AgriCare is a comprehensive solution for diagnosing crop diseases using AI, providing a user-friendly iOS application to capture and analyze leaf images. The system comprises three main components: an iOS app, an API, and a deep learning AI model.
AgriCare.mov
AgriCare: Crop Disease Diagnosis is your go-to app for maintaining healthy crops. Whether you're a professional farmer or a home gardener, our app helps you protect your valuable crops from diseases with ease and efficiency.
- AI-Powered Disease Detection: Simply take a photo of the affected part of your crop, and our advanced AI algorithms will analyze the image to identify the disease.
- Comprehensive Crop Support: Support for a wide range of crops including Apple, Blueberry, Cherry, Corn, Grape, Orange, Peach, Pepper, Potato, Raspberry, Squash, Strawberry, and Tomato.
- Instant Diagnosis: Get quick and accurate results to make timely decisions and prevent the spread of diseases.
- Expert Remedies: Receive tailored remedies and treatment options for each identified disease to ensure effective management and recovery of your crops.
- User-Friendly Interface: Designed for ease of use, AgriCare makes disease diagnosis straightforward and accessible for everyone.
The AgriCare system is composed of three main parts:
-
iOS App (SwiftUI): Developed with SwiftUI, the iOS app provides users with an easy-to-use interface for scanning and diagnosing plant diseases from leaf images.
- Directory:
iOS/AgriCare
- Directory:
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API (Flask): The API acts as the intermediary between the iOS app and the deep learning model. It handles image uploads and returns disease diagnosis results.
- Directory:
API
- Directory:
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Deep Learning AI Model: The AI model is based on the InceptionV3 architecture, achieving 98.5% accuracy in diagnosing crop diseases from leaf images.
- Directory:
AI Model
- Directory:
- Xcode (for iOS app development)
- Python 3.x (for API and AI model)
- AWS account (for deploying the AI model on EC2)
- Virtual environment (recommended)
- Open the
AgriCare.xcodeproj
in Xcode. - Build and run the project on your iOS device or simulator.
- Navigate to the
API
directory. - Create a virtual environment and activate it:
python3 -m venv venv source venv/bin/activate
- Install the required packages:
pip install -r requirements.txt
- Run the Flask server:
python app.py
- Navigate to the
AI Model
directory. - Run the
PlantDiseaseClassifierFinal.ipynb
in a Jupyter Notebook.
- Launch an EC2 instance and set up your environment.
- Clone the repository and navigate to the API directory.
- Set up the virtual environment and install dependencies as described above.
- Deploy the Flask API to handle incoming requests and serve predictions from the AI model.
- Open the AgriCare app on your iOS device.
- Capture a clear photo of the affected crop leaf.
- Submit the photo for analysis.
- Receive an instant diagnosis with detailed information about the disease and expert recommendations for treatment.
Contributions are welcome! Please follow these steps to contribute:
- Fork the repository.
- Create a new branch:
git checkout -b feature-branch-name
- Make your changes and commit them:
git commit -m 'Add some feature'
- Push to the branch:
git push origin feature-branch-name
- Submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.