Published: 2023-12-10
Implementation of Flower Recognition using Convolutional Neural Networks
DOI: 10.35870/ijsecs.v3i3.1808
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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 preprocessing, utilizing sophisticated techniques to augment the model's capacity to distinguish intricate characteristics of flowers. The crux of the study revolves around the amalgamation of a Convolutional Neural Network, a robust deep learning methodology, with Keras, a user-accessible open-source framework for machine learning. The integration of these components enables the development of a resilient flower recognition model that possesses the ability to acquire complex patterns and characteristics from input images. The training of the model encompasses exposure to a wide range of flower datasets, which enhances its ability to generalize across different species and environmental conditions effectively. The findings illustrate the effectiveness of "Blossom Insight" in attaining a notable level of precision in tasks related to the identification of flowers. The implementation not only contributes to the advancement of the field of computer vision but also offers a valuable resource for researchers, horticulturists, and enthusiasts seeking a comprehensive understanding and accurate identification of floral species. The development of "Blossom Insight" signifies a notable advancement in utilizing deep learning techniques to augment our understanding and admiration of the wide variety present in the realm of flowers.
Keywords
Blossom Insight ; Convolutional Neural Networks ; Deep Learning Methodology ; Keras ; Machine Learning
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 3 No. 3 (2023)
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Section: Articles
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Published: %750 %e, %2023
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License: CC BY 4.0
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Copyright: © 2023 Authors
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DOI: 10.35870/ijsecs.v3i3.1808
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Djarot Hindarto
Informatics Study Program, Faculty of Communication and Informatics Technology, Universitas Nasional, City of South Jakarta, Special Capital Region of Jakarta, Indonesia
Nadia Amalia
Faculty of Dentistry, Universitas Padjadjaran, Bandung City, West Java Province, Indonesia
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