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.
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