An AI-powered mobile application for aroid identification and interactive learning: Enhancing pharmacognosy education through deep learning and NLP

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dc.authorid0000-0001-9840-4211
dc.authorid0009-0007-4392-3162
dc.authorid0009-0009-4492-9405
dc.authorid0000-0001-6000-2479
dc.contributor.authorTokatlı, Nazlı
dc.contributor.authorBilmez, Yakuphan
dc.contributor.authorKılıç, Yusuf
dc.contributor.authorAlpınar, Abdülkerim
dc.date.accessioned2026-01-22T14:25:10Z
dc.date.available2026-01-22T14:25:10Z
dc.date.issued2025
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.departmentFakülteler, Eczacılık Fakültesi, Eczacılık Meslek Bilimleri Bölümü, Farmakognozi Ana Bilim Dalı
dc.description.abstractAroid plants (Araceae family), recognized for their distinct inflorescence, possess significant botanical, pharmaceutical, and practical importance due to their content of both beneficial compounds and toxins such as calcium oxalate crystals. Accurate identification of these species is particularly crucial in pharmacy education; however, morphological similarities among Aroid species often lead to confusion among students. This paper presents a deep learning-based mobile application designed to support both plant identification and interactive learning. The solution leverages EfficientNet and Convolutional Neural Network (CNN) architectures, achieving up to 96 % accuracy in classifying Aroid species. The visual classification model, trained on a comprehensive dataset, is deployed via a RESTful API and integrated within a Flutter-based mobile application. In addition, the app incorporates a Natural Language Processing (NLP)-powered chatbot to address user inquiries regarding plant characteristics and care. While technical evaluations demonstrate robust model performance, a comprehensive user evaluation aimed at assessing the system's educational value, usability, and chatbot interaction is planned as future work. This study underscores the potential of AI-driven mobile solutions in advancing pharmacognosy education, with future developments aimed at expanding the app's botanical scope and enhancing user engagement based on forthcoming survey results.
dc.identifier.citationTokatlı, N., Bilmez, Y., Kılıç, Y., & Alpınar, A. (2025). An AI-powered mobile application for aroid identification and interactive learning: Enhancing pharmacognosy education through deep learning and NLP. 2025 Innovations in Intelligent Systems and Applications Conference, ASYU 2025, (ss. 1-6). IEEE. https://doi.org/10.1109/ASYU67174.2025.11208294
dc.identifier.doi10.1109/ASYU67174.2025.11208294
dc.identifier.endpage6
dc.identifier.isbn9798331597276
dc.identifier.issn2770-7946
dc.identifier.issn2770-7938
dc.identifier.scopus2-s2.0-105022457116
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1109/ASYU67174.2025.11208294
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1261
dc.indekslendigikaynakScopus
dc.institutionauthorTokatlı, Nazlı
dc.institutionauthorBilmez, Yakuphan
dc.institutionauthorKılıç, Yusuf
dc.institutionauthorAlpınar, Abdülkerim
dc.institutionauthorid0000-0001-9840-4211
dc.institutionauthorid0009-0007-4392-3162
dc.institutionauthorid0009-0009-4492-9405
dc.institutionauthorid0000-0001-6000-2479
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2025 Innovations in Intelligent Systems and Applications Conference, ASYU 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAroid Plants
dc.subjectChatbot
dc.subjectConvolutional Neural Network
dc.subjectDeep Learning
dc.subjectEfficientNet
dc.subjectMobile Application
dc.subjectPharmaceutical Education
dc.subjectPlant Identification
dc.titleAn AI-powered mobile application for aroid identification and interactive learning: Enhancing pharmacognosy education through deep learning and NLP
dc.typeConference Object
dspace.entity.typePublication

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Bilgisayar Mühendisliği Bölümü Koleksiyonu
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