Plant Disease Detection is a deep learning project aimed at helping farmers identify and manage crop diseases more effectively. The project utilizes convolutional neural networks (CNNs) to classify images of plant leaves into different disease categories, providing farmers with early detection and intervention strategies.
The project employs DenseNet, a popular deep learning architecture known for its dense connectivity pattern, which enhances feature propagation and encourages feature reuse. By leveraging pre-trained DenseNet models and fine-tuning them on our dataset, we achieve robust disease classification performance.
- Classification of plant diseases of 9 different types of crops including Apple Scab, Black Rot, Cedar Apple Rust, Powdery Mildew, Common Rust, Gray Leaf Spot, and more.
- User-friendly web application interface for uploading images and receiving real-time disease predictions.
- Detailed information about each disease, including symptoms, treatment, and preventive measures.
- Ability to select different plant types to customize disease detection for specific crops.
- Option to choose various crop types such as fruits, vegetables, and grains for targeted disease detection.
- Python
- TensorFlow/Keras
- Streamlit
- Clone the repository:
git clone https://github.com/yourusername/plant-disease-detection.git - Install the required dependencies:
pip install -r requirements.txt - Run the Streamlit web application:
streamlit run app.py
- Select the type of plant (e.g., Apple, Cherry, Corn) from the dropdown menu.
- Upload an image of a plant leaf affected by disease or select a healthy leaf image.
- View the disease prediction and detailed information about the identified disease.
- Take appropriate action based on the recommended treatment and preventive measures.
Contributions are welcome! If you have any suggestions, feature requests, or bug reports, please open an issue or submit a pull request.
- Special thanks to kaggle for providing the dataset used in this project.
- Inspired by similar projects in the field of agriculture and deep learning.
For inquiries or collaborations, please contact me at miteshgupta2711@gmail.com.