Abstract: Medicinal plants have been utilized for centuries in traditional medicine Known as Ayurveda. However, manual identification and classification of these plants are time-consuming and error-prone tasks. In this study, we introduce "MediHerb Insight," an automated system using deep learning techniques for the identification and classification of medicinal herbs. Through the implementation of a convolutional neural networks (CNN), specifically Xception Architecture, our model demonstrates impressive accuracy in classifying medicinal plant species based on leaf images. Additionally, we present a user-friendly web application that allows users to upload leaf images for instant classification. This project holds significance in advancing research in botany, providing a valuable tool for plant species identification and analysis.
Keywords: Medicinal Plants, Classification, Automated system, Deep learning techniques, Convolutional neural network (CNN),Xception architecture,Plant species identification.
Cite:
Dr. Bhanu Prakash Battula, Alaparthi Sneha Madhuri, Kottamasu Naga Vinaya Sree, Patalam Asfiya, Kollipara Naga Sai Varshitha, "MEDIHERB INSIGHT", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 2, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13244.
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DOI:
10.17148/IJARCCE.2024.13244
