Usability
8.8/10Consistent formatting and concise metadata make exploration accessible.
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Computer Vision dataset prepared for reproducible experimentation and practical model evaluation.
985
174
8.1/10
3.2 GB
Image dataset of diseased crops in Algeria, focusing on wheat, barley, and tomatoes. Annotated with bounding boxes for computer vision model training.
Algerian Crop Disease Image Dataset aggregates domain-specific records from AnavTech + Ministry of Agriculture with a structure designed for quick downstream analysis and training.
Files include core records, lightweight metadata, and machine-learning-ready fields. The layout is intended for straightforward ingestion with common Python or JavaScript data tooling.
Published by AnavTech + Ministry of Agriculture and community contributors supporting open Algerian AI resources.
2
| File | Size | Description |
|---|---|---|
| main.zip | 3.2 GB | Primary data split for training and analysis. |
| part_02.zip | 21 MB | Supplementary partition 2 for validation and incremental updates. |
Browse files, inspect rows, and sort columns before downloading.
Files
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| Column | Type | Completeness | Description |
|---|---|---|---|
| record_id | string | 100% | Stable record identifier. |
| source_name | string | 100% | Origin reference for AnavTech + Ministry of Agriculture collection workflow. |
| task_1 | categorical | 100% | Object Detection target signal associated with this dataset. |
| tag_1 | string | 100% | Tag marker for Agriculture use cases and filtering. |
| tag_2 | string | 100% | Tag marker for Computer Vision use cases and filtering. |
| updated_at | datetime | 100% | Last update timestamp for each row. |
Consistent formatting and concise metadata make exploration accessible.
Core fields are strong; deeper long-tail metadata remains incremental.
Source pedigree is clear and suitable for experimentation.
Regular updates are present but should continue scaling with demand.
Community Member
Practitioner
15 Oct 2025
★★★★☆
Solid dataset for practical model development and benchmarking.