Automate Anomaly Detection in Crop Images Using Qdrant and Voyage AI

This workflow is part of a series designed to build robust data analysis tools for AI agents using vector databases. It specifically focuses on anomaly detection within a crop dataset stored in Qdrant. The workflow receives an image URL, generates embedding vectors via Voyage AI, and compares these vectors against a pre-defined set of crop classes in Qdrant. If the image does not match any known class based on threshold scores, it is flagged as an anomaly. This process is crucial for maintaining dataset integrity and identifying new or undefined crop types.