# MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
## 1 Introduction  
Recently, agriculture is undergoing the Fourth Industrial Revolution by integrating new technological advances into traditional farming practices [1]. Smart farming plays a vital role as it offers various innovative solutions to modernize the farming sector. Unfortunately, for farmers and farming in general, the growth of plants is hindered by a variety of plant diseases which result in huge losses [2]. Recent studies by the Food and Agriculture Organization of the United Nations (FAO) have shown that plant diseases result in around 10% loss of global food production. The situation has worsened due to trans-boundary plant diseases that have become increasingly noticeable and which affect agricultural production. To secure the global food supply, there is a need for early detection of diseases, and therefore this has become the ultimate concern of experts around the world [3]. Different studies have shown that agriculture is the leading activity in the economy of many countries. Agricultural products are traded at a large scale. Olives are one of the products that is highly demanded in many countries [4]. The Kingdom of Saudi Arabia is the leading olive-producing country in the South of the Mediterranean, accounting for6% of the global olive production, which makes the country the second highest producer in the world after the European Union. Half of the country's agricultural exports come from olive oil which represents around 5.5% of the total exports of the Kingdom of Saudi Arabia. For the Kingdom of Saudi Arabia, the olive oil trade is considered to be the fifth source of foreign currency, which makes it a vital sector for the economy of the country. In fact, significant efforts have been made in the country to restructure and to modernize olive production with the purpose of improving the quality of olive oil and expanding acreage [5]. Recently, olive farmers in the Kingdom of Saudi Arabia have been dealing with many serious diseases, such as olive leaf spot that is caused by fungi. The symptoms may take the form of purple or brown rings enclosing a green or yellow center, or they may be solid purple or brown circles. Olive leaf spot disease affects the vigor of the olive tree by damaging the leaves and causing them to prematurely fall to the ground, which, in turn, decreases productivity dramatically [6]. In addition, Aculus Olearius is one of the species which has been recently recorded in the Kingdom of Saudi Arabia. It dramatically damages olive trees, causing significant harm to the olive oil sector in the country, by distorting the buds and leaves, mainly in younger trees, and deforming the olive fruit. Purple or brown, circular leaf spots appear on affected leaves that turn yellow and fall prematurely [6]. Among olive species, the leaves are frequently affected by olive scab which is a wide-spread fungal disease that is awful looking and damaging, as it affects the well-being of olive trees, resulting in premature falling of leaves leading to an economic loss for the growers of olive trees. Olive leaf spots, scientifically referred to as Aculus Olearius, as well as olive scab, can be visually noticed and recognized. However, this represents only the first step in the process of detecting the disease affecting olive trees, which may not be enough. Thus, there is a need for contacts with experts to go further in the process of detecting the disease. Such a process may be time consuming and also costly. Human errors are also plausible. As a matter of fact, there is a need for an automatic, inexpensive, accurate, and in particular, a fast method to detect this olive disease [7]. Deep learning (DL) is one of the major domains of machine learning that uses neurons similar to mathematical functions for learning tasks [8]. Recently, neural networks (NN) have been commonly used in decision-making strategies; while the use of deep neural networks (DNN) such as a convolutional neural network (CNN) has attracted enormous interest since it is widely utilized for disease detection [9].