Flower Picture Classification Based on Convolutional Neural Network

Ben Zhao

2024

Abstract

Due to changes in the ecological environment, many species of flowers are on the brink of extinction. By using scanning technology to help people quickly identify each type of flower, that can implement conservation measures more directly and effectively. In this paper, characteristics are extracted from four different types of plant pictures using a convolutional neural network (CNN) model. Model checkpoints and early stopping techniques were used to preserve the trained model during training. The trained model is used to predict a single image, classify the flower according to its characteristics, and finally output the result. However, the output results show that although the accuracy is very high, the precision is abnormally low, which indicates that the model may be overfitting. In the future, the quality of the models can be further improved by increasing the complexity of the models, or balancing the data sets to more accurately protect these endangered flower species.

Paper Citation

in Harvard Style

Zhao B. (2024). Flower Picture Classification Based on Convolutional Neural Network. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 40-44. DOI: 10.5220/0013487000004619

in Bibtex Style

@conference{daml24,
author={Ben Zhao},
title={Flower Picture Classification Based on Convolutional Neural Network},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={40-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013487000004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Flower Picture Classification Based on Convolutional Neural Network
SN - 978-989-758-754-2
AU - Zhao B.
PY - 2024
SP - 40
EP - 44
DO - 10.5220/0013487000004619
PB - SciTePress