Automate Crop Anomaly Detection with AI
detail.loadingPreview
Instantly identify anomalous crops in your dataset. This n8n workflow leverages advanced AI models to analyze image URLs, compare them against a known crop database, and alert you to any unusual findings. Save time and resources by automating your crop quality control.
About This Workflow
This n8n workflow, '[3/3] Anomaly detection tool (crops dataset)', empowers users to automatically detect anomalies within agricultural crop images. By accepting an image URL, it generates embedding vectors using the Voyage.ai Multimodal Embeddings API. These vectors are then queried against a Qdrant vector database containing embeddings of known crop types. The workflow compares the input image's similarity to existing crop classifications and analyzes it against pre-defined threshold scores for each crop. If an image's score falls below all established thresholds, it is flagged as a potential anomaly, alerting users to new or unusual crop imagery within their dataset.
Key Features
- Automated Anomaly Detection: Effortlessly identify outlier images in your crop dataset.
- AI-Powered Image Analysis: Utilizes advanced multimodal embeddings for accurate image understanding.
- Seamless Integration: Connects Voyage.ai embeddings with Qdrant vector database for robust analysis.
- Configurable Thresholds: Customize detection sensitivity based on your specific needs.
- Clear Alerting System: Receive immediate notifications for potential crop anomalies.
How To Use
- Input Image URL: Provide the URL of the image you want to analyze via the workflow trigger (not explicitly shown but implied by the 'Execute Workflow Trigger' mention).
- Voyage.ai Embedding: The 'Embed image' node (using
n8n-nodes-base.httpRequest) sends the image URL to the Voyage.ai API to generate its embedding vector. - Qdrant Query: The 'Get similarity of medoids' node (using
n8n-nodes-base.httpRequest) queries your Qdrant collection (agricultural-crops) using the generated embedding. It retrieves the most similar known crops and their associated payload data. - Variable Configuration: Ensure the 'Variables for medoids' and 'Info About Crop Labeled Clusters' nodes are correctly configured with your Qdrant cloud URL, collection name, and cluster threshold types.
- Anomaly Comparison: The 'Compare scores' node (using
n8n-nodes-base.code) analyzes the similarity scores from Qdrant against the definedis_medoid_cluster_thresholdfor each crop. - Receive Result: Based on the comparison, the workflow outputs a message indicating whether the image is similar to a known crop or if it's a potential anomaly.
Apps Used
Workflow JSON
{
"id": "75d829ff-fab0-44b6-847e-211c6ee135b9",
"name": "Automate Crop Anomaly Detection with AI",
"nodes": 11,
"category": "Operations",
"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: 75d829ff-fab0...
About the Author
SaaS_Connector
Integration Guru
Connecting CRM, Notion, and Slack to automate your life.
Statistics
Related Workflows
Discover more workflows you might like
Universal CSV to JSON API Converter
Effortlessly transform CSV data into structured JSON with this versatile n8n workflow. Integrate it into any application as a custom API endpoint, supporting various input methods including file uploads and raw text.
Instant WooCommerce Order Notifications via Telegram
When a new order is placed on your WooCommerce store, instantly receive detailed notifications directly to your Telegram chat. Stay on top of your e-commerce operations with real-time alerts, including order specifics and a direct link to view the order.
On-Demand Microsoft SQL Query Execution
This workflow allows you to manually trigger and execute any SQL query against your Microsoft SQL Server database. Perfect for ad-hoc data lookups, administrative tasks, or quick tests, giving you direct control over your database operations.