[3/3] Anomaly detection tool (crops dataset)

We are working here with crops dataset: Existing (so not anomalies) crops images in dataset are: - 'pearlmillet(bajra)', - 'tobacco-plant', - 'cherry', - 'cotton', - 'banana', - 'cucumber', - 'maize', - 'wheat', - 'clove', - 'jowar', - 'olive-tree', - 'soyabean', - 'coffee-plant', - 'rice', - 'lemon', - 'mustard-oil', - 'vigna-radiati(mung)', - 'coconut', - 'gram', - 'pineapple', - 'sugarcane', - 'sunflower', - 'chilli', - 'foxnut(makhana)', - 'jute', - 'papaya', - 'tea', - 'cardamom', - 'almond

Integrations Used (2)

HTTP Request

Executeworkflow

Workflow Structure (9 nodes)

100%

Execute Workflow Trigger

Set

Image URL hardcode

Set

Variables for medoids

Http Request

Total Points in Collection

Http Request

Each Crop Counts

Set

Info About Crop Labeled Clusters

Http Request

Embed image

Http Request

Get similarity of medoids

Code

Compare scores

Node Types:

http

code

transform

trigger

Requires credentials

Prerequisites

Required Credentials (2)

HTTP Header Auth

Used by: Embed image

Docs

Qdrant

Used by: Get similarity of medoids, Total Points in Collection +1 more

Docs

Complexity

Complex

Advanced features used. Requires n8n experience.

Setup time: ~35 min

Score: 44/100

Custom Code

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workflow.json

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      "createdAt": "2024-12-08T22:05:15.945Z",
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