DeepFlorist: Rethinking Deep Neural Networks and Ensemble Learning as A
  Meta-Classifier For Object Classification
Afshin Khadangi
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.