The project aims to develop a machine learning model for the identification and classification of medicinal plants from images and create a user-friendly web application to provide valuable information about these plants.
India, with a rich heritage of floral diversity, is well-known for its medicinal plant wealth, but their identification is one of the major burning issues in Ayurvedic Pharmaceutics. Several crude drugs are being sold under the same name in the market leading to confusion and their misidentification. Even the collectors and traders are not completely aware of the exact morphological appearance or differentiating attributes of the many drugs owing to seasonal and geographical availability, and similar characteristics. Moreover, the extensive consumption to meet demand-supply ratio exerts a heavy strain on the existing resources. It further leads to the practice of adulteration, substitution, and disbelief in the curative capability of the system eventually.This project addresses the need for a reliable tool to identify and learn about medicinal plants.
Gather a diverse dataset of images of medicinal plants. The dataset should include various species of medicinal plant.
Train a machine learning model (e.g., convolutional neural network) using the collected dataset. The model's primary task is to classify images of medicinal plants into their respective species or categories.
Create a web application that allows users to upload images of medicinal plants or enter descriptions. The application should offer the following features:
- Plant Identification: The ML model should classify the uploaded images and provide information about the identified plant, including its scientific name, common name, medicinal properties, and uses.
- Plant Information: Include a database of medicinal plants with detailed information, including images, descriptions Users can browse this database.
- Search and Filter: Implement search and filter options to help users find specific plants or information quickly.
- User Interaction: Allow users to submit feedback, ask questions, or contribute their own plant observations.
- Compatibility: Ensure that the web application is responsive and accessible on desktop.
Design an intuitive and user-friendly interface for the web application. Focus on ease of use, accessibility, and a visually appealing layout.
Thoroughly test the machine learning model and web application to ensure accuracy, reliability, and performance. Collect user feedback and make improvements accordingly.
Provide educational resources on the web application to help users learn about medicinal plants, their uses, and the importance of conservation.
This project not only serves as a valuable tool for plant enthusiasts, herbalists, and researchers but also contributes to the conservation and sustainable use of medicinal plants.
