---
license: mit
tags:
- keras
- tensorflow
- image-classification
- agriculture
- crop-disease-detection
- mobilenetv2
- rwanda
base_model: mobilenetv2
---

# CropSense MobileNetV2 - Crop Disease Detection Model

## Model Description

This is a MobileNetV2-based deep learning model trained to detect crop diseases in leaf images. The model is specifically designed for smallholder farmers in Rwanda to identify common crop diseases.

**Model Type**: Image Classification  
**Framework**: Keras/TensorFlow  
**Architecture**: MobileNetV2  
**Input**: 224x224 RGB images  
**Output**: Disease classification (Healthy, Powdery, Rust)

## Model Details

- **Model Size**: ~14 MB
- **Classes**: 3 (Healthy, Powdery, Rust)
- **Input Shape**: (224, 224, 3)
- **Optimized for**: Mobile and edge devices

## Usage

### Using Python

```python
from huggingface_hub import hf_hub_download
import tensorflow as tf
from PIL import Image
import numpy as np

# Download model from Hugging Face
model_path = hf_hub_download(
    repo_id="Ruzindana/cropsense-mobilenetv2",
    filename="best_MobileNetV2.keras"
)

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

# Load class names
import json
with open(hf_hub_download(
    repo_id="Ruzindana/cropsense-mobilenetv2",
    filename="model_metadata.json"
)) as f:
    metadata = json.load(f)
    class_names = metadata.get("classes", ["Healthy", "Powdery", "Rust"])

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

# Make prediction
image = preprocess_image("path/to/leaf_image.jpg")
predictions = model.predict(image)
predicted_class_idx = np.argmax(predictions[0])
confidence = float(predictions[0][predicted_class_idx])
predicted_class = class_names[predicted_class_idx]

print(f"Prediction: {predicted_class} ({confidence*100:.2f}% confidence)")
```

### Using FastAPI Backend

Set the environment variable:
```bash
export HUGGINGFACE_MODEL_ID="Ruzindana/cropsense-mobilenetv2"
```

The backend will automatically download and use the model.

## Model Performance

- **Accuracy**: Optimized for mobile deployment
- **Inference Speed**: Fast inference on CPU and mobile devices
- **Use Case**: Real-time crop disease detection in field conditions

## Training Data

The model was trained on a dataset of crop leaf images from Rwanda, focusing on:
- Healthy crop leaves
- Powdery mildew infected leaves
- Rust disease infected leaves

## Limitations

- Trained specifically for certain crop types common in Rwanda
- Best results with clear, well-lit leaf images
- May require retraining for different geographic regions or crop varieties

## Citation

If you use this model, please cite:

```bibtex
@model{cropsense-mobilenetv2,
  author = {Ruzindana, Diana},
  title = {CropSense MobileNetV2 - Crop Disease Detection Model},
  year = {2025},
  url = {https://huggingface.co/Ruzindana/cropsense-mobilenetv2}
}
```

## License

MIT License - See LICENSE file for details

## Contact

For questions or support, please contact the model maintainer.