Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Aruna, S.K.; Praveen, P.; Gowtham, K.; Khashif, S.Mohammed; Jaganathan, Keerthana; Karthick, K. Title A name given to the resource Classification of Medicinal Plant Leaves using Deep Learning Algorithms Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Intelligent Systems and Applications;Volume;18;Issue;1;pp.132-149 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.5815/ijisa.2026.01.10" target="_blank" rel="noreferrer noopener">https://doi.org/10.5815/ijisa.2026.01.10</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030486801?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030486801?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Aruna S.K., Department of AI and Data Science Engineering, CHRIST University, Bangalore, India; Praveen P., Sun Publications, Sivakasi, India; Gowtham K., Juspay Technologies Private Limited, Bangalore, India; Khashif S.M., Impelsys Private Limited, Bangalore, India; Jaganathan K., School of Computer Science, Northumbria University, Newcastle upon Tyne, NE1 8ST, United Kingdom; Karthick K., Department of Electrical and Electronics Engineering, GMR Institute of Technology, Andhra Pradesh, Rajam, India Description An account of the resource This research explores the automated leaf-based identification of medicinal plants, utilizing machine learning and deep learning techniques to address the crucial need for efficient plant classification. Driven by the vast potential of medicinal plants in pharmaceutical development and healthcare, the study aims to surpass the limitations of existing methodologies through thorough experimentation and comparative analysis. The primary goal is to develop a robust and automated solution for classifying medicinal plants based on leaf morphology. The methodology encompasses acquiring diverse datasets. Specifically, set 1 data is processed by applying resizing, rescaling, saturation adjustment, and noise removal, while Set 2 data is processed by applying resizing, rescaling, saturation adjustment, noise removal, and PCA (Principal Component Analysis). The proposed algorithms include Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), YOLOv8, Vision Transformer (ViT), ResNet, and Artificial Neural Networks (ANN). The study evaluates the efficacy and effectiveness of each algorithm in plant classification using metrics such as accuracy, recall, precision, and F1 score. Notably, the ResNet model achieved 93.8% and 94.8% accuracy in Set 1 and Set 2, respectively. The SVM model demonstrated 56.5% and 56.6% accuracy in Set 1 and Set 2, while the Vision Transformer (ViT) model achieved 84.9% and 74.4% accuracy in Set 1 and Set 2, respectively. The CNN model showcased high accuracy at 96.7% and 94.8% in Set 1 and Set 2, followed closely by the ANN model with 96.7% and 96.6% accuracy. Lastly, the YOLOv8 model achieved 96.0% and 95.1% accuracy in Set 1 and Set 2, respectively. The comparative analysis identifies CNN and ANN as the top-performing algorithms. This research significantly contributes to the advancement of medicinal plant identification, pharmaceutical research, and environmental conservation efforts, emphasizing the potential of deep learning techniques in addressing complex classification tasks. 2026, Modern Education and Computer Science Press. All rights reserved. Subject The topic of the resource Classification; CNN; Deep Learning; Machine Learning; Vision Transformer Publisher An entity responsible for making the resource available Modern Education and Computer Science Press Relation A related resource ISSN: 2074904X; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online