Please use this identifier to cite or link to this item: https://has.hcu.ac.th/jspui/handle/123456789/3511
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dc.contributor.authorSila Temsiririrkkul-
dc.contributor.authorPrarinya Siritanawan-
dc.contributor.authorRungravi Temsiririrkkul-
dc.contributor.authorศิลา เต็มศิริฤกษ์กุล-
dc.contributor.authorรุ่งระวี เต็มศิริฤกษ์กุล-
dc.contributor.otherHuachiew Chalermprakiet University. Faculty of Science and Technologyen
dc.contributor.otherJapan Advanced Institute of Science and Technology. School of Information Scienceen
dc.contributor.otherThammasat University. Faculty of Pharmacyen
dc.date.accessioned2025-01-12T06:29:38Z-
dc.date.available2025-01-12T06:29:38Z-
dc.date.issued2021-
dc.identifier.urihttps://has.hcu.ac.th/jspui/handle/123456789/3511-
dc.descriptionTENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), Auckland, New Zealand, 2021, pp. 738-743, doi: 10.1109/TENCON54134.2021.9707324en
dc.descriptionสามารถเข้าถึงบทความฉบับเต็ม (Full Text) ได้ที่ : https://ieeexplore.ieee.org/document/9707324en
dc.description.abstractThe recent Deep Learning algorithms have demonstrated incredible performance in many fields of research, especially an object detection task where algorithm advances are at a level that can be applied to real-world problems. Classification of plants by leaf image is one of the problems that has been of interest among computer vision researchers for a long time because of its challenges due to a large number of species and their complex features. There are many approaches presented and show promising performance for the task. However, in the tropical area where the diversity of plants is rich, examples of the physical similarity of the leaves in different species can be found easily. Moreover, it is reported that many accidents of misidentifying plants and their usages can be life-threatening. Thus, specimens of plants with similar leaf structures are interested in this research. We present a method to identify plants that are similar in morphological characters of leaf. Using image processing and deep learning techniques and transfer learning of several deep convolutional neural network architectures: VGG-16, ResNet-50, and InceptionV3, the proposed method can identify seven Lamiaceae plants yielded high accuracy prediction of 98.71%, 91.32%, and 98.17%, respectively.en
dc.language.isoen_USen
dc.rightsIEEEen
dc.subjectDeep learning (Machine learning)en
dc.subjectการเรียนรู้เชิงลึก (การเรียนรู้ของเครื่อง)en
dc.subjectImage processingen
dc.subjectการประมวลผลภาพen
dc.subjectPlants – Classificationen
dc.subjectพืช – การจำแนกen
dc.subjectHerbs – Thailanden
dc.subjectสมุนไพร – ไทยen
dc.subjectConvolutional Neural Networken
dc.subjectโครงข่ายประสาทเทียมแบบสังวัตนาการen
dc.subjectNeural networks (Computer science)en
dc.subjectนิวรัลเน็ตเวิร์ค (วิทยาการคอมพิวเตอร์)en
dc.subjectConvolutions (Mathematics)en
dc.subjectคอนโวลูชัน (คณิตศาสตร์)en
dc.subjectImage analysisen
dc.subjectการวิเคราะห์ข้อมูลภาพen
dc.titleWhich one is Kaphrao? Identify Thai Herbs with Similar Leaf Structure using Transfer Learning of Deep Convolutional Neural Networksen
dc.typeProceeding Documenten
Appears in Collections:Science and Technology - Proceeding Document



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