Flower Species Recognition using DenseNet201 and Multilayer Perceptron

Jun Xian Shee*, Kian Ming Lim, Chin Poo Lee, Jit Yan Lim

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)

Abstract

Flower species recognition is the task of identifying the species of a flower from an image. It involves using computer vision techniques and machine learning algorithms to analyze the visual features of the flower in the image and match them to a known database of flower species. Flower species recognition is a challenging task due to the variations in color, shape, and size among different flower species. Accurate flower species recognition has important applications in fields such as agriculture, botany, and environmental conservation. In view of this, this research paper presents a deep learning approach for flower species recognition using a combination of DenseNet201 and MLP. The proposed model leverages the strengths of both models for enhanced performance in recognizing flower species. DenseNet201 is known for its ability to capture complex features in images, while MLP is a powerful tool for learning nonlinear relationships between features. The model achieves impressive classification results on multiple datasets, including 94.47% accuracy on Kaggle, 98.23% and 97.35% on Oxford17 for two different protocols, and 79.13% on Oxford102.

Original languageEnglish
Title of host publication2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Pages307-312
Number of pages6
ISBN (Electronic)9798350321982
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event11th International Conference on Information and Communication Technology, ICoICT 2023 - Melaka, Malaysia
Duration: 23 Aug 202324 Aug 2023

Publication series

Name2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Volume2023-August

Conference

Conference11th International Conference on Information and Communication Technology, ICoICT 2023
Country/TerritoryMalaysia
CityMelaka
Period23/08/2324/08/23

Keywords

  • DenseNet
  • Flower species recognition
  • Multilayer perceptron

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