Kutluhan Aktar / Tree Disease Identifier Public

Kutluhan Aktar / Tree Disease Identifier

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Object detection

About this project

Project Description

This object detection (FOMO) model detects potential tree diseases (foliar and bark) by utilizing two labels:

  • leaf_rust_spot_blister
  • stem_bark_branch_mildew

After building my object detection (FOMO) model, I deployed my model as a Linux x86_64 application and uploaded the generated application to LattePanda 3 Delta 864 so as to run inferences.


Creating your first impulse (100% complete)

Acquire data

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Design an impulse

Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.


Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.

Download block output

Title Type Size
Image training data NPY file 199 windows
Image training labels JSON file 199 windows
Image testing data NPY file 10 windows
Image testing labels JSON file 10 windows
Object detection model TensorFlow Lite (float32) 82 KB
Object detection model TensorFlow Lite (int8 quantized) 56 KB
Object detection model TensorFlow SavedModel 187 KB
Object detection model Keras h5 model 88 KB

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Data collected
199 items

Project info

Project ID 139523
Project version 1
License No license attached