# Urban Plants Classification Using Deep-Learning Methodology: A Case Study on a New Dataset
## 2 Related Work  
During the last two decades, various computer vision techniques were employed for plant species classification. Earlier methods used manually defined features, mostly based on the combination of shape, color and texture descriptors, and statistical information [1, 2, 3, 4, 5, 6, 7, 8]. Local features descriptors, e.g., HOG [9] and SIFT-based, have also been applied for flower analysis [10, 11, 12]. Manually defined and local feature descriptors are fed to the traditional machine learning models, e.g., k-NN [10] and SVM [9]. [PERSON] et al. [13] introduced a method for plant classification by analyzing leaf images. The method extracts invariants from the shifted Legendre-Fourier moments feed them to a fully connected artificial neural network. The comprehensive survey on the studies of computer vision approaches for plant species identification can be found in [14]. With the advances of hardware, especially with the incorporated use of GPUs, deep neural networks (DNNs) have achieved new standards in many research frontiers. The main advantage of the DNNs is that they do not require manual feature extraction, and the features are learnt within a DNN framework. However, DNNs require a large amount of training data, which is not always available. [PERSON] and [PERSON] [15] compared between a deep learning model (VGG19) and conventional machine learning methods, and showed that DNN yielded a higher accuracy for all (four) datasets of plant images. For small or moderate size datasets, transfer learning can help to overcome dataset size limitation [16]. In the case of plant classification, ImageNet is a common choice for a transfer learning scenario [17, 18, 19, 20]. [PERSON] and [PERSON] [17] based their flower classifier on an Inception-v3 model, trained on ImageNet. [PERSON] et al. [19] explored the effect of transfer learning for flower classification using four deep-learning models, VGG16, VGG19, Inception-v3, and ResNet. They showed that pre-training the models avoids over-fitting and improves the recognition accuracy. [PERSON] et al. [18] presented a two-step deep-learning classifier, where first a flower is segmented from the background and then is classified. This framework is based on the VGG16 model with pre-trained weights. [PERSON]. [21] introduced a deep learning system for diverse plants classification in agriculture applications. The experiment, performed on the Plant Seedlings Dataset, aimed to determine which of three pre-trained models--Inception-v3,VGG16, and Xception--reaches the best accuracy. Results determined that Xception is the best performing model. With the extensive use of mobile devices, lightweight DNNs are desirable. The MobileNet model was specially designed for usage in mobile applications. MobileNet significantly reduces the number of parameters compared to other conventional deep-learning models, such as VGG16 or Inception-v3. [PERSON] et al. [20] experimented with MobileNet models for flower classification on the Oxford102 dataset. They showed that MobileNets give comparable performance accuracy while being much smaller and requiring 2.5 times less computation. In [22], a feature extraction using different DNNs--ResNet50-v2, Inception ResNet-v2, MobileNet-v2, and VGG16--was explored in large-scale plant classification methods. A comparative evaluation on the PlantCLEF2003 dataset showed the superiority of the SVM classifier with MobileNet-v2 as a feature extractor. In [23], a comparative evaluation of four deep convolutional feature extraction models--MobileNet-v2, VGG16, ResNet-v2, and Inception-ResNet-v2--tested with the SVM classifier, showed the superiority of MobileNet-v2 on the Vietnamese plant image dataset. MobileNet combination with Logistic Regression was also the best performing system for leaf classification in [24], based on comparative evaluation on two botanical datasets--Flavia (32 classes) and Leafsnap (184 classes).