Agriculture
17 columns
80 rows
8 formats

Farm Sensor Pest Detection Logs

This dataset provides granular logs of pest detection events from IoT sensors deployed on small and medium farms, including sensor metadata, pest types, intervention actions, and outcomes. It enables comprehensive analysis of pest management practices, sensor effectiveness, and response efficiency, supporting data-driven decision-making for sustainable agriculture.

last updated 20 Jan 2026
by GoMask

The brief that made it

Monitor and optimize pest management interventions across multiple farms

Sample rows

preview · 8 of 80 rows · all 17 columns

log_idstring	intervention_typestring	pest_countinteger	intervention_requiredboolean	intervention_effectivenessstring	farm_idstring	sensor_idstring	sensor_typestring	sensor_location_latitudefloat	sensor_location_longitudefloat	sensor_location_descriptionstring	detection_timestampdatetime	pest_typestring	detection_confidencefloat	intervention_timestampdatetime	sensor_statusstring	notesstring
LOG-00001-A	pesticide	8	true	successful	FARM-001	SENS-01-001	camera	37.2412	-120.8323	Greenhouse 2	2024-05-03T07:23:01	aphid	0.89	2024-05-03T08:02:15	active	Immediate action taken, no issues.
LOG-00002-A	none	4	false	blank	FARM-002	SENS-02-003	pheromone_trap	38.0059	-121.1022	South field, near irrigation	2024-05-04T16:45:47	moth	0.74	blank	active	Low count, watch for trend.
LOG-00003-A	none	2	false	blank	FARM-001	SENS-01-002	acoustic	37.2427	-120.8351	North field perimeter	2024-05-05T13:18:34	beetle	0.92	blank	active	No action required.
LOG-00004-A	biological_control	15	true	partial	FARM-003	SENS-03-001	camera	36.9987	-119.7723	East orchard row 5	2024-05-06T09:30:45	aphid	0.83	2024-05-06T11:20:52	active	Ladybugs released.
LOG-00005-A	none	1	false	blank	FARM-004	SENS-04-004	pheromone_trap	39.1132	-120.5648	Greenhouse 1 south corner	2024-05-07T14:07:11	moth	0.66	blank	active	blank
LOG-00006-A	manual_removal	3	true	successful	FARM-005	SENS-05-006	acoustic	37.9811	-121.2845	West plot, near equipment shed	2024-05-08T20:19:08	beetle	0.95	2024-05-08T21:00:34	active	Staff removed insects by hand.
LOG-00007-A	pesticide	12	true	partial	FARM-003	SENS-03-002	other	36.9979	-119.7718	Orchard north boundary	2024-05-09T07:55:19	moth	0.82	2024-05-09T09:03:21	active	Follow-up spraying scheduled.
LOG-00008-A	biological_control	7	true	successful	FARM-002	SENS-02-004	camera	38.0072	-121.1007	Greenhouse 3, entry	2024-05-10T12:22:55	aphid	0.86	2024-05-10T13:10:44	active	Parasitic wasps used.
LOG-00009-A	manual_removal	5	true	successful	FARM-005	SENS-05-007	acoustic	37.9802	-121.2856	Main field, row 8	2024-05-11T19:40:27	beetle	0.93	2024-05-11T20:05:59	active	Removed with traps.
LOG-00010-A	none	2	false	blank	FARM-004	SENS-04-005	camera	39.1155	-120.5668	South greenhouse aisle	2024-05-12T11:14:15	moth	0.71	blank	active	blank
LOG-00011-A	biological_control	10	true	partial	FARM-001	SENS-01-003	pheromone_trap	37.2398	-120.8342	Field 2, east fence	2024-05-13T07:31:06	aphid	0.8	2024-05-13T09:00:10	active	Ladybugs released, monitoring.
LOG-00012-A	none	1	false	blank	FARM-002	SENS-02-005	other	38.0091	-121.1033	Shed roof	2024-05-13T14:18:32	beetle	0.77	blank	active	Unusual location.
LOG-00013-A	pesticide	11	true	failed	FARM-003	SENS-03-003	camera	36.9965	-119.7739	Orchard middle	2024-05-14T10:49:50	aphid	0.81	2024-05-14T12:00:35	active	Repeat application needed.
LOG-00014-A	manual_removal	6	true	successful	FARM-004	SENS-04-006	acoustic	39.1117	-120.5685	Greenhouse walkway	2024-05-15T17:33:28	beetle	0.83	2024-05-15T18:10:59	active	blank
LOG-00015-A	pesticide	14	true	partial	FARM-005	SENS-05-008	camera	37.9823	-121.2862	Equipment area	2024-05-16T06:05:37	moth	0.9	2024-05-16T07:20:23	active	High count.
LOG-00016-A	none	3	false	blank	FARM-001	SENS-01-004	other	37.2408	-120.8331	Well house	2024-05-16T15:48:20	aphid	0.75	blank	active	blank
LOG-00017-A	none	5	false	blank	FARM-002	SENS-02-006	camera	38.0068	-121.1014	Greenhouse 4	2024-05-17T13:22:46	moth	0.68	blank	active	Increasing numbers.
LOG-00018-A	manual_removal	8	true	successful	FARM-003	SENS-03-004	pheromone_trap	36.9992	-119.7747	Orchard row 2	2024-05-18T11:58:06	beetle	0.88	2024-05-18T12:45:18	active	Quick response.
LOG-00019-A	biological_control	6	true	unknown	FARM-004	SENS-04-007	acoustic	39.1137	-120.5671	East greenhouse	2024-05-19T18:30:29	aphid	0.79	2024-05-19T19:15:45	active	Effectiveness to be evaluated.
LOG-00020-A	none	7	false	blank	FARM-005	SENS-05-009	camera	37.9836	-121.2871	Main barn	2024-05-20T08:27:58	moth	0.82	blank	active	No intervention required.

What the 80 rows show

from the 80-row sample

None (intervention type) stands out: mean pest_count is 4.2, against 10.1 for the rest.

51%intervention_required = true
7median pest_count
4sensor types
5pest types
17farms
0.82median detection_confidence

Mean pest_count by intervention_type80 rows

pest_count80 rows, in bands of 2

Median 7, from 1 to 19.

intervention_effectiveness41 rows with a value · 39 left blank

successful26
partial13
failed1
unknown1

17 columns by typefrom the column list below

string 10
integer 1
float 3
datetime 2
boolean 1

Columns

17 columns in four groups

blueprint · 17 columns
column	type	description	example
Text 10 columns
`log_id`	string	Unique identifier for each pest detection log entryunique	LOG-00001-A
`farm_id`	string	Unique identifier for the farm where the sensor is deployed	FARM-001
`sensor_id`	string	Unique identifier for the IoT sensor device	SENS-01-001
`sensor_type`	string	Type or model of the IoT sensor (e.g., camera, pheromone trap, acoustic)camera · pheromone_trap · acoustic · other	camera
`sensor_location_description`	string	Textual description of the sensor's placement (e.g., greenhouse 2, north field)optional	Greenhouse 2
`pest_type`	string	Type or species of pest detected (e.g., aphid, moth, beetle)5 types	aphid
`intervention_type`	string	Type of intervention performed (e.g., pesticide, biological control, manual removal)5 values · optional	pesticide
`intervention_effectiveness`	string	Assessment of intervention effectiveness (e.g., successful, partial, failed, unknown)successful · partial · failed · unknown · optional	successful
`sensor_status`	string	Operational status of the sensor at the time of detection (e.g., active, offline, maintenance)active · offline · maintenance	active
`notes`	string	Additional notes or comments from farm staff or systemoptional	No action required.
Numbers 4 columns
`sensor_location_latitude`	float	Latitude coordinate of the sensor's location-90 to 90	37.2412
`sensor_location_longitude`	float	Longitude coordinate of the sensor's location-180 to 180	-120.8323
`pest_count`	integer	Number of pests detected in this event1 or more	8
`detection_confidence`	float	Confidence score (0-1) of the detection event as reported by the sensor or algorithm0 to 1 · optional	0.89
Dates and times 2 columns
`detection_timestamp`	datetime	Date and time when the pest was detected by the sensor	2024-05-03T07:23:01
`intervention_timestamp`	datetime	Date and time when the intervention was performedoptional	2024-05-03T08:02:15
True or false 1 column
`intervention_required`	boolean	Indicates if an intervention (e.g., pesticide application) was required based on this detection	true

Use it for

An agriculture dashboard
The intervention_required rate, pest_count by intervention_type and a breakdown of intervention_effectiveness. Excel, Power BI or Tableau.
Why do the 39 none rows have a mean pest_count of 4.2?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Logs80
LOG-00001-A8pesticide
LOG-00002-A4none
LOG-00004-A15biologic…
A software demo
Believable logs with farm_id, sensor_id and sensor_type to fill a screen in front of a buyer.

This dataset80 rows17 columns

Yours10,000 rows17 columnssensor_location_latitude: UK only

blueprint · farm-sensor-pest-detection-logs

Behind this dataset

Same schema. As many rows as you need.

These 80 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 sensor event.
Include pest type, detection time, and sensor ID.
Track intervention status and action timestamp.
Sensors are distributed across distinct farm zones.
Sensor readings must be within manufacturer calibration limits.
Pest detection events must be verified by manual inspection log.

Rows1,000 rows10,000 rows100,000 rows1,000,000 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: farm-sensor-pest-detection-logs

What should your data show?

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Sample rows

What the 80 rows show

Columns

Use it for

An agriculture dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Agriculture

What should your data show?