Agriculture
17 columns
200 rows
8 formats

Agricultural Crop Disease Records

This dataset provides detailed synthetic records of crop disease outbreaks, including region, crop type, disease specifics, severity, and estimated impact. It is structured for advanced risk modeling, disease prediction, and spatial-temporal analysis, making it invaluable for AgriTech research, policy planning, and precision agriculture solutions.

last updated 21 Aug 2025
by GoMask

The brief that made it

Crop disease risk modeling and prediction

Sample rows

preview · 8 of 200 rows · all 17 columns

record_idstring	severity_levelstring	estimated_crop_loss_tonnesfloat	region_countrystring	observation_datedate	region_namestring	region_statestring	region_districtstring	crop_typestring	disease_namestring	disease_categorystring	affected_area_hectaresfloat	reporter_namestring	reporter_contactstring	latitudefloat	longitudefloat	notesstring
AB9X-7K2Q	high	190.2	India	2024-03-21	Punjab Plains	Punjab	Ludhiana	Wheat	Leaf Rust	fungal	850.5	Punjab Agri Council	contact@punjabagri.in	30.9012	75.8575	Outbreak linked to early irrigation and humid conditions.
C7VQ9MJK	medium	37.4	USA	2023-07-18	Central Valley	California	Fresno	Tomato	Southern Blight	fungal	120.8	Fresno County Extension	info@fresnoext.org	36.7356	-119.7851	Disease spread accelerated by warm summer temperatures.
XQJ8-2ZLP	critical	33.1	Japan	2022-05-11	Chiba Prefecture	Chiba	Kisarazu	Mandarin	Citrus Tristeza Virus	viral	47.3	Japan Citrus Observatory	citrus@jco.jp	35.3708	139.9257	First critical outbreak observed in region since 2012.
4GZP2B8K	high	21.2	South Africa	2021-09-14	Western Cape	Western Cape	Stellenbosch	Grapes	Powdery Mildew	fungal	95.6	SA Wine Growers Assn.	growers@wine-sa.org	-33.9321	18.8604	Seasonal rainfall contributed to outbreak severity.
7P2R-X1QJ	medium	17.6	Argentina	2024-02-26	Pampas Region	Buenos Aires	Tandil	Barley	Barley Yellow Dwarf	viral	72.4	Argentine Crop Lab	lab@argcrop.ar	-37.3217	-59.1332	Aphid vectors confirmed in multiple districts.
QRJ4-6ZNB	medium	6.7	Bangladesh	2019-08-11	Sylhet Basin	Sylhet	Moulvibazar	Tea	Red Spider Mite	pest	43.5	Sylhet Tea Board	info@sylhetteaboard.bd	24.4826	91.7537	Outbreak linked to dry season and lack of control measures.
Z8N2-G7QK	high	42.5	France	2023-05-06	Normandy Fields	Normandy	Rouen	Potato	Late Blight	fungal	150.2	France Crop Watch	monitor@cropwatch.fr	49.4432	1.0993	Persistent rain increased blight risk in potato fields.
P9L6-3RXY	medium	18.9	New Zealand	2022-11-02	Canterbury Plains	Canterbury	Ashburton	Wheat	Fusarium Head Blight	fungal	69.9	NZ Grain Council	contact@grainnz.co.nz	-43.9007	171.7433	Blight spread due to wet autumn conditions.
M2BV7JK8	high	31.6	Egypt	2020-06-27	Delta Nile	Gharbia	Tanta	Cotton	Cotton Bollworm	pest	113.4	Egypt Cotton Board	cottonboard@eg.gov	30.7855	31.0004	Bollworm numbers peaked during summer months.
VQX2-4KJ9	critical	22.8	Canada	2024-01-17	Saskatchewan Fields	Saskatchewan	Regina	Barley	Rhizomania	viral	34.5	Canada Crop Monitor	cropmonitor@agcan.ca	50.4452	-104.6189	Critical outbreak detected in central Saskatchewan.
K8QX5R7L	medium	26.5	Argentina	2018-04-15	Cordoba Province	Cordoba	Rio Cuarto	Maize	Corn Borer	pest	97.2	Cordoba AgriLab	agri@cordobalab.ar	-33.1245	-64.2275	Borer infestation linked to delayed planting.
WJ5K-2RPA	low	2.3	Australia	2022-08-09	Eastern Highlands	Victoria	Bendigo	Barley	Scald	fungal	18.9	Vic Crop Services	support@viccrop.au	-36.757	144.2794	Routine outbreak, well managed with fungicide.
LQ6J4VNP	medium	5.8	Germany	2023-04-23	Lower Saxony	Niedersachsen	Braunschweig	Sugar Beet	Beet Rust	fungal	27.7	German Beet Watch	beet@germanwatch.de	52.2689	10.5264	Rust detected in scattered patches across farms.
R7KQ5XJP	high	138.6	Philippines	2024-03-02	Mindanao Plains	Davao	Tagum	Rice	Rice Blast	fungal	442.1	PhilRice Center	outbreak@philrice.ph	7.4478	125.8129	High humidity and rainfall contributed to outbreak.
J8K2-VQRP	medium	10.4	Peru	2020-07-15	Arequipa Valley	Arequipa	Cayma	Potato	Andean Potato Moth	pest	54.2	Peru Crop Extension	extension@perucrops.pe	-16.3989	-71.5368	Moth larval numbers above average for season.
P4ZJ1X8L	high	86.7	Mexico	2018-11-06	Yucatan Peninsula	Yucatan	Merida	Maize	Maize Streak Virus	viral	298.3	Yucatan Ag Office	ag@yucatan.mx	20.9674	-89.5926	Vector control measures implemented in response.
K5ZQ8R3A	medium	16.2	Russia	2023-09-04	Volgograd Steppe	Volgograd	Kamyshin	Sunflower	White Rot	fungal	81.6	Russian Agro Monitor	monitor@agro.ru	50.0805	45.4069	Rot detected after heavy rains in early autumn.
ZJ7Q-9BEX	low	1.7	Morocco	2022-06-19	Casablanca Plains	Casablanca	Mohammedia	Barley	Net Blotch	fungal	14.3	Moroccan Crop Bureau	info@crop.ma	33.7156	-7.3898	Early detection resulted in swift containment.
8PKJ2QX4	critical	2.1	Greenland	2021-02-23	Greenland Edge	blank	blank	Barley	Arctic Wilt	other	9.2	blank	blank	71.7069	-42.6043	Rare outbreak; system test case for monitoring.
QK2P5XJ7	low	0	N/A	2022-10-28	Null Island	blank	blank	Unknown	N/A	other	0	blank	blank	0	0	Synthetic record for system testing; no real outbreak.

What the 200 rows show

from the 200-row sample

Critical (severity level) stands out: mean estimated_crop_loss_tonnes is 918.3, against 31.4 for the rest.

16.2median estimated_crop_loss_tonnes
5disease categories
15crop types
62.0median affected_area_hectares

Mean estimated_crop_loss_tonnes by severity_level200 rows

estimated_crop_loss_tonnes200 rows, in bands of 200

Median 16.2, from 0.0 to 2,000.

region_country200 rows · top 10 of 32 values

Canada16
Argentina14
France14
Germany11
Morocco11
South Africa10
New Zealand10
Peru10
Russia10
India9

17 columns by typefrom the column list below

string 12
float 4
date 1

Columns

17 columns in three groups

blueprint · 17 columns
column	type	description	example
Text 12 columns
`record_id`	string	Unique identifier for each disease observation recordunique	AB9X-7K2Q
`region_name`	string	Name of the geographic region where the outbreak occurred	Punjab Plains
`region_country`	string	Country of the region	India
`region_state`	string	State or province of the region (if applicable)optional	Punjab
`region_district`	string	District or sub-region within the state/province (if applicable)optional	Ludhiana
`crop_type`	string	Type of crop affected by the disease	Wheat
`disease_name`	string	Name of the disease affecting the crop	Leaf Rust
`disease_category`	string	Broad category of the disease (e.g., fungal, bacterial, viral, pest)fungal · bacterial · viral · pest · other	fungal
`severity_level`	string	Severity of the outbreak (e.g., low, medium, high, critical)low · medium · high · critical	high
`reporter_name`	string	Name of the person or organization reporting the outbreakoptional	Punjab Agri Council
`reporter_contact`	string	Contact information of the reporter (email or phone)optional	contact@punjabagri.in
`notes`	string	Additional notes or comments about the outbreakoptional	Outbreak linked to early …
Numbers 4 columns
`affected_area_hectares`	float	Estimated area affected by the disease in hectares0 or more	850.5
`estimated_crop_loss_tonnes`	float	Estimated crop loss due to the disease in metric tonnes0 or more · optional	190.2
`latitude`	float	Latitude coordinate of the outbreak location-90 to 90 · optional	30.9012
`longitude`	float	Longitude coordinate of the outbreak location-180 to 180 · optional	75.8575
Dates and times 1 column
`observation_date`	date	Date when the disease outbreak was observed	2024-03-21

Use it for

An agriculture dashboard
Estimated_crop_loss_tonnes by severity_level and a breakdown of region_country. Excel, Power BI or Tableau.
Why do the 20 critical rows have a mean estimated_crop_loss_tonnes of 918.3?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Records200
AB9X-7K2Q190.2high
C7VQ9MJK37.4medium
XQJ8-2ZLP33.1critical
A software demo
Believable records with observation_date, region_name and region_country to fill a screen in front of a buyer.

This dataset200 rows17 columns

Yours1,000 rows17 columnsregion_name: UK only

blueprint · agricultural-crop-disease-records

Behind this dataset

Same schema. As many rows as you need.

These 200 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

Disease type randomly assigned from 10 classes
Severity scored 1-5 per outbreak
Region codes mapped to country standards
Date of detection within last 5 years

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-crop-disease-records

What should your data show?

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Sample rows

What the 200 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?