# Urban Plants Classification Using Deep-Learning Methodology: A Case Study on a New Dataset
## 3 Urban Planter Dataset  
The dataset was especially collected and annotated by our research team. We photographed the plants in the countryside and public gardens, while trying to choose the underrepresented species not covered by the existing dataset.  
The Urban Planter dataset covers 15 species of houseplants images, 100 images per each. Some species have a unique visual appearance, for example, Begonia Maculata; others have a very similar appearance, for example, House Leek and Paddle Plant (see Figure 1). There are large viewpoint, scale, and illumination variations. The large intra-class variability and sometimes small inter-class variability make this dataset very challenging for the plant classification task. The plant categories are deliberately chosen to have some ambiguity on each aspect. For example, some classes cannot be distinguished on color alone (e.g., moon cactus, nerve plant, poinsettia), others cannot be distinguished on shape alone (e.g., soleus), as illustrated in Figure 2. The majority of the images were photographed by our team. In addition, the plant images were retrieved from multiple sources, including numerous websites (USDA Plants Dataset, Missouri Botanical Garden Database, Better Homes & Gardens, the Urban Nursery, the National Gardening Association Database, House Plants Expert, RHS, and ASPCA), social networks, and self-made photographs of house plants. Table 1 contains a summary of 15 species covered by the Urban Planet dataset. For experiment, the dataset was traditionally split into 70% training, 10% validation, and 20% test sets. According to [25], all three sets are necessary for fitting a classifier to a new domain. Namely, _training set_ is used for learning, that is, to fit the parameters of the classifier; _validation set_ is used for fine-tuning, that is, to tune the parameters of a classifier (for example, the number of hidden units in a neural network), while a _test set_ is used to evaluate the performance of a fully-specified classifier.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**ID** & **Class** & **Scientific Name** & **Higher Classification** & **Habitat** \\ \hline
0 & Begonia Maculata & Begonia maculata & Begonia & Brazil \\ \hline
1 & Coleus & Coleus & Ocimeae & Southeast Asia and Malaysia \\ \hline
2 & Elephant’s Ear & Colocasia & Aroideae & Pacific Islands \\ \hline
3 & House Leek & Sempervivum & Stonecrops & Sahara Desert and Caucasus \\ \hline
4 & Jade Plant & Crassula ovata & Pigmyweeds & South Africa \\ \hline
5 & Lucky Bamboo & Dracena sanderiana & Dracena & Southeast Asia \\ \hline
6 & Moon Cactus & Gymnocalycium mihanovichin & Gymnocalycium & tropical and subtropical America \\ \hline
7 & Nerve Plant & Fittonia albivensis & Fittonia & South America \\ \hline
8 & Paddle Plant & Kalanchoe luciae & Kalanchoideae & South Africa \\ \hline
9 & Parlor Palm & Chamaedorea elegans & Chamaedorea & Southern Mexico and Guatem \\ \hline
10 & Poinsettia & Euphorbia pulcherrima & Euphorbia subg. Poinsettia & Central America \\ \hline
11 & Sansevieria Ballyi & Sansevieria Ballyi & Asparagaceae & Africa, Madagascar and southern Asia \\ \hline
12 & String Of Banana & Senecio rowleyanus & Ragworts & South Africa \\ \hline
13 & Venus Fly Trap & Dionaea muscipula & Dionaea & Carolinas \\ \hline
14 & Zebra Cactus & Haworthia attenuata & Haworthiopsis & South Africa \\ \hline \end{tabular}
\end{table}
Table 1: The Urban Planter Dataset summary.  
Figure 1: Examples of the fifteen species from the Urban Planter dataset. Note the similarity between Paddle Plant and House Leek (top row, right), and between Coleus and Poinsettia (the middle and last images in the last column).