[论文解读] 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 AI被引用 0

一句话总结

该论文开发了一个基于移动 CNN 的花卉识别应用，评估 MobileNet、DenseNet121 和 Xception，采用七种优化器；DenseNet121 结合 SGD 取得最佳性能（95.84% 准确率，精确率、召回率和 F1 分数均为 96.00%）。

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.

研究动机与目标

• 通过移动应用实现快速的非专业花卉识别。
• 评估多种 CNN 架构在设备端的花卉分类性能。
• 确定在移动花卉识别中能够获得最佳性能的优化算法。

提出的方法

• 在移动友好型流程中实现三种 CNN 模型（MobileNet、DenseNet121、Xception）。
• 使用七种不同的优化算法对模型进行训练与评估。
• 比较准确率、精确率、召回率和 F1 分数，以选择适合移动部署的最佳模型。

实验结果

研究问题

• RQ1 在 MobileNet、DenseNet121 与 Xception 中，哪种 CNN 架构最适合移动花卉识别？
• RQ2 不同优化算法在移动花卉识别场景中如何影响模型性能？
• RQ3 基于 CNN 的移动应用能否以高准确率为非专业用户提供可靠的花卉识别？

主要发现

• DenseNet121 结合 SGD 在被测试的模型中表现最佳。
• 报道的最佳指标：准确率 95.84%，精确率、召回率和 F1 分数均为 96.00%。