Automated Crop Anomaly Detection: Identify Unknowns Instantly
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This powerful n8n workflow automates the detection of anomalous crops from an input image URL. It leverages Voyage AI for embeddings and Qdrant for vector similarity search to identify if an image depicts a crop not present in a pre-defined dataset.
About This Workflow
The Crop Anomaly Detection Tool is designed to automatically identify if an input image represents a known crop type or an anomaly. By integrating with Voyage AI's multimodal embeddings API and Qdrant's vector database, this workflow can process an image URL and determine its similarity to a curated dataset of agricultural crops. The system generates embeddings for the input image, queries a Qdrant collection for the most similar known crop embeddings, and compares the similarity scores against pre-defined thresholds. If the image's similarity score falls below the thresholds for all known crop types, it's flagged as an anomaly, enabling proactive identification of new or unexpected agricultural elements.
Key Features
- Automated Anomaly Detection: Instantly identify images that do not match a defined set of crop types.
- AI-Powered Embeddings: Utilizes Voyage AI's advanced multimodal embeddings for robust image understanding.
- Vector Database Integration: Leverages Qdrant for efficient similarity searches against a growing crop dataset.
- Configurable Thresholds: Allows for fine-tuning of anomaly detection sensitivity based on cluster medoid scores.
- Clear Status Reporting: Provides straightforward text output indicating whether a crop is similar to known types or potentially anomalous.
How To Use
- Input Image URL: Trigger the workflow with a valid image URL containing the crop to be analyzed.
- Voyage AI Embedding: The workflow uses a hardcoded image URL (or can be configured via trigger) to generate embeddings via the Voyage AI API.
- Qdrant Query: The generated embeddings are used to query a Qdrant collection (e.g.,
agricultural-crops) for similar crop data, using specified cluster center types and thresholds. - Variable Configuration: Ensure the
Variables for medoidsnode is correctly configured with your Qdrant Cloud URL, collection name, and the appropriateclusterCenterTypeandclusterThresholdCenterType(e.g.,is_medoidandis_medoid_cluster_threshold). - Anomaly Determination: The
Compare scoresnode analyzes the similarity scores from Qdrant. If the input image scores lower than all defined thresholds, it's flagged as an anomaly; otherwise, it's identified as similar to a known crop. - Review Output: The final output is a text message indicating the detection result.
Apps Used
Workflow JSON
{
"id": "7d3ac123-f7dc-4a7f-9d2f-05182664c633",
"name": "Automated Crop Anomaly Detection: Identify Unknowns Instantly",
"nodes": 29,
"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.
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ID: 7d3ac123-f7dc...
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