Datasets:
file_name stringclasses 5
values | quality stringclasses 2
values | disease_presence stringclasses 3
values | severity_level stringclasses 2
values | affected_area_percentage stringclasses 4
values | leaf_color_change stringclasses 5
values | leaf_texture stringclasses 4
values | affected_leaf_count stringclasses 4
values |
|---|---|---|---|---|---|---|---|
53ac25d844dc2c8daece53690d847536.jpg | 1920*2560 | Present | Moderate | Approximately 30% | Browning | Dry | Approximately 5 leaves |
53d671af9ce629a7b4591c20ca627868.jpg | 1920*2560 | Leaf blight disease is present. | Moderate | Approximately 30% | Leaves have brown spots | Some leaf surfaces are dry and cracked | 5 leaves |
954e0c541b98d2ef78dc7f9c3853ca05.jpg | 3024*4032 | Present | Moderate | Approximately 20% | Brown | Dry | Multiple leaves |
ab31abc29a970f49e28e448b5eb8c8a2.jpg | 3024*4032 | Present | Moderate | Approximately 15% | Partial browning of leaves | Partially dry areas | Approximately 5 leaves |
b4a4f90bcd2ed1f56b66139e3a7128cf.jpg | 1920*2560 | present | moderate | about 20% | yellowing and brown spots | dry | 8 leaves |
Durian Plantation Disease Leaf Rot Detection Image Dataset
Ensures high-quality data through multiple rounds of annotation and automated consistency checks, combined with reviews by agricultural pathology experts. The annotation team consists of 10 professionals in agriculture and computer vision. Pre-processing steps include noise reduction, size adjustments, and color normalization to enhance the model's recognition capabilities. Data is stored in JPG format, organized hierarchically in clear file directories. The core advantage of this dataset is an annotation accuracy of up to 95% and excellent consistency, with innovative application of data augmentation techniques such as random cropping and color jittering to enrich training samples and improve recognition robustness. Integrates the latest quality assessment methods, improving model accuracy by 10% over other public agricultural datasets based on industry standards, and significantly reduces false alarm rates. Notably, it includes representative disease images from major durian growing areas, providing the model with a comprehensive perspective. Supports transplantation in different scenarios, reflecting the dataset's scalability and versatility.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| disease_presence | boolean | Indicates whether there is a leaf blight disease present in the image. |
| severity_level | string | Describes the severity of the leaf blight, categorized as mild, moderate, or severe. |
| affected_area_percentage | float | Percentage of the leaf surface area affected by the leaf blight disease. |
| leaf_color_change | string | Describes the change in leaf color due to the disease, such as yellowing or browning. |
| leaf_texture | string | Describes changes in the leaf surface texture, such as dryness or cracking. |
| affected_leaf_count | integer | Number of leaves affected by leaf blight disease in the image. |
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 |
Source & Contact
If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com
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