Automated System for Medicinal Plant Classification Usage Recommendation

Abstract

Abstract: We propose the utilization of deep learning to create an automated system for classifying medicinal plants, enabling swift identification of beneficial plant species. Convolutional neural networks (CNNs) are renowned for their exceptional capabilities in extracting and classifying features. Our proposed model, named "deep learning model," is developed by extracting features and employing a CNN classifier, resulting in high accuracy and minimal prediction time when applied to real-time images. The system consists of three modules: image pre-processing, image feature extraction, and image classification. During the initial pre-processing step, we conduct RGB conversion to extract the green band from input images. In the subsequent step, after pre-processing, we extract features such as shape, color, and texture from the pre-processed image. These features are then utilized to classify the image as either medicinal or a regular leaf. Plants have been utilized as a medicinal resource in Ayurveda since ancient times. The identification of the correct plant is a crucial step in the preparation of Ayurvedic medicine, traditionally carried out manually. However, with the need for mass production, it becomes essential to automate the process of plant identification. In this study, we propose the implementation of a technique that employs a CNN algorithm, along with an ensemble supervised machine learning algorithm that considers color, texture, and geometrical features, for the identification of medicinal plants. The Convolutional Neural Network (CNN) is utilized for classification and determining the plant class