Abstract
This project develops an automatic recognition system for medicinal plants using computer vision and machine learning techniques. By analyzing images of plants, the system aims to identify various species accurately and provide information about their medicinal properties. This technology can assist botanists, herbalists, and enthusiasts in identifying plants efficiently and correctly, thereby supporting the broader application of traditional medicine.
Introduction
Medicinal plants play a crucial role in traditional medicine systems worldwide. However, accurately identifying these plants is challenging and typically requires expert knowledge. This project introduces an automated system that uses image recognition technology to identify medicinal plants from photographs, aiming to bridge the gap between traditional botanical knowledge and modern technology.
Existing System
Current systems for plant recognition often rely on manual identification or basic mobile applications that are not specialized in medicinal plants. These systems might lack accuracy, especially under varying environmental conditions, and usually do not provide detailed information about the plant’s medicinal uses.
Proposed System
The proposed system utilizes a deep learning model trained on a dataset of medicinal plant images. It includes features for real-time image processing, a user-friendly interface for both mobile and desktop platforms, and a database that links plant species with detailed medicinal information.
Methodology
- Data Collection: Compile a comprehensive dataset of medicinal plant images, annotated with species names and medicinal properties.
- Preprocessing: Implement image preprocessing techniques to enhance model accuracy under diverse lighting and background conditions.
- Model Development: Develop a convolutional neural network (CNN) model tailored for high-accuracy plant recognition.
- Training and Validation: Train the model using the prepared dataset and validate it using separate test data to ensure robustness.
- Integration: Develop an application interface that allows users to upload images and receive instant feedback on plant identification and medicinal details.
- Deployment: Deploy the system on appropriate platforms for access by end-users.
- Feedback and Iteration: Implement a feedback mechanism to continuously improve the accuracy and database of the system based on user interactions.
Technologies Used
- Python: For backend development and model training.
- TensorFlow/Keras: For designing and training the deep learning model.
- OpenCV: For image processing tasks.
- Flutter/React Native: For developing cross-platform mobile applications.
- Firebase/MySQL: For database management and storing plant information.
- Heroku/AWS: For deploying the web application and backend services.
