Applying convolutional neural network and Nadam optimization in flower classification

Aini, Qurrotul, Zulfiandri, Zulfiandri, Firmansyah, Rezky and Arif, Yunifa Miftachul ORCID: https://orcid.org/0000-0002-2183-0762 (2024) Applying convolutional neural network and Nadam optimization in flower classification. Bulletin of Electrical Engineering and Informatics, 13 (4). pp. 2865-2877. ISSN 20893191

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Abstract

Flowers have a variety of shapes, colors and structures, the images of which need to be classified using guided learning techniques. Several studies classify flowers using machine learning, but their accuracy performance is not good. The thing is, the flowers come in a variety of colors that can sometimes look similar to the background. Therefore, this study aims to classify flowers using a convolutional neural network (CNN) and measure its performance. The method used is mixed methods by collecting existing data from previous studies and connecting it with the realities in the field. The Kozłowski and Steinbrener models were used, while the image data was obtained from the Oxford17 and Oxford102 dataset with 17 and 102 flower types, respectively. The results show 60% and 84% accuracy of CNN using the scratch and transfer learning approach for the Oxford17 dataset. The Oxford102 dataset shows 42% and 64%, respectively, with CNN from baseline and transfer learning.

Item Type:	Journal Article
Keywords:	Convolutional neural network; Transfer learning; ResNet; Nadam optimization; Flower classification
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Yunifa Miftachul Arif
Date Deposited:	05 Jun 2024 14:04

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