Integration of Traditional Knowledge and Modern Science: A Holistic Approach to Identify Medicinal Leaves for Curing Diseases

Aim:

          The aim of this project is to develop and implement a holistic methodology for identifying and evaluating medicinal leaves with the potential to treat various diseases.

Abstract:

          In the quest to identify effective medicinal leaves for disease treatment, this project leverages the power of artificial intelligence through a MobileNetV2-based model. MobileNetV2, known for its efficiency in image classification tasks, is employed to develop a robust AI system capable of accurately predicting and classifying various medicinal leaves. By utilizing deep learning techniques, this project aims to bridge the gap between traditional herbal knowledge and modern technology.

        The model is trained on a diverse dataset of leaf images, enhancing its ability to distinguish between different medicinal plants with high precision. The outcome is an advanced tool that not only streamlines the process of identifying therapeutic plants but also supports research and application in natural medicine, ultimately contributing to improved health outcomes and accessible herbal solutions.

 Existing System:

         Medicinal plants have long played a crucial role in human health, yet their identification and understanding of their medicinal properties remain challenging. Traditional methods of recognizing these plants often fall short due to the vast number of species and their similarities. Various machine learning algorithms, such as Random Forest, K Nearest Neighbours (KNN), and Support Vector Machine (SVM), have been employed to enhance plant recognition. These techniques have demonstrated varying levels of effectiveness, with some achieving notable results.

Problem Definition:

        Identifying medicinal plants and understanding their therapeutic properties is a crucial yet challenging task due to the vast number of plant species and their visual similarities. Traditional methods of plant identification are time-consuming, often requiring expert knowledge, and lack the accuracy needed for broader use in healthcare or research. While machine learning techniques such as Random Forest, KNN, and SVM have been applied to this problem, they have provided varying levels of success. Despite recent advancements, models like YOLOv7 have achieved only moderate accuracy (87%), leaving room for improvement.

 Proposed System:

     The proposed system leverages MobileNetV2, a cutting-edge deep learning model, to enhance the identification and classification of medicinal leaves. Unlike previous methods, which have shown varying levels of success, MobileNetV2 is selected for its superior balance between accuracy and computational efficiency. The system is designed to process high-resolution leaf images, providing precise predictions about their medicinal properties.

      This system aims to surpass previous method and  providing a more accurate and efficient solution for medicinal leaf identification, ultimately bridging the gap between traditional herbal knowledge and modern AI technology

 Advantage:

By utilizing the MobileNetV2 model, the system offers higher accuracy in identifying medicinal leaves compared to traditional methods and other machine learning models, leading to more reliable predictions.

MobileNetV2 is a lightweight model designed for fast image classification, ensuring quick predictions even with limited computational resources, making it suitable for real-time applications.

The integration of Flask, HTML, CSS, JavaScript, and Bootstrap allows for a responsive and intuitive web application, making it accessible for users without technical expertise