---
language:
  - en
tags:
  - image-classification
  - plant-disease
  - keras
  - tensorflow
  - computer-vision
  - agriculture
license: mit
datasets:
  - plant-disease-dataset
metrics:
  - accuracy
library_name: keras
pipeline_tag: image-classification
---

# Plant Disease Classification Model

This repository contains a pre-trained InceptionResNetV2 model for plant disease classification. The model can classify 38 different plant diseases and healthy conditions across various plant species.

## Model Information

- **Framework**: Keras/TensorFlow
- **Architecture**: InceptionResNetV2
- **Task**: Image Classification
- **Number of Classes**: 38
- **Input Shape**: (224, 224, 3) - RGB images
- **Output**: Probability distribution over 38 classes

## Supported Plant Species and Conditions

The model can classify the following plant species and their conditions:

### Apple
- Apple scab
- Black rot
- Cedar apple rust
- Healthy

### Blueberry
- Healthy

### Cherry (including sour)
- Powdery mildew
- Healthy

### Corn (maize)
- Cercospora leaf spot Gray leaf spot
- Common rust
- Northern Leaf Blight
- Healthy

### Grape
- Black rot
- Esca (Black Measles)
- Leaf blight (Isariopsis Leaf Spot)
- Healthy

### Orange
- Haunglongbing (Citrus greening)

### Peach
- Bacterial spot
- Healthy

### Pepper, bell
- Bacterial spot
- Healthy

### Potato
- Early blight
- Late blight
- Healthy

### Raspberry
- Healthy

### Soybean
- Healthy

### Squash
- Powdery mildew

### Strawberry
- Leaf scorch
- Healthy

### Tomato
- Bacterial spot
- Early blight
- Late blight
- Leaf Mold
- Septoria leaf spot
- Spider mites Two-spotted spider mite
- Target Spot
- Tomato Yellow Leaf Curl Virus
- Tomato mosaic virus
- Healthy

## Usage

### Loading the Model

```python
import tensorflow as tf
from huggingface_hub import hf_hub_download

# Download the model from Hugging Face
model_path = hf_hub_download(
    repo_id="kero2111/Plant_Disease",
    filename="Pretrained_model.h5"
)

# Load the model
model = tf.keras.models.load_model(model_path)
```

### Making Predictions

```python
import numpy as np
from PIL import Image

# Load and preprocess image
def preprocess_image(image_path):
    img = Image.open(image_path)
    img = img.resize((224, 224))
    img = np.array(img) / 255.0
    img = np.expand_dims(img, axis=0)
    return img

# Make prediction
image = preprocess_image("path_to_your_image.jpg")
prediction = model.predict(image)
predicted_class = np.argmax(prediction[0])
confidence = prediction[0][predicted_class]

# Get class name
classes = [
    "Apple___Apple_scab", "Apple___Black_rot", "Apple___Cedar_apple_rust", "Apple___healthy",
    "Blueberry___healthy", "Cherry_(including_sour)___Powdery_mildew", "Cherry_(including_sour)___healthy",
    "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot", "Corn_(maize)___Common_rust_",
    "Corn_(maize)___Northern_Leaf_Blight", "Corn_(maize)___healthy", "Grape___Black_rot",
    "Grape___Esca_(Black_Measles)", "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)", "Grape___healthy",
    "Orange___Haunglongbing_(Citrus_greening)", "Peach___Bacterial_spot", "Peach___healthy",
    "Pepper,_bell___Bacterial_spot", "Pepper,_bell___healthy", "Potato___Early_blight",
    "Potato___Late_blight", "Potato___healthy", "Raspberry___healthy", "Soybean___healthy",
    "Squash___Powdery_mildew", "Strawberry___Leaf_scorch", "Strawberry___healthy",
    "Tomato___Bacterial_spot", "Tomato___Early_blight", "Tomato___Late_blight", "Tomato___Leaf_Mold",
    "Tomato___Septoria_leaf_spot", "Tomato___Spider_mites Two-spotted_spider_mite",
    "Tomato___Target_Spot", "Tomato___Tomato_Yellow_Leaf_Curl_Virus", "Tomato___Tomato_mosaic_virus",
    "Tomato___healthy"
]

print(f"Predicted: {classes[predicted_class]}")
print(f"Confidence: {confidence:.2%}")
```

## Model Performance

The model has been trained on a comprehensive dataset of plant disease images and can accurately classify various plant diseases and healthy conditions.

## Requirements

- TensorFlow 2.x
- NumPy
- PIL (Pillow)
- huggingface_hub

## License

This model is provided for research and educational purposes.

## Citation

If you use this model in your research, please cite the original dataset and model architecture used.