Kutluhan Aktar / Tree Disease Identifier
This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.
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
Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.
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.
Deploy
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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Summary
Data collected
199 itemsProject info
| Project ID | 139523 |
| Project version | 1 |
| License | No license attached |
