CropLens

中文

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1. Project Overview

This project is an intelligent crop disease detection system that leverages the powerful YOLO (You Only Look Once) algorithm for real-time object detection. It is built with a modern, decoupled, full-stack architecture, assisted by the DeepSeek AI model for code generation and project insights. The system provides an end-to-end solution for identifying crop diseases from images, videos, and live camera feeds, aiming to offer an efficient and accurate tool for agricultural producers and researchers.

2. Core Features

• Multi-source Detection: Supports disease detection from static images, video files, and real-time camera streams.
• High-Performance Backend: A microservices architecture featuring a Flask server for handling AI model inferences and a Spring Boot server for business logic, data management, and user interactions.
• Modern Frontend: A responsive and user-friendly web interface built with Vue 3, Vite, and Element Plus.
• Real-time Communication: Utilizes WebSocket for instant feedback during video processing and ECharts for rich data visualization of detection results.
• Scalable & Decoupled: The clear separation of frontend, business logic, and AI services allows for independent development, scaling, and maintenance.

3. Technology Stack

• Frontend: Vue 3, Vite, Element Plus, Axios, ECharts, Socket.io-client
• Backend (Business Logic): Java 1.8, Spring Boot, MyBatis-Plus, MySQL/MariaDB, Maven
• Backend (AI Model): Python, Flask, Ultralytics (YOLO), OpenCV, Flask-SocketIO

4. Application Scenarios

• Smart Agriculture: Assists farmers in quickly identifying crop diseases for timely intervention.
• Agricultural Research: Provides researchers with a tool for automated data collection and analysis of plant pathology.
• Educational Tool: Serves as a comprehensive full-stack project for developers to learn about integrating AI models with web applications.

5. Installation and Quick Start

Prerequisites:

• Node.js >= 16.0
• Python >= 3.8
• Java >= 1.8
• Maven
• MySQL or MariaDB

Backend Setup (Spring Boot):

1. Navigate to the backend directory.
2. Create a database and import the database/schema.sql file.
3. Modify the database connection settings in src/main/resources/application.properties.
4. Run the application:

   mvn spring-boot:run

Backend Setup (Flask AI):

1. Navigate to the inference directory.
2. Install Python dependencies:

   pip install -r requirements.txt

   (Note: A requirements.txt file should be created with libraries like ultralytics, flask, opencv-python, requests, flask-socketio)
3. Download the pre-trained YOLO model weights (e.g., yolo11n.pt) and place them in the weights folder.
4. Run the AI service:

   python main.py

Frontend Setup (Vue):

1. Navigate to the frontend directory.
2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

4. Access the application at the address provided (e.g., http://localhost:3000).

6. Contribution

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue to report bugs or suggest new features.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

7. License

This project is licensed under the MIT License. See the LICENSE file for details.