For more detail explanation and working of this please visit blog here
Here we have used the plant village dataset. The PlantVillage dataset consists of 61,486 healthy and unhealthy leaf images divided into 39 categories by species and disease.
Dataset can be found here
The classes uses in dataset are:
1. Apple_scab 2. Apple_black_rot 3. Apple_cedar_apple_rust 4. Apple_healthy 5. Background_without_leaves 6. Blueberry_healthy 7. Cherry_powdery_mildew 8. Cherry_healthy 9. Corn_gray_leaf_spot 10. Corn_common_rust 11. Corn_northern_leaf_blight 12. Corn_healthy 13. Grape_black_rot 14. Grape_black_measles 15. Grape_leaf_blight 16. Grape_healthy 17. Orange_haunglongbing 18. Peach_bacterial_spot 19. Peach_healthy 20. Pepper_bacterial_spot 21. Pepper_healthy 22. Potato_early_blight 23. Potato_healthy 24. Potato_late_blight 25. Raspberry_healthy 26. Soybean_healthy 27. Squash_powdery_mildew 28. Strawberry_healthy 29. Strawberry_leaf_scorch 30. Tomato_bacterial_spot 31. Tomato_early_blight 32. Tomato_healthy 33. Tomato_late_blight 34. Tomato_leaf_mold 35. Tomato_septoria_leaf_spot 36. Tomato_spider_mites_two-spotted_spider_mite 37. Tomato_target_spot 38. Tomato_mosaic_virus 39. Tomato_yellow_leaf_curl_virus
There are two versions of dataset one without augmentation and other with augmentation where augmentation is performed with 6 different techniques (flipping, Gamma correction, noise injection, PCA color augmentation, rotation, and Scaling).
Consider This file structure -plantdisease -dataset -input -Apple_black_rot.jpg -Apple_cedar_apple_rust.jpg -Apple_healthy.jpg -Apple_scab.jpg -Background_without_leaves.jpg -Blueberry_healthy.jpg -models -rn.h5 -src -dataset.py -train.py -predict.py -static -images -image_1.jpg -image_2.jpg -image_3.jpg -image_4.jpg -templates -index.html -result.html -upload -main.py -requirements.txt
To install all Dependency run pip install -r requirements.txt
First, we need to download the dataset and place it under the dataset folder. For downloading dataset run src/dataset.py file. To run dataset.py file execute python dataset.py.
Train ResNet152V2 model for 10 epochs with early stopping. To run this file, execute python train.py command.
Output of training is given below
Epoch 1/10
3074/3074 [==============================] - 794s 258ms/step - loss: 0.7289 - accuracy: 0.7813 - val_loss: 0.5274 - val_accuracy: 0.8464
Epoch 2/10
3074/3074 [==============================] - 791s 257ms/step - loss: 0.2194 - accuracy: 0.9288 - val_loss: 0.2383 - val_accuracy: 0.9264
Epoch 3/10
3074/3074 [==============================] - 803s 261ms/step - loss: 0.1427 - accuracy: 0.9531 - val_loss: 0.1081 - val_accuracy: 0.9674
Epoch 4/10
3074/3074 [==============================] - 803s 261ms/step - loss: 0.1065 - accuracy: 0.9653 - val_loss: 0.1219 - val_accuracy: 0.9585
Epoch 5/10
3074/3074 [==============================] - 799s 260ms/step - loss: 0.0835 - accuracy: 0.9730 - val_loss: 0.1150 - val_accuracy: 0.9653
Epoch 6/10
3074/3074 [==============================] - ETA: 0s - loss: 0.0670 - accuracy: 0.9778
Reached 97% accuracy so cancelling training!
3074/3074 [==============================] - 793s 258ms/step - loss: 0.0670 - accuracy: 0.9778 - val_loss: 0.0773 - val_accuracy: 0.9769
Test the model by uploading a single picture and predicting its class. To run this file, execute python train.py command.
Make a call to function build with image path if a call is made externally. To run this file execute python predict.py command.
To run this, file execute python main.py command. Congratulations, you have made a plant disease classification flask app.