Classification of Medicinal Leaves using SVM

Identification of medicinal plants is essential for the development of effective medicines and the preservation of biodiversity. The goal of the artificial intelligence discipline of computer vision is to make it possible for computers to comprehend and analyze visual information from their environment, mimicking the abilities of human vision to enable applications such as image and video recognition, object detection, and autonomous navigation. Statistical algorithms and computational models are used in the artificial intelligence branch of machine learning to enable computers to learn from data and make predictions or judgements without being explicitly programmed. This research employs Computer vision and Machine Learning for the classification of Medicinal leaves. This paper focuses on the classification of three medicinal plant species, namely Neem, Tulasi, and Peepal using leaf images as input and the SVM algorithm for classification. The proposed system processes input images by applying various image processing techniques such as histogram equalization, thresholding, and morphological operations to segment the leaf from the background. Several shape, and texture features were extracted from each leaf image such as its length, width, perimeter, area, aspect ratio, homogeneity and correlation. The system also extracts texture features using gray-level co-occurrence matrix (GLCM) analysis. The extracted features are then fed into a machine learning algorithm, specifically a Support Vector Machine (SVM) classifier, to classify the medicinal plant species. Accuracy, precision, recall, and F1 score are some of the measures that are used to assess the model’s performance once the SVM algorithm has been optimised. This work lays the foundation for future research in this field and shows the potential of machine learning in the high accuracy classification of medicinal plants.