Automated Crop Anomaly Detection with AI
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Effortlessly identify anomalous crop images within your dataset. This workflow leverages advanced AI embeddings to detect deviations, ensuring the integrity and accuracy of your agricultural data.
About This Workflow
This n8n workflow provides a robust solution for automated crop anomaly detection. By integrating with Voyage.ai for multimodal embeddings and Qdrant for vector search, it transforms image URLs into actionable insights. The tool analyzes incoming crop images, generates embeddings, and queries a pre-populated Qdrant collection. It then compares the similarity scores against defined thresholds for known crop types. If an image falls below all established similarity thresholds, it's flagged as a potential anomaly, alerting users to new or unexpected crop types in their dataset. This ensures high-quality, accurate crop data for further analysis.
Key Features
- AI-Powered Image Analysis: Utilizes Voyage.ai's multimodal embeddings to understand image content.
- Vector Database Integration: Leverages Qdrant for efficient similarity searches and data management.
- Automated Anomaly Flagging: Automatically identifies images that deviate from known crop types.
- Configurable Thresholds: Allows for flexible definition of what constitutes an anomaly based on your dataset.
- Clear Alerting System: Provides a distinct message when a new or undefined crop is detected.
How To Use
- Configure Voyage.ai Credentials: Set up your API key in n8n for the Voyage API credential.
- Configure Qdrant Credentials: Set up your Qdrant cloud credentials in n8n.
- Define Crop Variables: In the 'Variables for medoids' node, ensure
qdrantCloudURL,collectionName,clusterCenterType, andclusterThresholdCenterTypeare correctly set to match your Qdrant setup and the payload structure of your collection. - Prepare Qdrant Collection: Ensure your Qdrant collection (
agricultural-cropsby default) is populated with crop embeddings, includingcrop_name,is_medoid(boolean), andis_medoid_cluster_threshold(numeric score) fields. - Input Image URL: Use the workflow's trigger (e.g., HTTP Request, Manual Trigger) to provide an image URL to the 'Embed image' node.
- Execute Workflow: Run the workflow to process the image and receive an anomaly detection result in the 'Compare scores' node's output.
Apps Used
Workflow JSON
{
"id": "937a885f-226e-43da-817b-aaaa90b0935e",
"name": "Automated Crop Anomaly Detection with AI",
"nodes": 26,
"category": "DevOps",
"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: 937a885f-226e...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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