Agriculture
12 columns
200 rows
8 formats

Crop Disease Image Annotations

This dataset provides high-quality, synthetic annotations of crop images, each labeled with disease type and severity level, along with optional metadata such as location and annotator details. It is ideal for training and benchmarking computer vision models for early crop disease detection, supporting agricultural research, precision farming, and disease monitoring initiatives.

last updated 12 Jul 2025
by GoMask

The brief that made it

Training and evaluating computer vision models for crop disease detection

Sample rows

preview · 8 of 200 rows · all 12 columns

image_idstring	disease_severitystring	image_widthinteger	crop_typestring	disease_namestring	annotator_idstring	annotation_datedate	location_latitudefloat	location_longitudefloat	image_heightinteger	image_urlstring	notesstring
IMG00001	moderate	2048	wheat	leaf rust	ann_01	2023-04-15	44.9787	-93.265	1536	https://crops.example.com/images/wheat_1092.jpg	Visible orange pustules on leaf surface, recommend fungicide spray.
IMG00002	severe	3999	rice	bacterial blight	expert_a	2024-03-18	22.5726	88.3639	3000	/data/images/rice_3048.png	Leaf tips are yellowing with lesions. Advanced stage detected.
IMG00003	mild	1296	maize	northern leaf blight	ann_21	2022-10-07	0.3476	32.5825	972	https://crops.example.com/images/maize_2202.jpg	Long, elliptical lesions visible on several leaves.
IMG00004	moderate	3456	soybean	soybean rust	mobile_user_2	2021-08-15	-23.5505	-46.6333	2592	https://crops.example.com/images/soybean_8876.jpg	Rust pustules present on lower leaf surface, moderate spread.
IMG00005	severe	4032	tomato	late blight	ai_model_2	2024-05-24	41.0082	28.9784	3024	/images/tomato_3901.jpeg	Extensive necrotic patches with sporulation, immediate action needed.
IMG00006	mild	1600	potato	early blight	ann_07	2023-07-20	51.5074	-0.1278	1200	https://crops.example.com/images/potato_2222.png	Dark concentric rings visible on older leaves.
IMG00007	moderate	2592	grape	downy mildew	expert_c	2022-06-17	44.8378	-0.5792	1944	https://crops.example.com/images/grape_3542.jpg	Oil spots and white downy growth on leaf undersides.
IMG00008	severe	8192	sunflower	verticillium wilt	blank	2021-09-03	50.1109	8.6821	8192	/data/images/sunflower_1001.png	Edge case: very high resolution image. Severe wilting observed.
IMG00009	moderate	2000	cassava	cassava mosaic	ai_model_1	2020-05-04	6.5244	3.3792	1500	https://crops.example.com/images/cassava_2212.jpg	Mosaic patterns with some leaf distortion detected.
IMG00010	mild	1280	banana	black sigatoka	ann_11	2022-11-13	-1.2921	36.8219	960	https://crops.example.com/images/banana_1211.jpg	Streaks and spots with early signs of blackening.
IMG00011	moderate	3024	chili	anthracnose	expert_b	2023-09-09	13.7563	100.5018	3024	/images/chili_4009.png	Sunken lesions with dark margins on chili pods.
IMG00012	mild	1536	barley	net blotch	ann_17	2024-01-05	56.2639	9.5018	1152	https://crops.example.com/images/barley_3001.jpg	Net-like lesions on leaves, low severity.
IMG00013	severe	3600	sorghum	sooty stripe	admin	2024-05-01	12.9716	77.5946	2700	https://crops.example.com/images/sorghum_7011.jpg	Dark, sooty streaks on stems, plant stunted.
IMG00014	healthy	2048	rice	none	mobile_user_4	2023-03-22	16.8661	96.1951	1536	/data/images/rice_1020.jpg	No symptoms observed. Plant is healthy.
IMG00015	moderate	3000	wheat	powdery mildew	ann_03	2022-12-05	35.6895	139.6917	2250	https://crops.example.com/images/wheat_2012.jpg	White powdery spots on upper leaf surface.
IMG00016	severe	9999	maize	gray leaf spot	blank	2022-07-18	34.0522	-118.2437	9999	https://crops.example.com/images/maize_1234.jpg	Edge case: maximum image size. Large gray lesions, yield loss expected.
IMG00017	moderate	1920	tomato	septoria leaf spot	ann_09	2023-06-12	48.8566	2.3522	1080	/data/images/tomato_2013.png	Numerous small, circular spots with gray centers.
IMG00018	healthy	1600	potato	none	ann_18	2024-02-14	-34.6037	-58.3816	1200	https://crops.example.com/images/potato_0089.jpg	Healthy potato plant, no disease symptoms.
IMG00019	mild	1280	grape	powdery mildew	blank	2022-04-08	37.7749	-122.4194	960	https://crops.example.com/images/grape_9090.jpg	blank
IMG00020	moderate	1550	soybean	frog eye leaf spot	ai_model_3	2023-09-26	53.3498	-6.2603	1230	https://crops.example.com/images/soybean_1012.jpg	Tan lesions with reddish borders visible on multiple leaves.

What the 200 rows show

from the 200-row sample

Severe (disease severity) stands out: mean image_width is 4,616, against 1,792 for the rest.

1,920median image_width
1,235median image_height

Mean image_width by disease_severity200 rows

image_width200 rows, in bands of 1,000

Median 1,920, from 1 to 9,999.

crop_type200 rows · top 10 of 26 values

wheat16
tomato15
barley15
potato14
grape14
maize13
cassava13
sunflower12
banana12
soybean11

12 columns by typefrom the column list below

string 7
integer 2
float 2
date 1

Columns

12 columns in three groups

blueprint · 12 columns
column	type	description	example
Text 7 columns
`image_id`	string	Unique identifier for the crop image.unique	IMG00001
`image_url`	string	Direct URL or file path to the crop image.	/images/tomato_3901.jpeg
`crop_type`	string	Type or species of the crop shown in the image.	wheat
`disease_name`	string	Name of the disease identified in the crop image.	leaf rust
`disease_severity`	string	Severity level of the disease as annotated (e.g., healthy, mild, moderate, severe).healthy · mild · moderate · severe	moderate
`annotator_id`	string	Identifier for the person or system that annotated the image.optional	ann_01
`notes`	string	Additional notes or comments about the annotation.optional	Visible orange pustules o…
Numbers 4 columns
`location_latitude`	float	Latitude coordinate where the image was captured (if available).-90 to 90 · optional	44.9787
`location_longitude`	float	Longitude coordinate where the image was captured (if available).-180 to 180 · optional	-93.265
`image_width`	integer	Width of the image in pixels.1 or more · optional	2048
`image_height`	integer	Height of the image in pixels.1 or more · optional	1536
Dates and times 1 column
`annotation_date`	date	Date when the annotation was made.optional	2023-04-15

Use it for

An agriculture dashboard
Image_width by disease_severity and a breakdown of crop_type. Excel, Power BI or Tableau.
Why do the 50 severe rows have a mean image_width of 4,616?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Images200
IMG000012048moderate
IMG000023999severe
IMG000031296mild
A software demo
Believable images with image_url, crop_type and disease_name to fill a screen in front of a buyer.

This dataset200 rows12 columns

Yours1,000 rows12 columnslocation_latitude: UK only

blueprint · crop-disease-image-annotations

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 12 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 image is tagged with crop type and disease class
Severity scored on a 1–5 scale
Images grouped by region and season
Healthy samples included as controls
Annotations verified for label balance

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: crop-disease-image-annotations

What should your data show?

No signup. No card.

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?