Kutluhan Aktar / AI-driven Fertilizer Contamination Detector
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Project Description
This object detection (FOMO) model detects chemical fertilizer contamination levels based on the soil integrity and structure altered by the applied organic fertilizers:
- Enriched
- Toxic
- Unsafe
After building my object detection model, I deployed my model as a supported SenseCAP A1101 firmware (UF2) and uploaded it to SenseCAP A1101. Also, I employed LattePanda 3 Delta to program SenseCAP A1101 to capture images and send the latest model detection results to a Twilio-verified phone number over WhatsApp via the Helium LongFi Network.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Image training data | NPY file | 90 windows | |
| Image training labels | JSON file | 90 windows | |
| Image testing data | NPY file | 15 windows | |
| Image testing labels | JSON file | 15 windows | |
| Object detection model | TensorFlow Lite (float32) | 82 KB | |
| Object detection model | TensorFlow Lite (int8 quantized) | 55 KB | |
| Object detection model | TensorFlow SavedModel | 186 KB | |
| Object detection model | Keras h5 model | 88 KB |
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Summary
Data collected
105 itemsProject info
| Project ID | 233660 |
| Project version | 1 |
| License | Apache 2.0 |
