Real Time Flower Identification by Artificial Intelligence

dc.contributor.authorRahman, Moshiur
dc.contributor.authorJoy, Taushik Ahmed
dc.contributor.authorAbdus Sattar, Samia Akter
dc.date.accessioned2025-11-17T08:14:27Z
dc.date.available2025-11-17T08:14:27Z
dc.date.issued2024-03-21
dc.descriptionConference paper
dc.description.abstractIn the realm of flower-rich Bangladesh, the presence of these blossoms enriches our everyday experiences, whether encountered during leisurely strolls, along railway tracks, or within our gardens. However, the beauty of these flowers often remains unexplored due to our limited knowledge about their names and attributes. To address this, a project was initiated to close this gap and acquaint people with these unfamiliar yet frequently encountered blooms. Our endeavor involves an innovative mobile application that employs real-time camera recognition to identify flowers, powered by neural networks, particularly the Tensorflow-based image classifier on the Android platform. Machine learning's expansive applications in computer science have propelled our interest in this arena, specifically focusing on Convolutional Neural Networks (CNN) and Tensorflow for image classification. While our current application marks the inception, aspiration to further enrich and refine our system for the future. Our ultimate aim is to share the benefits of our work, enabling individuals to gain profound insights into the enchanting floral world that envelops them daily
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/15766
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/15766
dc.language.isoen_US
dc.publisherScopus
dc.sourceDIU Institutional Repository
dc.subjectReal-time systems
dc.subjectPropulsion
dc.subjectMachine learning
dc.subjectNeural networks
dc.subjectImage recognition
dc.subjectOperating systems
dc.subjectFlowering plants
dc.titleReal Time Flower Identification by Artificial Intelligence
dc.typeOther

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