Garden Anthracnose Disease Identification Image Dataset
#Image Classification #Disease Detection #Plant Disease Recognition #Pest and Disease Monitoring #Agricultural Production Management #Smart Agriculture
- 500 records
- 1.4G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-14
AI Analysis & Value Prop
With the intensification and large-scale application of agricultural production, crop diseases, especially anthracnose, have become a major challenge affecting economic efficiency. Currently, disease detection relies on manual experience and small-scale field observations, making it difficult to identify and respond to disease changes in real-time effectively. This dataset provides high-quality anthracnose images to assist in the development of intelligent disease detection systems, improving agricultural production efficiency and crop health. Data collection is conducted using professional agricultural drones and high-resolution cameras, covering various plant species and disease severities, with detailed delineation of disease spots. Data quality is ensured through multiple rounds of expert annotation, cross-verification by multiple teams, and consistency checks. The annotation team consists of pathology experts and PhD botanists, with a team size of over 20 people. The image data is standardized and enhanced, stored in high-quality JPG format, making it suitable for model training.
Dataset Insights
Sample Examples
0c1f2fa5**.jpg|4640*6960|1.55 MB
06acd872**.jpg|4640*6960|1.57 MB
328a0907**.jpg|4640*6960|2.03 MB
bb4b7158**.jpg|4640*6960|2.72 MB
10ffa4ba**.jpg|4640*6960|1.34 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| disease_presence | boolean | Indicates whether signs of anthracnose disease are present in the image. |
| severity_level | integer | The severity level of anthracnose disease, typically ranging from 1 to 5. |
| affected_area_percentage | float | The percentage of area affected by anthracnose disease in the image. |
| leaf_color_variation | string | The type of color variation in leaves compared to normal ones. |
| symptom_type | string | The specific symptom type of anthracnose on crops, such as spots or wilting. |
| crop_type | string | The specific type of crop shown in the image. |
| growth_stage | string | The growth stage of the crop in the image, such as seedling or maturity. |
| leaf_spot_size | string | The size of the spots on the leaves, typically measured in millimeters. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What is garden anthracnose?
- Garden anthracnose is a fungal disease affecting garden crops, particularly ornamental plants, often causing leaf spots and premature wilting.
- How does this dataset help in identifying garden anthracnose?
- This dataset includes a large number of images used to train machine learning models with the goal of automatically identifying and monitoring anthracnose in garden crops.
- Which crops are commonly affected by garden anthracnose?
- Commonly affected crops include plants such as peach trees, pineapples, strawberries, and various ornamental plants.
- What expertise is needed to use the garden anthracnose identification image dataset?
- Using this dataset for anthracnose identification requires some foundational knowledge in agronomy or plant pathology, as well as machine learning techniques.
- What is the application of image datasets in agriculture, forestry, and fisheries?
- In agriculture, forestry, and fisheries, image datasets can assist in automating pest and disease detection, improving the efficiency and accuracy of disease control.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Garden Anthracnose Disease Identification Image Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/682b459b46fa8d0b43e97ad7a85e778e},
urldate={2026-02-04},
keywords={Plant Disease Identification Dataset, Anthracnose Monitoring Images, Agricultural AI Training Set, Pest and Disease Image Recognition},
version={1.0}
}Using this in research? Please cite us.
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