A Mobile Application for Flower Recognition System Based on Convolutional Neural Networks

TL;DR

This paper presents a mobile app for flower recognition using CNNs, demonstrating high accuracy with DenseNet-121, enabling non-experts to identify flowers easily via smartphones.

Contribution

Introduces a mobile flower recognition system utilizing CNNs and compares three models to identify the most effective architecture for mobile deployment.

Findings

DenseNet-121 achieved 95.84% accuracy

SGD was the most effective optimizer for DenseNet-121

CNN-based mobile app successfully classifies flowers in real-time

Abstract

A convolutional neural network (CNN) is a deep learning algorithm that has been specifically designed for computer vision applications. The CNNs proved successful in handling the increasing amount of data in many computer vision problems, where classical machine learning algorithms were insufficient. Flowers have many uses in our daily lives, from decorating to making medicines to detoxifying the environment. Identifying flower types requires expert knowledge. However, accessing experts at any time and in any location may not always be feasible. In this study a mobile application based on CNNs was developed to recognize different types of flowers to provide non-specialists with quick and easy access to information about flower types. The study employed three distinct CNN models, namely MobileNet, DenseNet121, and Xception, to determine the most suitable model for the mobile application. The classification performances of the models were evaluated by training them with seven different optimization algorithms. The DenseNet-121 architecture, which uses the stochastic gradient descent (SGD) optimization algorithm, was the most successful, achieving 95.84 % accuracy, 96.00% precision, recall, and F1-score. This result shows that CNNs can be used for flower classification in mobile applications.