Identification of Different Medicinal Plants / Raw materials through PCA Using Support Vector Machine and K-Means Clustering Algorithms

Abstract

This research explores the identification of plants by analyzing their leaves, utilizing digital image processing techniques to examine shape, color, and texture features. Machine learning algorithms are then applied to classify the plants based on these characteristics. The study involves an examination of various image processing methods integrated with machine learning to differentiate between plant species using leaf features captured in images. Image processing serves as the primary approach for categorizing plants based on unique leaf characteristics or specific leaf regions, enabling subsequent identification through image analysis. The research specifically focuses on the identification and classification of plant species based on the analysis of different leaf parts. The paper presents an overview of classification methods applied in plant leaf identification, with Support Vector Machine (SVM) being the primary algorithm used for plant identification and classification. Experimental results demonstrate enhanced accuracy in plant species identification based on leaf features, offering a more efficient and time-saving approach to plant identification