This project aims to assist farmers in detecting diseases in their crops using machine learning techniques. By leveraging image data of crops, the system can predict whether a crop is diseased or not, enabling early intervention and efficient agricultural management.
- Website link: plant-disease-detection-system.paul_ndalila
- Android app download link: Plant disease detection app
- Machine Learning Model: Trained using Jupyter Notebook, the model utilizes convolutional neural networks (CNNs) to classify images of crops into diseased or healthy categories.
- API Backend: Developed with FastAPI, the backend serves as the interface between the machine learning model and the frontend. It handles incoming requests, processes data, and returns predictions.
- Drag and Drop Frontend: Built with React JS, the frontend provides a user-friendly interface where users can upload images of crops, and receive instant feedback on their health status.
- API Integration: Axios is utilized for seamless integration between the frontend and backend, ensuring efficient communication and data exchange.
To run the system locally, follow these steps:
- Start the FastAPI Python server:
uvicorn app:app --reload- Start the React frontend:
npm run dev- Access the application via your browser.