Developer & DevOps

Prepare dual-type media for anomaly detection in crop datasets

48 nodes 284 165 Automatic trigger
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Workflow Description

Advanced automation that receives crop data via manual trigger or HTTP request, processes it through custom scripts, splits it into two media types optimized for analysis, and detects anomalies and outliers in your agricultural dataset through intelligent data transformation and validation.

How it works

  1. 1.Receive raw crop data through manual trigger or direct HTTP endpoint call
  2. 2.Process incoming data using custom JavaScript or Python scripts
  3. 3.Split media into two distinct types based on predefined criteria
  4. 4.Merge and format results for downstream anomaly detection analysis

Use cases

  • Prepare agricultural datasets before anomaly detection analysis
  • Convert multi-format media into standardized output structure
  • Automate quality validation of crop monitoring data

Requirements

  • Proficiency with programmatic data processing logic
  • Access to HTTP endpoints for external data sources
  • Understanding of media type classification and differentiation criteria

Service Value

Ready-made workflow template for automation delivery and service execution.

Apps Used

Manual Trigger HTTP Request Code Set Split Out Merge Note

Details

Trigger Automatic trigger
Nodes 48
Apps 7
Views 284
Downloads 165

How to Use

  1. 1.Click "Download Template"
  2. 2.Open your n8n dashboard
  3. 3.Go to Workflows > Import from File
  4. 4.Select downloaded file and configure credentials

Nodes Used (48)

/

When clicking ‘Test workflow’

Manual Trigger

#1

Total Points in Collection

HTTP Request

#2

Cluster Distance Matrix

HTTP Request

#3

Scipy Sparse Matrix

Code

#4

Set medoid id

HTTP Request

#5

Get Medoid Vector

HTTP Request

#6

Prepare for Searching Threshold

Set

#7

Searching Score

HTTP Request

#8

Threshold Score

Set

#9

Set medoid threshold score

HTTP Request

#10

Split Out1

Split Out

#11

Merge

Merge

#12

Textual (visual) crop descriptions

Set

#13

Embed text

HTTP Request

#14

Get Medoid by Text

HTTP Request

#15

Set text medoid id

HTTP Request

#16

Prepare for Searching Threshold1

Set

#17

Threshold Score1

Set

#18

Searching Text Medoid Score

HTTP Request

#19

Medoids Variables

Set

#20

Text Medoids Variables

Set

#21

Qdrant cluster variables

Set

#22

Info About Crop Clusters

Set

#23

Crop Counts

HTTP Request

#24

Sticky Note

Sticky Note

#25

Sticky Note1

Sticky Note

#26

Sticky Note2

Sticky Note

#27

Sticky Note3

Sticky Note

#28

Sticky Note4

Sticky Note

#29

Sticky Note5

Sticky Note

#30

Sticky Note6

Sticky Note

#31

Sticky Note8

Sticky Note

#32

Sticky Note9

Sticky Note

#33

Split Out

Split Out

#34

Sticky Note10

Sticky Note

#35

Sticky Note11

Sticky Note

#36

Sticky Note12

Sticky Note

#37

Sticky Note13

Sticky Note

#38

Sticky Note14

Sticky Note

#39

Set text medoid threshold score

HTTP Request

#40

Sticky Note15

Sticky Note

#41

Sticky Note16

Sticky Note

#42

Sticky Note17

Sticky Note

#43

Sticky Note18

Sticky Note

#44

Sticky Note19

Sticky Note

#45

Sticky Note20

Sticky Note

#46

Sticky Note21

Sticky Note

#47

Sticky Note22

Sticky Note

#48

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