2023
DOI: 10.35870/ijsecs.v3i3.1808
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Implementation of Flower Recognition using Convolutional Neural Networks

Djarot Hindarto,
Nadia Amalia

Abstract: The recognition of flowers holds significant importance within the realms of ecological research, horticulture, and diverse technological applications. This study presents "Blossom Insight," an innovative methodology for flower identification that employs Convolutional Neural Networks within the Keras framework. This study aims to examine the necessity of precise and effective flower categorization, considering the extensive range of floral species. The methodology encompasses a rigorous procedure of data prep… Show more

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Cited by 4 publications
(2 citation statements)
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References 13 publications
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“…In the supervised learning method, the expected target of the input that can be accepted by the network is previously known. Convolutional Neural Networks is an architecture that can be trained and consists of several stages [23]. The input to a Convolutional Neural Network is an image, and the process of describing the image into a feature that can be understood by the network is what makes a Convolutional Neural Network different from other neural networks [24], [25].…”
Section: Restnet-50 Architecturementioning
confidence: 99%
“…In the supervised learning method, the expected target of the input that can be accepted by the network is previously known. Convolutional Neural Networks is an architecture that can be trained and consists of several stages [23]. The input to a Convolutional Neural Network is an image, and the process of describing the image into a feature that can be understood by the network is what makes a Convolutional Neural Network different from other neural networks [24], [25].…”
Section: Restnet-50 Architecturementioning
confidence: 99%
“…Hindarto and Amalia (2023) introduced "Blossom Insight," a novel methodology that leverages CNNs within the Keras framework for flower identification. Their work emphasizes the importance of precise and effective flower categorization, addressing the challenges posed by the vast diversity of floral species [7]. The integration of CNNs with Keras enables the development of a robust flower recognition model capable of distinguishing intricate floral characteristics, contributing significantly to the field of computer vision.…”
Section: Related Workmentioning
confidence: 99%