Medicinal Herbs Identification

IEEE BASE PAPER TITLE:

Medicinal Herbs Identification

(or)

OUR PROPOSED PROJECT TITLE:

An AI based approach for Advancing Medicinal Plant Identification using Deep Learning

IEEE BASE PAPER ABSTRACT:

Many rural communities have a strong belief in plant diversity. They collect useful plants and herbs and use them with indigenous knowledge and customs. One such oldest system that results in the use of medicinal herbs is Ayurveda. Approximately 10,000 plants are used medicinally in India, but not all plants are included in the official Ayurvedic Pharmacopoeia. Before becoming part of Ayurvedic medicine, all plants need to be thoroughly studied. For this reason, identifying herbs is the most important step. Many of these identifications are fully supported by human perception, leaving room for error and misjudgement. Therefore, it is necessary to develop an efficient system using computer vision, pattern recognition, and image processing algorithms alongside the availability of various combinations of feature detection methods with different classifiers that are often utilized in building an automatic identification system for herbal leaves using leaf images and reveal its associated information to realize knowledge.

PROJECT OUTPUT VIDEO:

Algorithm / Model Used:

Xception Architecture.

OUR PROPOSED ABSTRACT:

In recent years, there has been a growing interest in the identification and classification of medicinal plants due to their potential health benefits. This project presents an innovative AI-based approach for advancing medicinal plant identification using deep learning techniques, specifically employing the Xception architecture. Developed using Python, our model achieves remarkable training accuracy of 93.34% and validation accuracy of 96.79%.

To train and evaluate the model, we utilized the VNPlant-200 dataset, consisting of a comprehensive collection of 17,973 images of medicinal plants distributed among 200 distinct categories. This dataset encompasses a wide variety of plant species with diverse visual characteristics, enabling robust and accurate plant identification.

Through a meticulous training process, the Xception-based model learns intricate patterns and features within the images, enabling it to effectively distinguish between different medicinal plant species. Leveraging the power of deep learning, our approach significantly enhances the accuracy and efficiency of medicinal plant identification. Additionally, hyperparameter tuning and fine-tuning of the Xception architecture were performed to optimize the model’s performance and achieve exceptional accuracy.

The results obtained demonstrate the efficacy of our AI-based approach for medicinal plant identification. The high training and validation accuracies validate the model’s capability to accurately recognize and categorize medicinal plant species. This project contributes to the advancement of automated identification systems in the field of herbal medicine, enabling researchers, botanists, and healthcare professionals to rapidly and reliably identify medicinal plants for various purposes.

Overall, this project showcases the potential of AI and deep learning techniques, specifically the Xception architecture, in advancing medicinal plant identification. The successful application of our approach on the VNPlant-200 dataset opens up new possibilities for further research and development in this domain, fostering advancements in herbal medicine and botanical studies.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor.
• Hard Disk : 500 GB.
• Monitor : 15’’ LED
• Input Devices : Keyboard, Mouse
• Ram : 6 GB

SOFTWARE REQUIREMENTS: 

• Operating system : Windows 10 / 11.
• Coding Language : Python 3.8.
• Web Framework : Flask.
• Frontend : HTML, CSS, JavaScript.

REFERENCE:

Preethi; S. Jansi Rani; K. S. Pradhiksha; J. Ram Kumar; T. Vishal, “Medicinal Herbs Identification”, 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE Conference, 2023.

👉CLICK HERE TO BUY THIS PROJECT SOURCE CODE👈