Goal and Objectives
The primary goal is to extract detailed vegetation segments from field images. These images include additional tools like gray mats and color cards to enhance the accuracy of segmentation. The segments extracted are intended to be used directly for detailed analysis or to create synthetic image data, facilitating diverse machine learning tasks.
Explore Report on the current status of the dataset
Process ImagesBacklog
Continuously Monitor and Process
Images Sample Examples
Weeds
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| columns | 5 |
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| exclude | Picture0.jpg, NCB05413.JPG, NCB05414.JPG, TXB05162.JPG, TXB05163.JPG, TXB05161.JPG |
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| sort | name |
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Cover crops
| Gallery |
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| include | TXB05163.JPG, TXB05162.JPG, TXB05161.JPG |
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| columns | 3 |
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Cash Crops
| Gallery |
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| include | NCB05413.JPG, NCB05414.JPG |
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| columns | 2 |
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| sort | name |
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Key Features
Feature | Description |
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Reporting clean and organize backlogged data, understand it deficiencies, and review issues with current collection and data upload output: organize the contents of various tables and blob containers into a single working table (.csv). output 2: generate report of missing data, current status of the dataset and collection across locations, species, and plant types. Generate report about an major issues like the discrepency between uploaded jpgs, raws, and table entries. | |
Color correction | organzie data by state, capture date, and 3 hour time intervals | |
Semi-Automatic | |
Utilize a combination of classical digital image processing, and deep learning to generate multiple types of labels for the images, such as bounding boxes and segmentation masks. These labels are critical for training machine learning models and for validating the accuracy of computer vision algorithms.image quality classification | |
Verify label accuracy | |
Validation
Timeline
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| title | Roadmap%20Planner |
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| hash | d8bab6b49cf0999c5768bba8b64e834e93b8b388da6af304833db32745b48727 |
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\uD83D\uDDD2 Outputs
ID | Requirement | Category | Description | Format | Notes |
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A.1 | weed semantic mask | dataset | | PNG | |
A.2 | cover crop semantic mask | dataset | | PNG | |
A.3 | cash crop semantic mask | dataset | | PNG | |
A.4 | bounding box detection | dataset | | JSON objectkey/value pair | identify format (consider yolo) |
A.5 | Metadata | dataset | contains various metadata | JSON | |
B.5 | Report | Monitoring | | PNG/CSV | |
A.6 | image content tags | dataset | tags in metadata for whether the image contains a gray mat, color correction card, or has a plain background | JSON key/value pair | |
Open Questions
Question | Answer | Date Answered |
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Out of Scope