Agriculture
17 columns
200 rows
8 formats

Synthetic Plant Disease Diagnosis

This dataset provides synthetic plant disease diagnosis cases, combining image metadata and detailed tabular attributes such as plant species, disease type, severity, environmental conditions, and geolocation. Ideal for training machine learning models in smart agriculture, it enables robust analysis and development of crop management tools for disease detection and prevention.

last updated 5 Sept 2025
by GoMask

The brief that made it

Training machine learning models for automated plant disease detection

Sample rows

preview · 8 of 200 rows · all 17 columns

case_idstring	disease_severitystring	soil_moisturefloat	plant_speciesstring	image_filenamestring	image_widthinteger	image_heightinteger	disease_namestring	location_latitudefloat	location_longitudefloat	collection_datedate	environment_temperaturefloat	environment_humidityfloat	diagnosis_methodstring	diagnosis_confidencefloat	symptoms_observedstring	recommended_actionstring
PLNT-0001	none	65.4	Solanum lycopersicum	Solanum_lycopersicum_healthy_01.jpg	640	480	healthy	34.056	-117.195	2023-07-15	22.6	78.2	visual	blank	blank	Continue regular care and monitoring.
PLNT-0002	severe	88.3	Oryza sativa	Oryza_sativa_Panama_Disease_02.png	2048	2048	Panama Disease	14.5995	120.9842	2024-02-03	37.2	91.6	sensor	0.93	Wilting leaves, yellowing, vascular browning.	Destroy infected plants and rotate crops immediately.
PLNT-0003	moderate	41.1	Triticum aestivum	Triticum_aestivum_Leaf_Curl_03.jpg	800	600	Leaf Curl	55.7558	37.6173	2023-08-20	19.4	66.4	visual	blank	Curled leaf edges, mild chlorosis.	Apply mild fungicide and prune affected leaves.
PLNT-0004	severe	91.2	Solanum tuberosum	Solanum_tuberosum_Late_Blight_04.jpg	4096	4096	Late Blight	48.8566	2.3522	2024-03-10	15.8	95.1	expert	0.82	Dark lesions on leaves and stems, rapid decay.	Destroy infected tubers, apply fungicide, and monitor closely.
PLNT-0005	none	74	Zea mays	Zea_mays_healthy_05.jpg	640	480	healthy	19.4326	-99.1332	2023-09-11	27.5	82.3	sensor	0.88	blank	No action needed; maintain irrigation and monitoring.
PLNT-0006	severe	58.2	Triticum aestivum	Triticum_aestivum_Stem_Rust_06.png	2048	2048	Stem Rust	31.2304	121.4737	2023-10-05	28.7	81.6	expert	0.76	Red-brown pustules on stems and leaves.	Remove infected plants and apply fungicide.
PLNT-0007	mild	39.7	Solanum lycopersicum	Solanum_lycopersicum_Net_Blotch_07.jpg	800	600	Net Blotch	35.6895	139.6917	2024-01-21	18.3	55.2	visual	blank	Small brown spots on leaves.	Monitor regularly and apply preventive fungicide.
PLNT-0008	moderate	33.6	Glycine max	Glycine_max_Frogeye_Leaf_Spot_08.jpg	2048	2048	Frogeye Leaf Spot	40.7128	-74.006	2023-12-02	24.1	70.4	sensor	0.89	Gray circular spots with dark borders.	Apply fungicide and monitor leaf spots.
PLNT-0009	none	66.7	Zea mays	Zea_mays_healthy_09.jpg	4096	4096	healthy	-33.8688	151.2093	2024-04-14	31.9	79.6	visual	blank	blank	Continue regular care and maintain nutrients.
PLNT-0010	mild	29.7	Solanum tuberosum	Solanum_tuberosum_Septoria_Leaf_Spot_10.jpg	800	600	Septoria Leaf Spot	51.5074	-0.1278	2023-11-09	9.6	61.8	visual	blank	Small brown circular spots on foliage.	Monitor for spread and apply mild fungicide.
PLNT-0011	moderate	44.8	Oryza sativa	Oryza_sativa_Leaf_Curl_11.jpg	640	480	Leaf Curl	28.7041	77.1025	2023-06-30	27.2	72.1	visual	blank	Curled leaf tips and yellow streaks.	Prune affected areas and apply fungicide.
PLNT-0012	moderate	51.1	Solanum lycopersicum	Solanum_lycopersicum_Powdery_Mildew_12.png	2048	2048	Powdery Mildew	41.8781	-87.6298	2024-01-18	21.7	80.2	sensor	0.77	White powdery spots on leaves.	Apply fungicide and monitor for new symptoms.
PLNT-0013	none	57.3	Glycine max	Glycine_max_healthy_13.jpg	640	480	healthy	37.7749	-122.4194	2023-05-23	19.2	69.5	visual	blank	blank	No action required, maintain nutrient supply.
PLNT-0014	severe	94.6	Solanum tuberosum	Solanum_tuberosum_Late_Blight_14.jpg	4096	4096	Late Blight	52.52	13.405	2024-03-28	16.4	98.7	sensor	0.95	Rapid leaf necrosis, brown lesions, stem decay.	Destroy infected crops and disinfect equipment.
PLNT-0015	moderate	29.8	Oryza sativa	Oryza_sativa_Powdery_Mildew_15.png	800	600	Powdery Mildew	35.8617	104.1954	2023-04-19	13.4	54.3	sensor	0.69	Powdery white coating on foliage.	Apply fungicide and observe for recurrence.
PLNT-0016	none	71.2	Triticum aestivum	Triticum_aestivum_healthy_16.jpg	2048	2048	healthy	21.0285	105.8542	2023-06-12	26.3	80.8	visual	blank	blank	Continue standard crop management practices.
PLNT-0017	severe	85.7	Zea mays	Zea_mays_Stem_Rust_17.jpg	640	480	Stem Rust	-23.5505	-46.6333	2024-04-01	33.2	92.1	expert	0.71	Rusty pustules on stems, leaf damage.	Remove infected stalks and apply fungicide.
PLNT-0018	moderate	36.2	Solanum lycopersicum	Solanum_lycopersicum_Frogeye_Leaf_Spot_18.png	4096	4096	Frogeye Leaf Spot	51.1657	10.4515	2023-09-28	17.5	67.2	visual	blank	Gray concentric spots with brown margins.	Apply fungicide and remove affected leaves.
PLNT-0019	mild	28.5	Oryza sativa	Oryza_sativa_Net_Blotch_19.jpg	800	600	Net Blotch	43.6532	-79.3832	2023-10-17	14.2	47.9	sensor	0.79	Small net-pattern spots on leaves.	Monitor for progression and apply preventive measures.
PLNT-0020	severe	97.1	Solanum tuberosum	Solanum_tuberosum_Stem_Rust_20.png	2048	2048	Stem Rust	60.1699	24.9384	2024-02-11	11.3	93.7	sensor	0.81	Reddish-brown pustules on stems.	Destroy infected stems and apply fungicide.

What the 200 rows show

from the 200-row sample

Severe (disease severity) stands out: mean soil_moisture is 83.4, against 42.5 for the rest.

49.5median soil_moisture
3diagnosis methods
800median image_width
600median image_height
22.2median environment_temperature
73.5median environment_humidity

Mean soil_moisture by disease_severity200 rows

soil_moisture200 rows, in bands of 10

Median 49.5, from 0.0 to 100.0.

plant_species200 rows · 9 values

Oryza sativa41
Triticum aestivum34
Zea mays32
Solanum lycopersicum28
Glycine max28
Solanum tuberosum24
Brassica napus6
Hordeum vulgare6
Capsicum annuum1

17 columns by typefrom the column list below

string 8
integer 2
float 6
date 1

Columns

17 columns in three groups

blueprint · 17 columns
column	type	description	example
Text 8 columns
`case_id`	string	Unique identifier for each plant disease diagnosis caseunique	PLNT-0001
`image_filename`	string	Filename or path to the plant image associated with the diagnosis case	Zea_mays_healthy_05.jpg
`plant_species`	string	Scientific or common name of the plant species9 specieses	Solanum lycopersicum
`disease_name`	string	Name of the diagnosed disease (or 'healthy' if no disease)	healthy
`disease_severity`	string	Severity of the disease (e.g., mild, moderate, severe, none)none · mild · moderate · severe	none
`symptoms_observed`	string	Text description of symptoms observed on the plantoptional	Small brown leaf spots.
`diagnosis_method`	string	Method used for diagnosis (e.g., visual, sensor, expert)visual · sensor · expert · other · optional	visual
`recommended_action`	string	Recommended action or treatment for the diagnosed diseaseoptional	Continue regular care and…
Numbers 8 columns
`image_width`	integer	Width of the image in pixels1 or more · optional	640
`image_height`	integer	Height of the image in pixels1 or more · optional	480
`location_latitude`	float	Latitude coordinate of where the sample was collected-90 to 90 · optional	34.056
`location_longitude`	float	Longitude coordinate of where the sample was collected-180 to 180 · optional	-117.195
`environment_temperature`	float	Ambient temperature at the time of collection (Celsius)-50 to 60 · optional	22.6
`environment_humidity`	float	Ambient humidity at the time of collection (percentage)0 to 100 · optional	78.2
`soil_moisture`	float	Soil moisture level at the time of collection (percentage)0 to 100 · optional	65.4
`diagnosis_confidence`	float	Confidence score of the diagnosis (0-1)0 to 1 · optional	0.93
Dates and times 1 column
`collection_date`	date	Date when the sample/image was collected	2023-07-15

Use it for

An agriculture dashboard
Soil_moisture by disease_severity and a breakdown of plant_species. Excel, Power BI or Tableau.
Why do the 56 severe rows have a mean soil_moisture of 83.4?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Cases200
PLNT-000165.4none
PLNT-000288.3severe
PLNT-000341.1moderate
A software demo
Believable cases with image_filename, image_width and image_height to fill a screen in front of a buyer.

This dataset200 rows17 columns

Yours1,000 rows17 columnslocation_latitude: UK only

blueprint · synthetic-plant-disease-diagnosis

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

Each case includes image and features
Disease type and severity labeled
Crop type assigned
Diagnosis method indicated

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: synthetic-plant-disease-diagnosis

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?