In this paper, we propose a novel learning paradigm called "DeepFlorist" for flower classification using ensemble learning as a meta-classifier. DeepFlorist combines the power of deep learning with the robustness of ensemble methods to achieve accurate and reliable flower classification results. The proposed network architecture leverages a combination of dense convolutional and convolutional neural networks (DCNNs and CNNs) to extract high-level features from flower images, followed by a fully connected layer for classification. To enhance the performance and generalization of DeepFlorist, an ensemble learning approach is employed, incorporating multiple diverse models to improve the classification accuracy. Experimental results on benchmark flower datasets demonstrate the effectiveness of DeepFlorist, outperforming state-of-the-art methods in terms of accuracy and robustness. The proposed framework holds significant potential for automated flower recognition systems in real-world applications, enabling advancements in plant taxonomy, conservation efforts, and ecological studies.

本文描述了一种名为“DeepFlorist”的新型学习范式，使用集成学习作为元分类器进行花卉分类。 DeepFlorist结合了深度学习的强大能力和集成方法的鲁棒性，以实现准确可靠的花卉分类结果。 该框架在基准花卉数据集上的实验结果表明DeepFlorist的有效性，其在准确性和鲁棒性方面优于现有方法。 该框架具有在实际应用中用于自动化花卉识别系统的潜在价值，从而促进植物分类学，保护工作和生态研究的进步。

DeepFlorist：基于元分类器的深度神经网络和集成学习方法用于物体分类的重新思考