Plant Disease Detection Project

This is plant diease detection project and We have implemented deep learning model for that. This model could achive near about 99% accuracy trained over the 38 plant disease classes.

Resources:

Kaggle Data Source

Data Overview:

Modelling:

Neural Network Architecture:

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 128, 128, 32)      896       
_________________________________________________________________
activation_1 (Activation)    (None, 128, 128, 32)      0         
_________________________________________________________________
batch_normalization_1 (Batch (None, 128, 128, 32)      128       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 42, 42, 32)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 42, 42, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 42, 42, 64)        18496     
_________________________________________________________________
activation_2 (Activation)    (None, 42, 42, 64)        0         
_________________________________________________________________
batch_normalization_2 (Batch (None, 42, 42, 64)        256       
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 42, 42, 64)        36928     
_________________________________________________________________
activation_3 (Activation)    (None, 42, 42, 64)        0         
_________________________________________________________________
batch_normalization_3 (Batch (None, 42, 42, 64)        256       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 21, 21, 64)        0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 21, 21, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 21, 21, 128)       73856     
_________________________________________________________________
activation_4 (Activation)    (None, 21, 21, 128)       0         
_________________________________________________________________
batch_normalization_4 (Batch (None, 21, 21, 128)       512       
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 21, 21, 128)       147584    
_________________________________________________________________
activation_5 (Activation)    (None, 21, 21, 128)       0         
_________________________________________________________________
batch_normalization_5 (Batch (None, 21, 21, 128)       512       
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 10, 10, 128)       0         
_________________________________________________________________
dropout_3 (Dropout)          (None, 10, 10, 128)       0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 12800)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              13108224  
_________________________________________________________________
activation_6 (Activation)    (None, 1024)              0         
_________________________________________________________________
batch_normalization_6 (Batch (None, 1024)              4096      
_________________________________________________________________
dropout_4 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 38)                38950     
_________________________________________________________________
activation_7 (Activation)    (None, 38)                0         
=================================================================
Total params: 13,430,694
Trainable params: 13,427,814
Non-trainable params: 2,880
_________________________________________________________________

Project Organization

├── LICENSE
├── Makefile
├── README.md
├── docs
│   ├── Makefile
│   ├── commands.rst
│   ├── conf.py
│   ├── getting-started.rst
│   ├── image_overview.png
│   ├── index.rst
│   └── make.bat
├── index.md
├── models
├── notebooks
│   ├── Plant disease detection model - Final Model.ipynb
│   └── Plant disease identification -  Sample Model.ipynb
├── references
├── reports
│   └── figures
├── requirements.txt
├── setup.py
├── src
│   ├── __init__.py
│   ├── data
│   │   ├── __init__.py
│   │   └── make_dataset.py
│   ├── features
│   │   ├── __init__.py
│   │   └── build_features.py
│   ├── models
│   │   ├── __init__.py
│   │   ├── predict_model.py
│   │   └── train_model.py
│   └── visualization
│       ├── __init__.py
│       └── visualize.py
├── test_environment.py
└── tox.ini`