Abstract
The main aim of the project IDENTIFICATION AND CLASSIFICATION OF MEDICINAL PLANTS is to use image processing techniques for the classification of medicinal plants found in Sikkim. Plants play a vital role in human life, they provide oxygen, food, shelter, medicine, and environmental protection. Many plants are rich in medicinal value and contain active ingredients for medicinal use. Manual identification of medicinal plants is a time-consuming process and needs the help of experts for plant identification.
To overcome this problem, automatic identification and classification of medicinal plants are needed for greater benefit to humankind. In today’s era, the automatic identification and classification of medicinal plants is an active
research area in the field of image processing. Feature extraction and classification are the main steps in the identification of medicinal plants and the classification process which affect the overall accuracy of the classification
system.
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Abstract: Identification and Classification of Medicinal Plants
Existing System:
The current methods for identifying and classifying medicinal plants heavily rely on manual expertise, leading to inefficiencies and inaccuracies. Traditional systems lack the ability to handle large datasets and may not provide real-time results. This project addresses these shortcomings by proposing an advanced system that leverages the power of artificial intelligence and computer vision.
Proposed System:
The proposed system employs image recognition techniques and machine learning algorithms to identify and classify medicinal plants accurately. Users can capture images of plant specimens through a dedicated mobile application or upload images to a web interface. The system processes these images, extracts relevant features, and employs a trained model for plant species identification and classification.
System Requirements:
- High-resolution cameras for image capture
- Sufficient storage for a diverse dataset of plant images
- Internet connectivity for regular updates and model training
Algorithms:
The project utilizes deep learning algorithms, such as Convolutional Neural Networks (CNNs), for image recognition and classification. Transfer learning techniques are employed to adapt pre-trained models to the specific domain of medicinal plants.
Hardware and Software Requirements:
- Hardware: A machine with decent processing power and GPU for model training
- Software: Python programming language, TensorFlow or PyTorch for deep learning, Flask for web interface, and a database system for storing plant information
Architecture:
The system architecture consists of three main components:
- Image Processing Module: Responsible for pre-processing images and extracting relevant features.
- Machine Learning Module: Utilizes trained models for plant identification and classification.
- Web Interface: Allows users to interact with the system, upload images, and receive results.
Technologies Used:
- Python for overall system development
- TensorFlow or PyTorch for implementing deep learning models
- Flask for building the web interface
- Database system (e.g., MySQL or MongoDB) for storing plant information
Web User Interface:
The web interface provides an intuitive platform for users to interact with the system. It includes features such as image uploading, result display, and additional information about identified plants. The interface is designed to be user-friendly, making it accessible to a wide range of users, including botanists, researchers, and enthusiasts.
In conclusion, this project aims to revolutionize the identification and classification of medicinal plants by integrating cutting-edge technologies. The proposed system offers a more efficient and accurate solution, contributing to the field of plant sciences and facilitating research in herbal medicine.
Introduction
The main aim of the project is to use image processing techniques for the Classification of medicinal plants. Ayurveda is the ancient Indian system of healing using medicinal plants available naturally in the Indian subcontinent, also
called the mother of healing arts. According to World Health Organization (WHO), 65% to 80% of the world population currently use medicinal plants as remedies for various diseases. Because of environmental factors and a lack of awareness about medicinal plants in human beings, plants are becoming extinct and rare. Sikkim is a very small hilly state in the Eastern Himalayas with a total geographical area of 7096. Sq. km. The state is bestowed with abundant natural resources.
India has two out of the 18 Biodiversity Hotspots in the world, which is the Western Ghats and Eastern Himalayas. Sikkim, covering just 0.2% of the geographical area of the country, harbours more than 26% of flowering plants have
tremendous biodiversity, and has been identified as one of the most important geographical HOT SPOTS in the Eastern Himalayas. At glances Orchids-410, Rhgododendrons-36, Bamboos-26, ferns and Ferns allies-326, Tree ferns-8, Primulas30, Oaks-11, and approx. 242 medicinal plants are found here. Sikkim Himalayas is bestowed with abundant medicinal plants, herbs, shrubs, bamboo, and medicinal plants. More than 242 species of medicinal plants are reported to be found in the state.
The local inhabitants for the treatment of various ailments use numerous herbal remedies furthermore modern medicines owe to the flora of these mountains. Many inhabitants for the treatment of various ailments use
numerous herbal remedies. Further, more modern medicines owe to the flora of these mountains. The main aim of the project is to use image processing techniques for the Classification of medicinal plants found in Sikkim. The northeastern region of the Himalayas is more luxurious in vegetation and thus is sometimes referred to as the “Cradle of Medicinal Plants”. The region exhibits more diversity in its types of plants than perhaps any other region in the Indian subcontinent, and it is considered as the origin of a large number of Medicinal plants.
Medicinal plants always played an important role in the maintenance of health, well-being, and everyday life of a population worldwide. Throughout the centuries, plant leaves, stems, flowers, seeds, berries, and roots were used for healing and maintenance of different pathological conditions, as well as in beauty formulas, massage applications, foods preparations and beverages. This book, which is based on a scientific findings and original research, represent a comprehensive and up to date introduction to medicinal plants from all over the world, describes their huge economic, and therapeutic potential, and analysing different aspects of their nontoxicity, and importance for human health and homeostasis
CLASSIFICATION AND IDENTIFICATION ON MEDICINAL PLANTS project aims to gather this invaluable knowledge and train a machine learning model so as to be able to classify and identify plants.
Feasibility Study
The feasibility study is an evaluation of the viability of a given project or system and takes into account technical, operational, economical and schedule considerations to determine the possibility of completing the project. In the process of a feasibility study, many aspects and constraints are considered and these are listed below:
- Technical Feasibility:
This project is technically feasible as uses open-source software. This project requires Python as the programming language and datasets which will be used to train the model. Since Python is the most user-friendly and 3 efficient programming language to handle large-scale data, therefore the
proposed work is technically feasible. - Operational Feasibility:
This project is supposed to provide a user-friendly interface and will be easy to operate and maintain. The project will not have much maintenance and will be easy to operate although updating datasets would be required; thus, allowing it operational feasible. - Economic Feasibility:
This project is economic with respect to use within any organization. Economic feasibility is the most important and frequently used method for evaluating the effectiveness of the proposed system. It is economically feasible as it is developed using the tools which are available for free and can run on any device thus reducing the cost
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