Crop Anomaly Detection Tool

Target Audience

• Agricultural Researchers: Individuals studying crop varieties and their anomalies.
• Farmers and Agriculturalists: Those who want to monitor their crops for any unusual characteristics.
• Data Scientists: Professionals interested in applying machine learning techniques to agricultural datasets.
• Developers: Individuals looking to integrate anomaly detection into agricultural applications.

Problem Solved

This workflow addresses the challenge of identifying anomalous crops in a dataset by comparing input images against a collection of known crop images. It automates the detection process, providing a text message indicating whether the input image depicts a known crop or an anomaly, thus enhancing crop management and monitoring.

Workflow Steps

1. Execute Workflow Trigger: The workflow is initiated manually by providing an image URL.
2. Image URL Hardcode: The provided image URL is stored for further processing.
3. Variables for Medoids: Essential parameters for accessing the Qdrant collection are set, including the Qdrant Cloud URL and collection name.
4. Total Points in Collection: Retrieves the total number of crop images stored in the Qdrant collection.
5. Each Crop Counts: Counts how many different crop classes are present in the collection.
6. Embed Image: The input image is embedded using the Voyage AI API to create a vector representation.
7. Get Similarity of Medoids: The embedded image is compared to the stored crop images in Qdrant to find similar classes based on predefined thresholds.
8. Compare Scores: The scores of the crops are analyzed. If the input image scores below the thresholds for all known crops, it is flagged as an anomaly.
9. Output Result: A message is returned indicating whether the input image is similar to existing crops or if it represents an unknown anomaly.