用CNN开发手机识花应用，准确率达95.84%
A Mobile Application for Flower Recognition System Based on Convolutional Neural Networks
- 采用MobileNet、DenseNet121、Xception三模型对比选型
- DenseNet121+SGD组合达95.84%准确率，各项指标均超96%
- 适合普通用户快速识别花卉，无需专业背景
卷积神经网络（CNN）是一种专为计算机视觉设计的深度学习算法，在处理大量视觉数据方面表现优异，超越了传统机器学习方法。花卉在日常生活中用途广泛，从装饰到制药再到环境净化，但识别花种需专业知识。实时获取专家帮助并不总可行。本研究开发了一款基于CNN的移动端花种识别应用，旨在让非专业人士快速获取花种信息。采用MobileNet、DenseNet121和Xception三种模型，通过七种优化算法训练评估其分类性能。结果显示，使用随机梯度下降（SGD）优化的DenseNet-121架构表现最佳，准确率达95.84%，精确率、召回率和F1分数均为96.00%。结果表明，CNN可在移动应用中有效实现花种分类。
原文摘要 · Abstract (English)
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.
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