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This is a public Edge Impulse project, use the navigation bar to see all data and models in this project; or clone to retrain or deploy to any edge device.
Plants 33 Output
About this project
Project uses the Plant Village Dataset which can be found on github. Was trained with EfficientNetLite and then compressed into a much smaller model size. 33 outputs.
For more details see kaggle:
- https://www.kaggle.com/code/timothylovett/plant-disease-shrunken-efficientnet
- https://www.kaggle.com/code/timothylovett/plant-disease-shrunken-tflite-quantization
Former shows testing the shrunken model while latter shows quantization process. Given the model used relu6 for its activations (EfficientNetLite) vs swish (like EfficientNet) it quantized with minimal loss in accuracy.
@article{Mohanty_Hughes_Salathé_2016, title={Using deep learning for image-based plant disease detection}, volume={7}, DOI={10.3389/fpls.2016.01419}, journal={Frontiers in Plant Science}, author={Mohanty, Sharada P. and Hughes, David P. and Salathé, Marcel}, year={2016}, month={Sep}}
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Project info
| Project ID | 782690 |
| Project version | 3 |
| License | 3-Clause BSD |
| No. of views | 41,771 |
| No. of clones | 2 |
