Deep Learning-Based Convolutional Neural Network for Flower Classification

Yangchuan Liu

2024

Abstract

To tackle the flower classification problem, this study utilizes the Oxford 102 Flowers dataset and develops a machine learning model using a Convolutional Neural Network (CNN). Initially, the preprocessing phase involved the use of grayscale images. However, recognizing the critical role that color plays as a distinctive feature of flowers, the study shifted to using RGB images. To further enhance the model's performance, data augmentation techniques were introduced. These included random adjustments to brightness, saturation, contrast, and hue, which helped diversify the training set and improve the model's generalization ability. To mitigate overfitting, several strategies were employed, such as tuning the number of neurons and incorporating Dropout Layers. These approaches helped the model achieve a validation accuracy of approximately 0.7, which is sufficiently accurate for basic flower classification tasks in everyday applications. This outcome demonstrates the effectiveness of the chosen methods and highlights the potential of CNNs in flower image classification.

Paper Citation

in Harvard Style

Liu Y. (2024). Deep Learning-Based Convolutional Neural Network for Flower Classification. In Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM; ISBN 978-989-758-738-2, SciTePress, pages 315-319. DOI: 10.5220/0013329800004558

in Bibtex Style

@conference{mlscm24,
author={Yangchuan Liu},
title={Deep Learning-Based Convolutional Neural Network for Flower Classification},
booktitle={Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM},
year={2024},
pages={315-319},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013329800004558},
isbn={978-989-758-738-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM
TI - Deep Learning-Based Convolutional Neural Network for Flower Classification
SN - 978-989-758-738-2
AU - Liu Y.
PY - 2024
SP - 315
EP - 319
DO - 10.5220/0013329800004558
PB - SciTePress