AI-Driven Crop Disease Detection This project aims to leverage artificial intelligence to identify and classify crop diseases from images, helping farmers take timely preventive measures and improve agricultural outcomes.
🚀 Features Automated Disease Detection: Classifies crop diseases from uploaded images with high accuracy. User-Friendly Interface: Simple, intuitive interface for farmers and researchers. Scalable Architecture: Designed to handle diverse datasets for global adaptability. Insights and Recommendations: Provides actionable advice for disease management.
🛠️ Tech Stack Frontend: Python(streamlit), HTML5, CSS3 Backend: Python AI/ML: TensorFlow/Keras, Python Cloud Services: Streamlit cloud service
📊 Dataset The project uses a publicly(Kaggle.com) available dataset of crop images labeled with disease types. Preprocessing steps include resizing, augmentation, and normalization.
📁 Directory Structure
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├── main2.py / # user-interface-HTML/CSS
├── model.ipynb/ # Trained AI/ML models
├── dataset/ # Preprocessed image data
└── README.md # Project documentation
⚙️ Installation
Clone this repository:
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git clone https://github.com/yourusername/crop-disease-detection.git
Navigate to the project directory and install dependencies:
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cd frontend
npm install
cd ../backend
npm install
Run the application:
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🔬 Model Training Ensure all dependencies are installed: bash Copy code pip install -r requirements.txt
Train the model using the dataset:
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python train_model.py
Save the trained model in the models/ directory.
📈 Results Achieved an accuracy of XX% on the validation dataset. Supports X+ disease classes and healthy plant identification.
🤝 Contributing Contributions are welcome!
Fork the repository.
Create a feature branch:
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git checkout -b feature-name
Commit your changes and open a pull request.
📜 License This project is licensed under the MIT License.
📧 Contact For inquiries, please contact [[email protected],7658975169].