Automate Crop Anomaly Detection with AI: Image URL to Anomaly Alert
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Detect anomalous crops from image URLs instantly. This n8n workflow uses Voyage AI embeddings and Qdrant to compare images against a dataset, alerting you to new crop types.
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About This Workflow
How it works
This n8n workflow automates crop anomaly detection. It begins with an Execute Workflow Trigger that accepts an image URL. The URL is then processed by a Set node (Image URL hardcode) before being sent to a HTTP Request node (Embed image) to generate an embedding vector using the Voyage AI multimodal model. This embedding is then used to query a Qdrant collection (Get similarity of medoids) to find the most similar crop types based on pre-defined medoids and cluster thresholds. A Code node (Compare scores) analyzes the similarity scores against these thresholds. If the image's similarity falls below all defined thresholds, it's flagged as an anomaly, and a notification is generated. The workflow also includes nodes to count the total number of crops and individual crop counts within the Qdrant collection, which is used to dynamically set the number of similar crops to query.
Key Features
- Real-time anomaly detection for crop images.
- Utilizes advanced AI embeddings from Voyage AI.
- Leverages Qdrant for efficient similarity search.
- Dynamic adjustment of similarity query based on dataset size.
- Clear anomaly alerting mechanism.
How To Use
- Trigger Setup: Configure the
Execute Workflow Triggerto accept animageURLquery parameter. - Credentials: Ensure you have valid API credentials for Voyage AI (via HTTP Header Auth) and Qdrant (via Qdrant API).
- Qdrant Configuration: In the
Variables for medoidsnode, updateqdrantCloudURLandcollectionNameto match your Qdrant setup. - Medoid Configuration: In the
Variables for medoidsnode, ensureclusterCenterTypeandclusterThresholdCenterTypeare correctly set based on your Qdrant collection schema. - Dataset Preparation (Pre-requisite): Ensure your Qdrant collection is populated with agricultural crop data, including vectors and payload fields like
is_medoidandis_medoid_cluster_threshold. - Execute: Trigger the workflow with an image URL. The output will be a message indicating if the crop is recognized or if it's an anomaly.
Apps Used
Workflow JSON
{
"id": "b95f93d5-31b8-4e4d-ad88-c804387ecc09",
"name": "Automate Crop Anomaly Detection with AI: Image URL to Anomaly Alert",
"nodes": 0,
"category": "AI & LLM",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: b95f93d5-31b8...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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