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.

翻译：卷积神经网络（CNN）是一种专门为计算机视觉应用设计的深度学习算法。在许多计算机视觉问题中，当传统机器学习算法无法处理日益增长的数据量时，CNN被证明是成功的。鲜花在我们的日常生活中有许多用途，从装饰到制药再到净化环境。识别花卉类型需要专业知识。然而，随时随地访问专家可能并不总是可行的。本研究开发了一个基于CNN的移动应用程序，用于识别不同类型的鲜花，为非专业人士提供快速便捷的花卉类型信息访问途径。该研究采用了三种不同的CNN模型，即MobileNet、DenseNet121和Xception，以确定最适合移动应用的模型。通过使用七种不同的优化算法对模型进行训练，评估了它们的分类性能。采用随机梯度下降（SGD）优化算法的DenseNet-121架构表现最为成功，达到了95.84%的准确率，以及96.00%的精确率、召回率和F1分数。这一结果表明CNN可用于移动应用中的花卉分类。