# Urban Plants Classification Using Deep-Learning Methodology: A Case Study on a New Dataset
## 5 Results and Discussion  
Table 3 contains accuracy scores for each experiment scenario. We can note that the results of the experiment 0-TL are the lowest. This can be attributed to the size of the Urban Planter dataset, i.e., more data are needed to accurately train DNNs with a very large number of parameters. Both Oxford102 and ImageNet pre-training (experiments 1-TL-Ox and 1-TL-IN) improve the results. We have three exceptions--both Inception models and VGG16 perform worse in 1-TL-Ox than in 0-TL. However, all models, including these three, perform significantly better when pre-trained in ImageNet (1-TL-IN). In general, the results of transfer learning with the ImageNet are higher than those of transfer learning with the Oxford102. This outcome can be explained by the size of the datasets used for pre-training: ImageNet contains over 1.2 M million images, while Oxford102 contains only about 8000 images. It bears evidence that a training size plays a more crucial role for fitting networks' parameters than a training domain. The results of the experiment 2-TL--two-steps transfer learning--are very close to the results of the experiment 1-TL-IN--transfer learning with ImageNet pre-training only. As such, we can conclude that the following pre-training on Oxford102 has almost no influence on the quality of the classification models pre-trained on ImageNet and their ability to classify plants in Urban Planter. The possible explanation for this outcome is also the superior size of the ImageNet and its rich class diversity. Many features and patterns are shared between different categories, and it seems that Oxford102 hardly adds any new information to it. In addition, we can note a significant gap in the performance of the MobileNet in the experiment 0-TL. After training for 100 epochs, its classification accuracy reached only 6.67%. However, the accuracy rates of the MobileNet in other experiments are comparable to the other models, and even higher than the accuracy rates of both VGGs in the experiments 1-TL-IN and 2-TL. The MobileNet architecture is much more shallow than the architecture of advanced deep models. Therefore, to learn complex features needed for fine-grained classification, it requires a larger amount of data. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **0-TL** & **1-TL-Ox** & **1-TL-IN** & **2-TL** \\ \hline Xception & 66.33\% & 69.67\% & 94.67\% & **95.00**\% \\ Inception-ResNet-v2 & **74.33**\% & **72.00**\% & 93.33\% & 93.67\% \\ Inception-v3 & 71.67\% & 62.33\% & 91.67\% & 90.00\% \\ DenseNet201 & 63.33\% & 66.33\% & **96.00**\% & 94.67\% \\ MobileNet-v2 & 6.67\% & 41.67\% & 86.33\% & 83.67\% \\ VGG19 & 51.67\% & 59.00\% & 70.67\% & 75.00\% \\ VGG16 & 62.00\% & 57.67\% & 80.67\% & 81.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy rates on the Urban Planter dataset in different experiments. We can see that Xception, Inception-ResNet-v2, and DenseNet201 are the best-performing systems in most scenarios. Their advanced architectures can explain their superiority. Both the Xception and Inception-ResNet-v2 models are the extensions of the Inception architecture. The Inception architecture is characterized by Inception modules--modules that combine multiple layers with their output filter banks concatenated into a single output vector, which forms the next layer's input. In the DenseNet architecture, each layer receives direct input from all preceding layers; consequently, it can learn very diverse features and patterns. In comparison to the previous works, the following can be concluded:  
(1) No direct comparisons between accuracy scores can be made because our results are obtained on a new--Urban Planter--dataset. Previous results range from 25% to 99% [14] on different datasets;  
(2) The majority of studies proposed approaches for plant classification that are based on the analysis of only one part of a plant's structure. Leaf followed by flower was the most widely studied part [14].