Set up medoids for anomaly detection in crops dataset

• Identify anomalies in crop health data using clustering techniques.
• Monitor agricultural datasets for unusual patterns and outliers.
• Enhance decision-making in crop management through data analysis.

Initiates with a manual trigger to start the workflow. Fetches total points in the crop collection from Qdrant. Calculates a distance matrix to identify cluster centers. Determines medoids using sparse matrix calculations. Sets medoid IDs and threshold scores for anomaly detection.

1. 1
   Import the workflow template into your n8n environment.
2. 2
   Configure the Qdrant cluster variables with your credentials.
3. 3
   Run the workflow by clicking the 'Test workflow' button.