Published: 2023-12-30
The Application of Convolutional Neural Networks in Floristic Recognition
DOI: 10.35870/ijsecs.v3i3.1827
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Abstract
In the dynamic field of computer vision, this research explores the application of Convolutional Neural Networks (CNNs) for the complex task of floristic recognition, a critical aspect of botanical and ecological studies. Addressing the challenges posed by the vast diversity and subtle morphological differences among plants, our study leverages CNNs for an efficient and accurate plant identification method. Distinguished by a comprehensive dataset encompassing a wide range of plant species and employing a state-of-the-art CNN model, our research significantly advances the methodology of flower recognition. This paper highlights the CNN model's sophisticated feature extraction and image analysis capabilities, demonstrating its superior performance in classifying a diverse range of flora compared to traditional methods and other machine learning techniques like Support Vector Machines (SVM) and decision trees. Our approach emphasizes practical applications in areas such as agriculture, ecology, and conservation, and offers a powerful tool for rapid and efficient plant identification, crucial in biodiversity studies. The research contributes to the fields of botany, ecology, and environmental conservation, underscoring the transformative potential of CNNs in floristic recognition. It also outlines the future direction for enhancing the model's efficiency, including developing more computationally efficient architectures and expanding training datasets.
Keywords
Convolutional Neural Networks ; Floristic Recognition ; Botanical Research ; 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.v3i3.1827
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Legito
Informatics Engineering Study Program, Sekolah Tinggi Teknologi Sinar Husni, Medan City, North Sumatra Province, Indonesia
Rini Nuraini
Informatics Study Program, Faculty of Communication and Informatics Technology, Universitas Nasional, City of South Jakarta, Special Capital Region of Jakarta, Indonesia
Loso Judijanto
Public Policy Research, IPOSS Jakarta, Indonesia
Ahmadi Irmansyah Lubis
Software Engineering Technology Study Program, Politeknik Negeri Batam, Batam City, Riau Islands, Indonesia
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