Prepare multiple media formats for crop anomaly detection
Workflow Description
Automation that receives crop dataset through manual trigger or HTTP request, processes two media format types, splits and merges data structures to prepare datasets for anomaly detection algorithms efficiently.
How it works
- 1.Receive dataset via manual trigger or HTTP request
- 2.Process and convert data into two different media format types using custom code
- 3.Split processed data by media type and merge for final output structure
Use cases
- Prepare images and video feeds from crop fields for disease detection models
- Process large datasets from agricultural monitoring systems before analysis
Requirements
- Data source accessible via HTTP or manual trigger
- Understanding of required media formats and technical specifications
Service Value
Ready-made workflow template for automation delivery and service execution.
Apps Used
Details
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (48)
When clicking ‘Test workflow’
Manual Trigger
Total Points in Collection
HTTP Request
Cluster Distance Matrix
HTTP Request
Scipy Sparse Matrix
Code
Set medoid id
HTTP Request
Get Medoid Vector
HTTP Request
Prepare for Searching Threshold
Set
Searching Score
HTTP Request
Threshold Score
Set
Set medoid threshold score
HTTP Request
Split Out1
Split Out
Merge
Merge
Textual (visual) crop descriptions
Set
Embed text
HTTP Request
Get Medoid by Text
HTTP Request
Set text medoid id
HTTP Request
Prepare for Searching Threshold1
Set
Threshold Score1
Set
Searching Text Medoid Score
HTTP Request
Medoids Variables
Set
Text Medoids Variables
Set
Qdrant cluster variables
Set
Info About Crop Clusters
Set
Crop Counts
HTTP Request
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Split Out
Split Out
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Set text medoid threshold score
HTTP Request
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