Agriculture
20 columns
75 rows
8 formats

Agricultural Drone Crop Health Survey

This dataset contains detailed results from drone-based crop health surveys, including field identification, location, crop type, sensor-derived metrics (NDVI, health scores), and actionable recommendations. It enables agri-businesses and farmers to monitor field conditions, benchmark crop health, and optimize interventions for climate adaptation and operational efficiency.

last updated 3 Dec 2025
by GoMask

The brief that made it

Benchmarking crop health across fields and seasons

Sample rows

preview · 8 of 75 rows · all 20 columns

survey_idstring	recommended_interventionstring	health_scorefloat	pest_infestation_detectedboolean	drone_idstring	field_idstring	field_namestring	crop_typestring	survey_datedate	operator_idstring	latitudefloat	longitudefloat	field_area_hectaresfloat	ndvi_meanfloat	ndvi_minfloat	ndvi_maxfloat	water_stress_detectedboolean	disease_detectedboolean	notesstring	image_urlstring
SURV-001-A1	none	82.5	false	DRN-AX45	FIELD-001	North Meadow	wheat	2024-03-15	OP-ALPHA	38.9423	-90.1132	15.3	0.72	0.54	0.86	false	false	Field showing good uniformity, minor edge variation.	https://agdrone.example.com/img/northmeadow-20240315.jpg
SURV-002-A1	pesticide	76.1	true	DRN-ZX11	FIELD-002	Riverbank Plot	maize	2024-03-17	OP-BETA	39.0411	-90.2098	8.9	0.63	0.42	0.79	false	false	Signs of mild pest activity in the southwest section.	https://agdrone.example.com/img/riverbank-20240317.jpg
SURV-003-A1	irrigation	69.3	false	DRN-AX45	FIELD-003	Green Valley	rice	2024-03-20	OP-ALPHA	38.9912	-90.1518	22.4	0.58	0.38	0.72	true	false	Water stress detected near center, recommend irrigation.	https://agdrone.example.com/img/greenvalley-20240320.jpg
SURV-004-A1	none	91.4	false	DRN-QT30	FIELD-004	East Orchard	soybean	2024-03-22	OP-GAMMA	39.0021	-90.2011	10.7	0.81	0.65	0.9	false	false	Excellent crop health throughout field.	https://agdrone.example.com/img/eastorchard-20240322.jpg
SURV-005-A1	other	61.7	false	DRN-ZX11	FIELD-005	Sunny Slope	corn	2024-03-25	OP-DELTA	38.9739	-90.1342	13.2	0.47	0.27	0.59	false	true	Disease detected in north quadrant, further analysis advised.	https://agdrone.example.com/img/sunnyslope-20240325.jpg
SURV-006-A1	none	74.2	false	DRN-QT30	FIELD-006	West Pasture	barley	2024-03-27	OP-GAMMA	39.0305	-90.2493	5.8	0.68	0.49	0.81	false	false	Normal field conditions, no anomalies.	https://agdrone.example.com/img/westpasture-20240327.jpg
SURV-007-A1	irrigation	66.8	false	DRN-AX45	FIELD-007	Highland Farm	wheat	2024-03-29	OP-ALPHA	39.0123	-90.2209	30.5	0.53	0.31	0.72	true	false	Low NDVI in southern sector, water stress present.	https://agdrone.example.com/img/highlandfarm-20240329.jpg
SURV-008-A1	none	96.2	false	DRN-ZX11	FIELD-008	Maple Ridge	soybean	2024-04-01	OP-BETA	38.9651	-90.1782	17.6	0.85	0.69	0.94	false	false	Excellent NDVI, field thriving.	https://agdrone.example.com/img/mapleridge-20240401.jpg
SURV-009-A1	pesticide	72	true	DRN-QT30	FIELD-009	Cedar Grove	maize	2024-04-03	OP-DELTA	39.0198	-90.1193	9.4	0.6	0.45	0.74	false	false	Pest activity increasing, treatment recommended.	https://agdrone.example.com/img/cedargrove-20240403.jpg
SURV-010-A1	other	59.9	false	DRN-AX45	FIELD-010	Hilltop	corn	2024-04-05	OP-GAMMA	38.9783	-90.1428	12.6	0.49	0.35	0.62	false	true	Leaf blight detected, recommend lab testing.	https://agdrone.example.com/img/hilltop-20240405.jpg
SURV-011-A1	none	79.3	false	DRN-ZX11	FIELD-011	Silver Fields	barley	2024-04-07	OP-DELTA	39.0102	-90.2126	7.5	0.7	0.52	0.85	false	false	Healthy crop, minor soil compaction at edges.	https://agdrone.example.com/img/silverfields-20240407.jpg
SURV-012-A1	irrigation	65.4	false	DRN-QT30	FIELD-012	South Corner	rice	2024-04-10	OP-BETA	38.9521	-90.1673	20.1	0.55	0.33	0.68	true	false	Low NDVI in center, recommend additional watering.	https://agdrone.example.com/img/southcorner-20240410.jpg
SURV-013-A1	none	98.1	false	DRN-AX45	FIELD-013	Orchard Lane	soybean	2024-04-12	OP-ALPHA	39.0246	-90.1307	16.8	0.87	0.72	0.95	false	false	Outstanding crop health, ideal growing conditions.	https://agdrone.example.com/img/orchardlane-20240412.jpg
SURV-014-A1	none	89.7	false	DRN-ZX11	FIELD-014	Prairie View	wheat	2024-04-15	OP-GAMMA	38.9832	-90.1991	18.5	0.76	0.59	0.88	false	false	Good yield expected, minimal variation.	https://agdrone.example.com/img/prairieview-20240415.jpg
SURV-015-A1	none	78.4	false	DRN-AX45	FIELD-001	North Meadow	wheat	2024-04-18	OP-ALPHA	38.9423	-90.1132	15.3	0.67	0.48	0.83	false	false	Crop uniformity improving, no issues.	https://agdrone.example.com/img/northmeadow-20240418.jpg
SURV-016-A1	pesticide	74.5	true	DRN-ZX11	FIELD-002	Riverbank Plot	maize	2024-04-21	OP-BETA	39.0411	-90.2098	8.9	0.62	0.44	0.78	false	false	Repeat pest activity, treatment ongoing.	https://agdrone.example.com/img/riverbank-20240421.jpg
SURV-017-A1	none	71.2	false	DRN-AX45	FIELD-003	Green Valley	rice	2024-04-24	OP-ALPHA	38.9912	-90.1518	22.4	0.59	0.4	0.73	false	false	Water stress resolved, NDVI rising.	https://agdrone.example.com/img/greenvalley-20240424.jpg
SURV-018-A1	none	94.7	false	DRN-QT30	FIELD-004	East Orchard	soybean	2024-04-27	OP-GAMMA	39.0021	-90.2011	10.7	0.83	0.68	0.92	false	false	Crop thriving, ideal soil moisture.	https://agdrone.example.com/img/eastorchard-20240427.jpg
SURV-019-A1	other	63.8	false	DRN-ZX11	FIELD-005	Sunny Slope	corn	2024-04-30	OP-DELTA	38.9739	-90.1342	13.2	0.5	0.29	0.62	false	true	Disease persists, further investigation needed.	https://agdrone.example.com/img/sunnyslope-20240430.jpg
SURV-020-A1	none	70.1	false	DRN-QT30	FIELD-006	West Pasture	barley	2024-05-02	OP-GAMMA	39.0305	-90.2493	5.8	0.65	0.47	0.78	false	false	Consistent NDVI readings, no stress detected.	https://agdrone.example.com/img/westpasture-20240502.jpg

What the 75 rows show

from the 75-row sample

Pesticide (recommended intervention) stands out: 11 of its 11 rows have pest_infestation_detected = true, against 1 of 64 for the rest.

16%pest_infestation_detected = true
77.6median health_score
6crop types
17operators
12.6median field_area_hectares
0.62median ndvi_mean

Pest infestation detected rate by recommended_interventionpest_infestation_detected = true

health_score75 rows, in bands of 10

Median 77.6, from 39.2 to 100.0.

drone_id75 rows · top 10 of 18 values

DRN-AX4514
DRN-ZX1113
DRN-QT3011
DRN-CHN-037
DRN-CHN-025
DRN-CHN-045
DRN-CHN-015
DRN-CHN-055
DRN-ALPHA-071
DRN-BETA-041

20 columns by typefrom the column list below

string 9
float 7
date 1
boolean 3

Columns

20 columns in four groups

blueprint · 20 columns
column	type	description	example
Text 9 columns
`survey_id`	string	Unique identifier for each drone crop health surveyunique	SURV-001-A1
`field_id`	string	Unique identifier for the agricultural field being surveyed	FIELD-001
`field_name`	string	Name or label of the agricultural fieldoptional	North Meadow
`crop_type`	string	Type of crop present in the field (e.g., wheat, maize, rice)6 types	wheat
`drone_id`	string	Identifier for the drone used in the surveyoptional	DRN-AX45
`operator_id`	string	Identifier for the operator or company conducting the surveyoptional	OP-ALPHA
`recommended_intervention`	string	Recommended action based on survey results (e.g., irrigation, pesticide, fertilizer)irrigation · pesticide · fertilizer · none · other · optional	none
`notes`	string	Additional comments or observations from the surveyoptional	No intervention needed.
`image_url`	string	URL to the drone-captured image or map of the fieldoptional	https://agdrone.example.c…
Numbers 7 columns
`latitude`	float	Latitude coordinate of the field center-90 to 90	38.9423
`longitude`	float	Longitude coordinate of the field center-180 to 180	-90.1132
`field_area_hectares`	float	Total area of the field in hectares0 or more	15.3
`ndvi_mean`	float	Mean NDVI (Normalized Difference Vegetation Index) value for the field-1 to 1	0.72
`ndvi_min`	float	Minimum NDVI value observed in the field-1 to 1 · optional	0.54
`ndvi_max`	float	Maximum NDVI value observed in the field-1 to 1 · optional	0.86
`health_score`	float	Overall crop health score (0-100) derived from sensor data0 to 100	82.5
Dates and times 1 column
`survey_date`	date	Date when the drone survey was conducted	2024-03-15
True or false 3 columns
`water_stress_detected`	boolean	Indicates if water stress was detected in the fieldoptional	false
`pest_infestation_detected`	boolean	Indicates if pest infestation was detected in the fieldoptional	false
`disease_detected`	boolean	Indicates if crop disease was detected in the fieldoptional	false

Use it for

An agriculture dashboard
The pest_infestation_detected rate, health_score by recommended_intervention and a breakdown of drone_id. Excel, Power BI or Tableau.
Why do 12 of 75 rows have pest_infestation_detected = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Surveys75
SURV-001-A182.5none
SURV-002-A176.1pesticide
SURV-009-A172pesticide
A software demo
Believable surveys with field_id, field_name and crop_type to fill a screen in front of a buyer.

This dataset75 rows20 columns

Yours1,000 rows20 columns

blueprint · agricultural-drone-crop-health-survey

Behind this dataset

Same schema. As many rows as you need.

These 75 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.

Rules it was built with

Each row represents a unique survey of a field section conducted by a drone.
Crop health scores are based on NDVI (Normalized Difference Vegetation Index) calculations (scale 0-1).
Weather conditions at time of survey are included (temperature, humidity, wind speed).
Soil moisture levels are logged from drone sensors (in %).
Intervention recommendations are generated based on crop health and moisture trends.

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

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: agricultural-drone-crop-health-survey

What should your data show?

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Sample rows

What the 75 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?