Crop Anomaly Detection Tool
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Detects if an input image depicts an anomalous crop not present in the trained dataset.
About This Workflow
This workflow acts as a standalone tool for detecting anomalies within the agricultural crops dataset. It takes an image URL as input and leverages pre-trained embeddings and a Qdrant database to determine if the depicted crop is an anomaly compared to the known crop types. This tool is the third part of a series, building upon previous pipelines that prepared the Qdrant collection with crop data and medoid cluster centers.
Key Features
- Image URL Input: Accepts any image URL to process.
- Voyage.ai Embedding: Generates multimodal embeddings for the input image.
- Qdrant Similarity Search: Queries a Qdrant collection to find the most similar known crop types.
- Threshold-Based Anomaly Detection: Compares the image's similarity scores against pre-defined thresholds for each crop class.
- Clear Anomaly Reporting: Outputs a message indicating if the image is an anomaly or resembles a specific crop.
How To Use
- Input Image URL: Provide an image URL to the
Execute Workflow Triggernode. This can be done by modifying thepinDatasection of the workflow JSON. - Workflow Execution: Run the workflow.
- Receive Result: The
Compare scoresnode will output a message indicating whether the image is an anomaly or resembles a known crop.
Apps Used
Workflow JSON
{
"id": "77992610-72bb-4cc0-9f5c-0283de432cb3",
"name": "Crop Anomaly Detection Tool",
"nodes": 27,
"category": "AI/ML",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 77992610-72bb...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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