A Study on Flower Classification Using Deep Learning Techniques

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Published: Oct 11, 2023
Updated: 2023-10-11
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2023-10-11 (2)
DOI: https://doi.org/10.22362/ijcert/2023/v10/i04/v10i0403
Keywords:
Deep convolutional neural network (DCNN), Region-based convolutional neural network(R-CNN’s), Fast Region-based convolutional neural network (Fast R-CNN’s), Visual Geometric Group (VGG19), Residual Neural Network(Resnet)

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S. Navyakala
Computer Science and Engineering, Sri Padmavati Mahila Visvavidyalayam, SPMVV, Tirupati, India
N. Padmaja
Computer Science and Engineering, Sri Padmavati Mahila Visvavidyalayam, SPMVV, Tirupati, India
P. Neelima
Computer Science and Engineering, Sri Padmavati Mahila Visvavidyalayam, SPMVV, Tirupati, India

Abstract

A vital component of the ecological order, the flower is one of the plant's organs. There are various applications for flowers that are advantageous to people. Flowers today come in more than 400000 different types. Due to the similarities in shape and color of different flower types, it is challenging to distinguish them from one another. The wide variety of shapes, the distribution of colors, the lighting, and the distortion of exposure make flower classification a difficult challenge. With some photos, it gets harder for the human eye to distinguish between flowers that are similar in color and shape. For picture recognition and classification issues, deep learning algorithms are frequently used. Deep learning architectures have been improved throughout time to include more layers and create more reliable models for categorization issues. Researchers recently used CNN models to solve a variety of classification issues, doing away with the necessity for manual elements of classification. In this study, Deep CNN-based traditional artificial neural networks are used for image classification and identification of flower species. Popular pre-trained learning techniques such as VGG19, RCNN, Fast R-CNN, GoogleNet, and ResNet are conducted to classify flower species.


 

Article Details

How to Cite
[1]
N. S, P. N, and N. P, “A Study on Flower Classification Using Deep Learning Techniques”, Int. J. Comput. Eng. Res. Trends, vol. 10, no. 4, pp. 161–166, Oct. 2023.
Issue
Vol. 10 No. 4 (2023): April (2023) Issue
Section
Research Articles
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Author Biographies

S. Navyakala, Computer Science and Engineering, Sri Padmavati Mahila Visvavidyalayam, SPMVV, Tirupati, India

 

 

N. Padmaja, Computer Science and Engineering, Sri Padmavati Mahila Visvavidyalayam, SPMVV, Tirupati, India

 

 

P. Neelima, Computer Science and Engineering, Sri Padmavati Mahila Visvavidyalayam, SPMVV, Tirupati, India

 

 

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