# MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
## 2 Related Work
The process of testing and detecting these diseases was conducted through the gathering of olive leaf images affected by Spilocaea oleaginea fungus and Neofabrea fungus. However, the infected images were extracted from online sources. A high connection was identified with Neofabraea leaf spot disease, which included energy (r = 0.97) and entropy (r = 0.92) properties of spot texture. To perform plant disease detection, researchers in [15] managed to develop convolutional neural network models as well as deep learning methodologies, mainly, AlexNet, VGG, GoogleNet, AlexNetOWTBn, and Overfeat. To train the models, the researchers made use of an open database consisting of 87,848 images of both healthy leaves and infected plants. The dataset was composed of images taken in experimental setups and others captured in the field in real cultivation conditions. Various architecture models were trained and one of them reached a high success rate of about 99.53% in identifying corresponding diseases. To classify olive-fruit varieties, [PERSON] et al. [16] proposed a system using CNN; 2800 fruits that belonged to seven different olive varieties were photographed. For better image quality, mathematical morphology and global thresholding techniques were used to transform the initial captures. Such a transformation helped to obtain a set of images of individual fruits. Furthermore, various architectures of CNN were tried through training them with the considered images. However, the highest accuracy reached (95.91%) was obtained using the Inception-ResnetV2 architecture. In [17], a model to identify plant leaf diseases that made use of a nine-layered deep CNN was proposed by the authors. To train the model, an open dataset of 39 various classes of plant leaves and background images was used. To do so, the researchers used six different types of data augmentation methods which were image flipping, noise injection, gamma correction, principal component analysis (PCA) colour augmentation, scaling, and rotation. The performance of the model could be enhanced through the use of data augmentation. To train the proposed model, various training epochs, dropouts, and batch sizes were used. Validation data were used to improve the performance of the proposed model. The extensive simulation resulted in a classification accuracy of around 96.46%. A novel deep learning method with the purpose of detecting and classifying plant diseases was proposed by the researchers in [18] following three major steps. Initially, to obtain the region of interest, annotations were developed. Then, the researchers introduced an improved CenterNet in which there was a proposal of DenseNet-77 to achieve deep extraction of key points. In the last step, CenterNet was used as a one-stage detector for the detection and categorization of the different plant diseases. Thirty-eight types of crop diseases from the PlantVillage dataset were effectively located and classified by the proposed system. The extensive simulation achieved a classification accuracy of 96.46%. A proposed ResNet DNN architecture for leaf disease classification was shown by the authors of [19]. Its aim was the development of a deep learning-based system to predict and to classify vegetable leaf diseases. To carry out the work, five major vegetable crops were considered. To train the model, a public dataset was used. However, real-time data were gathered from agricultural fields for testing. The design of the system and its testing were carried out making use of various types of a CNN mainly AlexNet, LeNet, VGG16, VGG19, and ResNets. The prediction of the overall performance of the system was a maximum of 98%.