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.
Abstract (translated)
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，随着数据量的增长，传统的机器学习算法变得不够用，而CNN们成功地解决了这一挑战。花朵在我们的日常生活中有着多种用途，从装饰到制作药物再到净化环境。识别花种需要专业知识。然而，在任何时间和地点都能随时访问专家并不总是可行的。为此，本研究开发了一款基于CNN的移动应用，用于识别不同种类的花卉，为非专业人士提供快速便捷地获取有关花种的信息途径。该研究采用了三种不同的CNN模型，即MobileNet、DenseNet121和Xception，以确定最适合应用于移动端的模型。通过使用七种不同的优化算法对这些模型进行训练后对其分类性能进行了评估。结果表明，在使用随机梯度下降（SGD）优化算法的情况下，DenseNet-121架构表现最佳，达到了95.84%的准确率、96.00%的精确度、召回率和F1值。这一结果显示了CNN在移动应用中用于花卉分类的有效性。
URL
https://arxiv.org/abs/2601.15810
