International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Flower Recognition System using CNN

Author(s) Rohan Vayase, Dr. Shilpa S. Paygude
Country India
Abstract Flower recognition stands as a pivotal task across diverse fields such as botany, agriculture, and environmental studies. The manual process of identifying flower species is not only laborious but also susceptible to inaccuracies. In response to this challenge, this report introduces a Flower Recognition System employing Convolutional Neural Networks (CNNs) to automate and enhance this process. By harnessing the capabilities of deep learning, our system demonstrates remarkable proficiency in accurately classifying various flower species from input images. The CNN architecture, meticulously designed and trained on a comprehensive dataset of labeled flower images, exhibits robustness and efficiency in flower identification tasks. Through rigorous experimentation and evaluation, this report meticulously examines the development and performance of the Flower Recognition System. Furthermore, it explores the potential applications of this system in facilitating automated flower species identification, thereby contributing to advancements in botanical research, agricultural practices, and environmental conservation efforts.
Keywords Flower Recognition, Convolutional Neural Networks (CNN), Deep Learning, Image Classification, Automation, Botanical Research, Agricultural Practices, Environmental Conservation
Field Computer > Data / Information
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-18
Cite This Flower Recognition System using CNN - Rohan Vayase, Dr. Shilpa S. Paygude - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.28688
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.28688
Short DOI https://doi.org/g8rd6n
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