-0014 | humidity | 64.1 | blank | RICE-410 | RICE-LOT-410 | SENSOR-HUM-0014 | %RH | harvested | 2024-04-02T08:13:09Z | 30.0444 | 31.2357 | 12 Rice Field | Cairo | 11511 | Egypt | OP-RICE-EG-01 | compliant | none | Rice harvest in Cairo; humidity in optimal range. |
| LOG-0015 | pressure | 150 | blank | RICE-410 | RICE-LOT-410 | SENSOR-PRES-0015 | kPa | packaged | 2024-04-03T13:22:30Z | 35.6895 | 139.6917 | Rice Lane 16 | Tokyo | 100-0001 | Japan | OP-RICE-JP-02 | non-compliant | none | Pressure exceeded safe limit during packaging; flagged as non-compliant. |
| LOG-0016 | temperature | -2.7 | blank | RICE-410 | RICE-LOT-410 | SENSOR-TEMP-0016 | Celsius | stored | 2024-04-04T17:18:15Z | 27.7172 | 85.324 | Storage 3 | Kathmandu | 44600 | Nepal | OP-RICE-NP-03 | pending | none | Storage temperature below zero; pending compliance review. |
| LOG-0017 | gps | 22.3 | blank | RICE-410 | RICE-LOT-410 | SENSOR-GPS-0017 | km/h | shipped | 2024-04-05T10:32:40Z | 19.4326 | -99.1332 | Shipping St 21 | Mexico City | 01000 | Mexico | OP-RICE-MX-04 | compliant | none | Rice batch shipped at average speed; GPS sensor operational. |
| LOG-0018 | other | 4980.5 | blank | RICE-410 | RICE-LOT-410 | SENSOR-OTHER-0018 | ppm | inspected | 2024-04-06T13:08:34Z | 55.7558 | 37.6173 | Inspection Center | Moscow | 101000 | Russia | OP-RICE-RU-05 | non-compliant | none | High ppm detected during inspection; marked non-compliant. |
| LOG-0019 | gps | 14.2 | blank | COFFEE-501 | COFFEE-LOT-501 | SENSOR-GPS-0019 | m/s | shipped | 2024-05-04T07:44:55Z | -15.7939 | -47.8828 | Export St 9 | Brasilia | 70000 | Brazil | OP-COFFEE-BR-01 | compliant | fairtrade | Coffee shipment speed within limits; fairtrade standards observed. |
| LOG-0020 | humidity | 41.8 | blank | COFFEE-501 | COFFEE-LOT-501 | SENSOR-HUM-0020 | %RH | received | 2024-05-05T13:36:41Z | 35.6762 | 139.6503 | Receiving Dock 5 | Tokyo | 105-0001 | Japan | OP-COFFEE-JP-02 | compliant | fairtrade | Coffee received in Tokyo; humidity within safe range. |
What the 160 rows show
from the 160-row sampleVibration (sensor type) stands out: mean sensor_
- 29.6median sensor_
reading_ value - 3compliance statuses
- 3sustainability certifications
- 7sensor reading units
- 7event types
- 28location countries
Median 29.6, from -3.2 to 10,000.
- string 16
- float 3
- datetime 1
Columns
20 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 16 columns | |||
log_id | string | Unique identifier for each traceability log entryunique | LOG-0001 |
product_id | string | Unique identifier for the agricultural product being tracked | APPLE-101 |
batch_id | string | Identifier for the product batch or lot | BATCH-A-2024 |
sensor_id | string | Unique identifier for the IoT sensor capturing the reading | SENSOR-TEMP-0001 |
sensor_type | string | Type of IoT sensor (e.g., temperature, humidity, GPS, vibration)6 values | temperature |
sensor_reading_unit | string | Unit of measurement for the sensor reading (e.g., Celsius, %RH, m/s, ppm)7 units | Celsius |
event_type | string | Type of supply chain event (e.g., harvested, packaged, shipped, received, stored)7 values | harvested |
location_street_address | string | Street address of the event locationoptional | 123 Orchard Lane |
location_city | string | City of the event locationoptional | Fresno |
location_state | string | State or province of the event locationoptional | CA |
location_postal_code | string | Postal code of the event locationoptional | 93722 |
location_country | string | Country of the event locationoptional | USA |
operator_id | string | Identifier for the operator or company responsible for the eventoptional | OP-APPLE-CA-01 |
compliance_status | string | Compliance status of the product at this event (e.g., compliant, non-compliant, pending)compliant · non-compliant · pending · optional | compliant |
sustainability_certification | string | Sustainability or origin certification at this event (e.g., organic, fairtrade, none)organic · fairtrade · none · other · optional | organic |
notes | string | Additional notes or comments about the event or readingoptional | Harvested in optimal cond… |
| Numbers 3 columns | |||
sensor_reading_value | float | Value recorded by the IoT sensor | 7.8 |
location_latitude | float | Latitude coordinate of the event location-90 to 90 | 37.7749 |
location_longitude | float | Longitude coordinate of the event location-180 to 180 | -122.4194 |
| Dates and times 1 column | |||
event_timestamp | datetime | Date and time when the supply chain event occurred | 2024-01-08T09:16:23Z |
Use it for
An agriculture dashboard
Sensor_
reading_ value by sensor_ type and a breakdown of location_ state. Excel, Power BI or Tableau. Why do the 19 vibration rows have a mean sensor_
reading_ value of 4,262? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Logs160LOG-00017.8temperat…LOG-000283.2humidityLOG-000317.4gps
A software demo
Believable logs with product_
id, batch_ id and sensor_ id to fill a screen in front of a buyer.
blueprint · agri-product-iot-traceability-logs
Behind this dataset
Same schema. As many rows as you need.
These 160 rows came out of a blueprint — 20 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.
- Each product batch is assigned a unique trace ID
- Sensor readings (temperature, humidity, location) are logged at each supply chain node
- Timestamps must consistently increase along the product path
- Any deviation outside storage thresholds is flagged
- Batches must record at least three supply chain events
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
- agri-product-iot-traceability-logs