Medicinal Plant Identification with Image Processing and Machine Learning

Table of Contents

• Description
• Demo
• Motivation
• Features
• Installation
• Usage
• Contributing

Description

India is known for its vast diversity of medicinal plants, but their accurate identification is a significant challenge, leading to issues like adulteration and misidentification in the market. This project aims to address this problem by developing a software solution that can identify different medicinal plants and raw materials through image processing using various machine learning algorithms. The software will be a valuable tool for everyone involved in the supply chain of these raw materials, from wholesalers to distributors, helping ensure the authenticity and quality of medicinal products.

Motivation

The motivation for this project stems from the need to preserve the authenticity and quality of medicinal plants and raw materials in the Ayurvedic system. Adulteration and misidentification can lead to serious consequences for consumers and the traditional medicinal system as a whole. By using image processing and machine learning, we can create a reliable and efficient way to identify these valuable resources.

Features

• Image Processing: Utilizes image analysis techniques to process and extract relevant information from images of medicinal plants.
• Machine Learning Algorithms: Employs various machine learning algorithms to classify and identify different medicinal plants and raw materials.
• Supply Chain Integration: Provides a valuable tool for stakeholders in the supply chain, ensuring the authenticity of raw materials.
• Promoting Authenticity: Aids in the preservation and promotion of authentic Ayurvedic practices.

Installation

• Clone the repo
• In backend folder create a venv and install all dependencies using requirement.txt
• In frontend/sanjeevani folder install npm, yarn, then run yarn install.

Usage

• Go to backend/sanjeevani and run python manage.py runserver.
• Go to frontend/sanjeevani and run yarn dev.

Contributing

We welcome contributions from the open-source community. If you would like to contribute to this project, please follow these steps:

1. Fork the project.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your changes to your fork.
5. Create a pull request to the main repository.

We appreciate your contributions to making this project even better!