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

2026-01-22

AI Summary

This study addresses the challenge faced by non-experts in accurately identifying flower species in the wild, a task traditionally reliant on expert consultation that suffers from limited accessibility. To overcome this, the authors develop a mobile-based flower recognition system leveraging lightweight convolutional neural networks. They systematically evaluate three architectures—MobileNet, DenseNet121, and Xception—combined with various optimization algorithms. Experimental results demonstrate that DenseNet121 trained with the SGD optimizer achieves the best performance, yielding an accuracy of 95.84% on a custom dataset, with precision, recall, and F1-score all reaching 96.00%. The resulting system enables high-accuracy, low-latency real-time flower identification, offering non-specialist users an efficient and accessible tool for plant recognition in natural settings.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Object Detection & Categorization
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• flower recognition
• mobile application
• convolutional neural networks
• image classification
• non-expert identification

Innovation

• Convolutional Neural Networks
• Mobile Application
• Flower Recognition
• DenseNet121
• Optimization Algorithms