International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 08|Aug 2023
p-ISSN: 2395-0072
www.irjet.net
A Novel Machine Learning Approach for Medicinal Leaf Identification and its Beneficial Insights Mahadevi R S1, Asst. Prof. Savitha Patil2 1
Student, Dept. of Computer Science and Engineering, Sharnbasva University, Kalaburagi, Karnataka, India Asst.Professor,Dept. of Computer Science and Engineering, Sharnbasva University, Kalaburagi, Karnataka, India --------------------------------------------------------------------------***-------------------------------------------------------------------------2
Abstract The identification of medicinal plants' leaves is of paramount importance for both traditional and modern healthcare systems. As the demand for herbal remedies continues to grow, the need for precise and efficient leaf identification methods becomes increasingly significant. This study presents a comprehensive approach for medicinal leaf identification through the utilization of diverse machine learning algorithms. Subsequently, an advanced feature extraction process captures distinctive characteristics inherent to the leaf images. These extracted features serve as input for an array of machine learning algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, Multiple Linear Regression (MLR), Random Forest, Support Vector Machine (SVM), and Decision Tree. Through extensive experimentation on a diverse dataset of medicinal leaves representing a wide spectrum of species and variations, the performance of each machine learning algorithm is rigorously evaluated using metrics such as accuracy, precision, recall, and F1-score. Comparative analyses shed light on the unique strengths and limitations of each algorithm, guiding the selection of optimal approaches based on specific contextual requirements. The results underscore that our novel machine learning-based approach achieves remarkable accuracy and robustness in identifying medicinal leaves. Furthermore, the methodology unveils valuable insights into the distinct features that substantially contribute to accurate classification. This research not only advances the field of plant identification but also holds promising implications for the exploration of herbal remedies and their potential healthcare benefits. Keywords: Medicinal plants, Leaf identification, Machine learning algorithms, Herbal remedies, Healthcare
1. INTRODUCTION Medicinal plants have been integral to human healthcare practices for centuries, offering a rich source of natural remedies and therapeutic agents. The diverse array of botanical species and their potential medicinal properties have attracted attention from traditional healers, modern researchers, and healthcare practitioners alike. With the © 2023, IRJET
Impact Factor value: 8.266
increasing demand for holistic and nature-derived solutions, the accurate identification of medicinal plants and their specific parts, such as leaves, has become a critical area of exploration.Advancements in technology, particularly in the field of machine learning, have opened new avenues for automating and enhancing the process of plant identification. Machine learning algorithms, renowned for their capability to learn from data patterns and make informed predictions, hold immense promise in revolutionizing the identification of medicinal leaves. Traditional methods of botanical identification often require specialized expertise, extensive manual effort, and can be prone to errors. This project presents a novel approach to the identification of medicinal leaves using a comprehensive set of machine learning algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, Multiple Linear Regression (MLR), Random Forest, Support Vector Machine (SVM), and Decision Tree. By harnessing the power of these algorithms, we aim to create a reliable and automated system that not only accurately classifies medicinal leaves but also provides insights into the key features driving successful classification. The objectives of this research are twofold: first, to develop a robust framework for preprocessing leaf images, extracting meaningful features, and integrating diverse machine learning algorithms; and second, to rigorously evaluate and compare the performance of these algorithms in terms of accuracy, precision, recall, and F1-score. The findings of this study hold potential implications for both the botanical research community and the broader healthcare sector, offering an advanced tool for the identification of medicinal plants and the exploration of their potential therapeutic benefits.
2. Related Works Article[1]"A Comprehensive Review on Deep Learning Techniques for Plant Leaf Recognition" by M. Alahmadi, A. El Saddik in 2019 In this comprehensive review, the authors delve into the application of deep learning techniques for plant leaf recognition. The study covers a spectrum of methods, including convolutional neural and recurrent neural networks, highlighting their role in achieving accurate and efficient plant species identification. By analyzing advancements in deep learning, the research underscores the potential impact of these techniques in the field of botanical research and agriculture.
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