[Paper Review] A Mobile Application for Flower Recognition System Based on Convolutional Neural Networks

Mustafa Yurdakul, Enes Ayan+2 more|arXiv (Cornell University)|Jan 22, 2026

Smart Agriculture and AI0 citations

TL;DR

The paper develops a mobile CNN-based flower recognition app evaluating MobileNet, DenseNet121, and Xception with seven optimizers; DenseNet121 with SGD yields the best performance (95.84% accuracy, 96.00% precision, recall, and F1).

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.

Motivation & Objective

• Motivate quick, non-expert flower identification via a mobile app.
• Evaluate multiple CNN architectures for on-device flower classification.
• Identify the optimization algorithm that yields best performance for mobile flower recognition.

Proposed method

• Implement three CNN models (MobileNet, DenseNet121, Xception) on a mobile-friendly pipeline.
• Train and evaluate models using seven different optimization algorithms.
• Compare accuracy, precision, recall, and F1-score to select the best-performing model for mobile deployment.

Experimental results

Research questions

• RQ1 Which CNN architecture among MobileNet, DenseNet121, and Xception is most suitable for mobile flower recognition?
• RQ2 How do different optimization algorithms affect model performance in a mobile flower recognition setting?
• RQ3 Can a CNN-based mobile app provide reliable flower identification for non-experts with high accuracy?

Key findings

• DenseNet121 with SGD achieved the best performance among the tested models.
• Best metrics reported: 95.84% accuracy and 96.00% for precision, recall, and F1-score.