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 Kavitha, S.; Vinay, M.; Joby, Sona; Ribu, P.B. Title A name given to the resource Automated Classification of Medicinal Plants Using Lightweight Deep Learning and Transfer Learning 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 SN Computer Science;Volume;7;Issue;3;Article No.;263; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s42979-025-04716-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-025-04716-5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033842899?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033842899?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 Kavitha S., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India; Vinay M., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India; Joby S., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India; Ribu P.B., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India Description An account of the resource The identification of medicinal plants plays a pivotal role in traditional medicine, biodiversity conservation, and rural healthcare. Conventional manual identification methods are often time-consuming and error-prone, particularly when differentiating between morphologically similar species or plants at varying growth stages. Recent developments in deep learning, especially convolutional neural networks (CNNs) with transfer learning, have emerged as robust solutions for image-based classification tasks, offering efficiency and high accuracy with limited computational resources. The proposed framework employs a carefully structured deep learning pipeline integrating advanced preprocessing, lightweight architecture design, and domain-adaptive transfer learning. A large real-world dataset of 20,109 medicinal leaf images across 99 classes was standardized through resizing, normalization, and categorical encoding, followed by targeted data augmentation and class-weight balancing to address inter-class similarity and dataset imbalance. A key methodological novelty lies in the use of MobileNetV3 with an optimized transfer-learning strategy, leveraging its inverted residual blocks, Squeeze-and-Excite modules, and hard-swish activation to enhance texture-, venation-, and contour-based feature extraction in plant leaves. Unlike existing plant-recognition studies that rely on heavier CNNs, our approach introduces a computationally efficient, low-latency model specifically tailored for mobile and embedded deployment. Experimental results demonstrate that the proposed MobileNetV3-based model achieved a classification accuracy of 92.88%, with macro- and weighted-average F1-scores of 0.85 and 0.86, respectively. Precision and recall values across most classes ranged between 0.80 and 0.95, confirming the models reliability in differentiating species. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Biodiversity informatics; Deep learning; Feature extraction; Healthcare technology; Medicinal plants; Plant identification; Smart agriculture tools Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 2662995X; 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 Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online