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Real Time Flower Identification by Artificial Intelligence

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dc.contributor.author Rahman, Moshiur
dc.contributor.author Ahmed Joy, Taushik
dc.contributor.author Akter, Samia
dc.contributor.author Sattar, Abdus
dc.date.accessioned 2026-11-01T03:21:32Z
dc.date.available 2026-11-01T03:21:32Z
dc.date.issued 2024-03-21
dc.identifier.uri http://192.168.40.123:8080/handle/123456789/18272
dc.description Conference paper en_US
dc.description.abstract In 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. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Data Science en_US
dc.subject Machine Learning en_US
dc.subject Deep Learning en_US
dc.subject Smartphone Sensors en_US
dc.subject Artificial Intelligence en_US
dc.subject Monitoring en_US
dc.title Real Time Flower Identification by Artificial Intelligence en_US
dc.type Other en_US


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