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 the dataset
Process Images
Monitor
Images Sample Examples
Weeds
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Cover crops
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Cash Crops
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Key Features
Feature | Description |
|---|---|
Reporting | clean and organize backlogged data, understand it deficiencies, and review issues with current collection and data upload
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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
\uD83D\uDDD2 Outputs
ID | Requirement | Category | Description | Format | Notes |
|---|---|---|---|---|---|
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 object | ||
A.5 | Metadata | dataset | JSON | ||
B.5 | Report | Monitoring | PNG/CSV |
Open Questions
Question | Answer | Date Answered |
|---|---|---|
Out of Scope
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