IRO Journals
Journal of Ubiquitous Computing and Communication Technologies

Deep Learning based Identification of Medicinal Plants using Morphology

Open Access
Published: 08 August, 2026
Pages: 248-260
  • Murugan G. , Murugan G.
    Assistant professor, Department of Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
    Assistant professor, Department of Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
  • Vishali K. , Vishali K.
    UG Scholar, Department of Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
    UG Scholar, Department of Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
  • Meenakrishna B. , Meenakrishna B.
    UG Scholar, Department of Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
    UG Scholar, Department of Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
  • Abirami K. Abirami K.
    UG Scholar, Department of Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
    UG Scholar, Department of Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India
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How to Cite

G., Murugan, Vishali K., Meenakrishna B., and Abirami K. 2026. “Deep Learning Based Identification of Medicinal Plants Using Morphology”. Journal of Ubiquitous Computing and Communication Technologies 8 (3): 248-60. https://doi.org/10.36548/jucct.2026.3.006.
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Keywords

Medicinal Plant Identification
Morphological Feature Analysis
Xception Network
XGBoost Classification
Multi-Plant Recognition
Flutter-based Mobile Application

Abstract

Identification of medicinal plants is highly significant for healthcare applications, agricultural activities, and iological diversity management. Correct identification of plants is difficult due to the existing morphological similarities between different plant species and variations in image capturing scenarios. This research work proposes a deep learning model, VanaOshadhi, which uses the morphological properties for successful identification of medicinal plants. The VanaOshadhi system includes a pretrained Xception deep neural network model to extract discriminatory features and XGBoost to classify the plant species effectively. The process involves image preprocessing, deep feature extraction, feature classification, and identification of multiple plants, followed by the deployment of the framework using a mobile application designed with Flutter framework and Flask REST API. The proposed system offers the ability to identify plants in real-time and provide information about identified plants in multiple languages.

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