Plant Disease Detection CNN

# CNN Model Architecture
import tensorflow as tf
from tensorflow.keras import layers, models

def create_plant_disease_model(input_shape=(224, 224, 3), num_classes=38):
    model = models.Sequential([
        # First Convolutional Block
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
        layers.BatchNormalization(),
        layers.MaxPooling2D((2, 2)),
        layers.Dropout(0.25),
        
        # Second Convolutional Block
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.BatchNormalization(),
        layers.MaxPooling2D((2, 2)),
        layers.Dropout(0.25),
        
        # Third Convolutional Block
        layers.Conv2D(128, (3, 3), activation='relu'),
        layers.BatchNormalization(),
        layers.MaxPooling2D((2, 2)),
        layers.Dropout(0.25),
        
        # Fourth Convolutional Block
        layers.Conv2D(256, (3, 3), activation='relu'),
        layers.BatchNormalization(),
        layers.MaxPooling2D((2, 2)),
        layers.Dropout(0.25),
        
        # Dense Layers
        layers.Flatten(),
        layers.Dense(512, activation='relu'),
        layers.BatchNormalization(),
        layers.Dropout(0.5),
        layers.Dense(256, activation='relu'),
        layers.Dropout(0.3),
        layers.Dense(num_classes, activation='softmax')
    ])
    
    return model

# Compile model
model = create_plant_disease_model()
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

# Data preprocessing and augmentation
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Data augmentation for better generalization
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)

validation_datagen = ImageDataGenerator(rescale=1./255)

# Training configuration
BATCH_SIZE = 32
EPOCHS = 50
IMG_HEIGHT = 224
IMG_WIDTH = 224

# Training with callbacks
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau

callbacks = [
    EarlyStopping(patience=10, restore_best_weights=True),
    ReduceLROnPlateau(factor=0.5, patience=5, min_lr=1e-7)
]

# Train the model
history = model.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // BATCH_SIZE,
    epochs=EPOCHS,
    validation_data=validation_generator,
    validation_steps=validation_generator.samples // BATCH_SIZE,
    callbacks=callbacks
)

# Mobile deployment with TensorFlow Lite
import tensorflow as tf

# Convert to TensorFlow Lite
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

# Save the model
with open('plant_disease_model.tflite', 'wb') as f:
    f.write(tflite_model)

# Web deployment with TensorFlow.js
import tensorflowjs as tfjs

tfjs.converters.save_keras_model(model, 'web_model/')

# Docker deployment
"""
FROM tensorflow/tensorflow:latest-gpu
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . /app
WORKDIR /app
EXPOSE 8000
CMD ["python", "app.py"]
"""