**2. Digital economy**

Due to the high speed of technological change and its use by companies and consumers [21], the definition of the digital economy has evolved over time [22]. Digital economy, according to the definition of the Organization for Economic Co-operation and Development (OECD), is an economy most of which is based on digital technologies, including communication networks, computers, software, and other information technologies, and various types of e-commerce, e-markets. It also includes smart cards, e-money, and financial transactions. According to the UNCTAD (United Nations Conference on Trade and Development) definition, digital economy means the use of Internet-based digital technologies to produce and trade goods and services [21]. Bukht and Heeks [22] defined the digital economy as "that part of economic output derived solely or primarily from digital technologies with a business model based on digital goods or services." Through this approach, the digital economy consists of three layers:

#### **Figure 1.** *Illustration of the scopes of the digital economy [22].*

**Figure 2.** *Changes in digital economy share of GDP in Iran from 2012 to 2019 [23].*

first, a core including hardware manufacturing, software, and digital (IT/ICT) sector, the second layer narrow scope including electronic business, digital services, and platform economy (digital economy), and the third layer broad scope including ecommerce and algorithm economy (digitalized economy) (**Figure 1**).

The digital economy share of GDP in Iran increased from 2.2% in 2012 to 6.5% in 2019 (**Figure 2**). Although the core layer with 4% is close to the global average of 4.5%, the second and third layer with 2.5% is still significantly different from the global average of 15.5%. The digital economy in Iran, however, is rapidly growing. According to Tufts University, Iran ranks sixth among the 90 countries surveyed in the world in the momentum (growth rate) of the digital economy [23].

#### **2.1 Digital infrastructure**

While the penetration rate of fixed telephones from 2013 to September 2021 shows a decrease of 2.4 percent (**Figure 3a**) and in the years 2006 to September 2021, the

*Digital Agriculture in Iran: Use Cases, Opportunities, and Challenges DOI: http://dx.doi.org/10.5772/intechopen.103967*

**Figure 3.** *A comparison between the number of landline users (top) and mobile phone users between 2006 and 2021 in Iran.*

#### **Figure 4.**

*Fixed telephony and mobile broadband subscriptions and penetration from 2016 to 2021.*

penetration rate of mobile phones increased from 18.7 to 154.8 percent (**Figure 3b**). The penetration rate of mobile phones from 2006 to 2017 and 2017 to September 2021 increased by 7.9% and 13.8% every year, respectively [24].

From 2016 to September 2021, the mobile broadband penetration rate reached from zero to 100.22%, while it was around 12.2% for fixed telephone bandwidth (**Figure 4**) [24]. In 2020, the population covered by at least a 3G and 4G mobile network was 85% and 81%, respectively. In 2017, households with Internet access at rural and urban home were 56.98% and 77.92%, respectively, and households with Internet access at home reached 93.30% in 2020. In 2017, individuals with basic, standard, and advanced ICT (Information and Communications Technology) skills were 20.58%, 7.98%, and 1.28%, respectively [24].

#### **2.2 Challenges of IoT in Iran**

The development and use of the IoT in Iran face several challenges, and the reluctance of internet service providers to enter the IoT market in Iran [25] have caused the development of IoT infrastructures to be very slow [26], and face shortages [25–27]. For example, due to the lack of demand and the market, operators are reluctant to construct infrastructure, and the prospect of moving to 5G is challenged [25]. One of the most important platforms of the IoT is the migration from the fourth generation of IP addresses to the sixth generation, and it is not clear what the stage is in Iran [26]. The ignorance of various institutions about the powerful applications of IoT, such as smart making and Industrial IoT, is one of the obstacles to the productivity and development of this technology, and consequently, the IoT market prosperity in Iran [28]. While the pillar of IoT implementation is equipping devices with sensors and hardware components that transmit data to the IoT platform, given the current economic situation in the country, the production of these parts or their import has problems, and estimates show that the cost of the existing parts is very high [29]. Another major challenge is the lack of high-performance software platforms for sensor data collection, storage, processing, and analysis, in a short time. Almost none of the powerful foreign platforms inside Iran provide services [29]. Another obstacle is the lack of public awareness of the use of the Internet of Things on a large scale [27, 29]. For the IoT field, there is a need for access to data and measurements (data transparency and open data), but for various reasons, there are problems in the IoT field in Iran [25]. There are also challenges to data transferring to the network for use in Iran. In IoT technology, Zigbee, BLE 5.0, or Wi-Fi can be used to connect devices in the environment to a network that requires short-range connections. While Wi-Fi is present in almost all public and private places, it takes a lot of energy to connect to the network and reduces battery life. Zigbee requires less cost and energy consumption but has a low data transfer rate and is also supported by limited modules in Iran (despite their very high price). There are also standards for long-distance connections such as SigFox, NB-IoT, and LoRa. Despite the high data transmission security by LoRa, it is almost not used in Iran. SigFox protocol was recently launched, and there is still the problem of supporting it in different country regions and devices that can communicate by this protocol. NB-IoT not only has low security in data transmission but also has been piloted by mobile operators and has limited support [29].

Orandi [28] summarized IoT challenges in Iran as follows: (1) The provision of the necessary technical infrastructure has been challenged by international sanctions, (2) there is no proper standardization for smart advice, (3) the lack of separation of smart goods from non-smart goods by customs has created many problems for actors in this field, (4) government institutions and organizations do not function in an integrated way to develop the Internet of Things, (5) the rights and ownership of data collected in IoT are not specified in the country, (6) the role of private sector investment and participation in large national projects is very small, (7) cumbersome rules are in some cases an barrier for IoT developers, and (8) the very important role of universities and research centers in the development of IoT technology has not been considered.

### **3. Digital agriculture in Iran**

Iranian agriculture is in the second stage of the agricultural revolution and is transitioning from the second to third generation agriculture. In recent years,

*Digital Agriculture in Iran: Use Cases, Opportunities, and Challenges DOI: http://dx.doi.org/10.5772/intechopen.103967*

**Figure 5.** *The share of different sectors of the digital economy (a) and digital agriculture (b).*

extensive efforts have been made to develop the technologies of the fourth generation of the industrial revolution in the agricultural sector. All products produced in the third layer of Iran's digital economy are divided into eight branches: digital health, digital education, intelligent transportation systems, smart home, digital agriculture, digital tourism, fintech, and cyber security, showing digital agriculture with 117 products (8.6%) of all manufactured products is in the seventh place (**Figure 5a**) [23].

The statistics presented do not show the total products of the digital economy [23] but give an overview of the digital agriculture situation in Iran. In the world, digital agriculture is not as prominent as other sectors of the digital economy. The distribution of products in different agricultural sectors shows that most products are related to the marketplace (61.5%) and agricultural intelligence (21.4%), respectively, which includes 82.9% of the total products (**Figure 5b**). Agriculture intelligence includes smart agriculture, smart animal husbandry, smart poultry, smart farm, smart irrigation, and smart aquaculture. This statistic is not clear and transparent because greenhouse smartening is perhaps the most important part of smartening in Iranian agriculture, which in this statistic is probably a subset of smart agriculture. On the other hand, companies that produce greenhouse automation products are also active in smartening mushroom breeding halls, poultry, and livestock farms. Of course, as mentioned before, this statistic can show the ratio of different products in the digital agriculture sector. To study digital agriculture in Iran, we survey smart greenhouse, smart irrigation, drones in agriculture, and robotics.

#### **3.1 Digitalization toward smart greenhouses**

Iran's greenhouse area increased from 600 hectares in 1996 to 15,700 hectares in 2019. In the last decade, the average annual growth of the greenhouse cultivation area

#### **Figure 6.** *Automation and control of greenhouse using PLC.*

in Iran has been about 1000 hectares. The development of greenhouses has made it attractive to invest in related industries, including greenhouse automation. In many cases, agricultural graduates have priority for the greenhouse construction, or an agricultural expert is required to work as a greenhouse consultant. Due to the employment of agricultural graduates, the demand and acceptance of new technologies in the Iranian greenhouse industry are easier than in other parts of it.

Studies have been conducted on the greenhouse automation system manufacturing and evaluation in Iran [30–32]. AS the evaluation of commercial greenhouse automation systems has not been carried out in Iran and, there are no data in this regard, to check the status of greenhouse automation systems, in addition to visiting some greenhouses with automation, interviewing was done with some manufacturers and greenhouse owners. In Iran, due to the existing market, several companies are currently making the automation systems of climate, feeding, irrigation, carbon dioxide injection, and lighting for greenhouse. The performance of automation systems can be evaluated from both hardware and software (control algorithm used in them). In terms of hardware design, manufactured systems are generally based on PLC (Programmable Logic Controller) (**Figure 6**), and manufacturers rarely design their specific electronic boards for greenhouse automation systems.

The reason is that the market is practically small because not all greenhouses request the installation of an automation system, and as long as companies are not sure that they have the right number of orders, the design and implementation of the board is not economically justified. One of the first companies that make its specific electronic boards is not able to send SMS (Short Message Service) to its clients with 3G or 4G of wireless mobile telecommunications due to the old hardware of the board and lack of updates. Also, the operation of the electronic boards is not stable and sometimes issues error commands. In many greenhouse climate control systems, the central controller communicates by the sensors and actuators via wires (**Figure 7**).

**Figure 7.** *Demonstration of sensor placement inside greenhouse environment.*

#### *Digital Agriculture in Iran: Use Cases, Opportunities, and Challenges DOI: http://dx.doi.org/10.5772/intechopen.103967*

The manufacturers believe the metal structure of the greenhouse blocks the wireless connection like a shield or reduces the antenna's field of view. Also, the height and the moisture content of the plant, as well as the ambient humidity, can damage wireless data transmission. Rezvani et al. [33] also pointed out that water in the high amount of biomass of the plants damps the radio signals and avoids communication distances over long ranges. Of course, the poor performance of wireless sensors in some projects has affected the mindset of greenhouse owners. Approximately one sensor is installed per 1000 square meters. But the number of temperature and humidity sensors is not equal, and the number of temperature sensors is almost twice as many as that of the humidity sensors. As a result, the relative humidity distribution or vapor deficit pressure (VPD) cannot be monitored like the temperature at the greenhouse surface. The ability to connect and control greenhouse equipment with the Internet (Internet of Things) and send messages via SMS to the operator is available in almost all greenhouses with automation. Of course, in some cases, by disconnecting the server of the support company, sending the text message to the mobile is practically stopped.

In large greenhouses (two or three hectares), the transmission of sensors data through wiring creates operational problems and increases the number of masters for data collection and processing. Using this method is very costly and time consuming, especially in places where the distance between sensors and actuators to the central board is long, and for this reason, researchers always try to reduce the consumption of wires and cables by using specific methods. One of the methods is to use a bus line so that all sensors and actuators are connected to the central board through a single cable [34]. The transmitter and receiver system or network connection are the most useful controlling method, especially effective for control operations that require data collection from different points in large areas [9, 17]. The main of remote technologies is the ability to be controlled by an intelligent remote-control system and Internet connection module. The remote-control systems have limitations in use and cannot be used easily and cheaply for all control purposes, especially the needs of the greenhouse. Therefore, researchers such as Jalilian et al. [35] use a wireless sensor network for designing greenhouse automation system.

Javadi Moghadam [31] successfully used the Zigbee transceiver to send data from temperature and humidity sensors in the greenhouse to Arduino boards for monitoring and IoT purposes. The climate control system was divided into three types of hardware including node, sensor, actuator, and central control or hub. The system consisted of two sensor nodes and, on the microcontroller board, a transmitter module was installed that was responsible for sending data to the central board. The sensor nodes used the Arduino Mega 2560 board, which contained a microcontroller with an AT Mega 2560 processor. An XBee S2 transceiver was used in each sensor so that it was possible to create cloud sensors. The temperature and humidity sensor used in each sensor node was DHT 22. The temperature and humidity data were called through a sensor connected to the board and sent to the central system via a radio transmitter (**Figure 8**). The range of the transceiver model is about 25 to 30 meters, which can be increased by about 10 meters by changing the UFL antenna to SMA.

One of the problems in greenhouse climate control systems is the lack of in-depth knowledge of greenhouse climate parameters and the interaction between the plant and the environment by the manufacturers of greenhouse automation systems. In almost all cases, the setting points include only temperature and relative humidity and no VPD control. The control algorithm is often on and off, and the PID (Proportional Integral Derivative) is not used. For this reason, sometimes available systems do not

**Figure 8.** *An prototype automation system for small-scale greenhouses [31].*

work well. Also, despite the use of the Internet of Things and metadata space, it is not provided to the user in an analyzed form. In limited cases, the algorithm for controlling the climatic parameters of the greenhouse has an error. Other problems include the lack of structures and suitable climate control equipment. If the ratio of ventilator opening to greenhouse floor area is not enough or climate control equipment such as heating and cooling systems are not appropriately designed and implemented or do not have the correct location, greenhouse automation systems will not work well.

### **3.2 Smart irrigation**

Smart irrigation in agricultural fields is being developed in three approaches. In the first method, a platform is used for collecting data such as water right, soil properties, water source discharge, cultivation pattern, crop characteristics (length of cultivation period, crop coefficient), cultivation area (using satellite maps, Google Earth), irrigation system, irrigation frequency and costs, and revenues. Preparation and processing of meteorological information anywhere using interpolation from synoptic meteorological stations located in and around the zone, finally information analysis and estimation of required water and irrigation schedule offer and estimated yield to the farmer, are via SMS or website (**Figure 9**) [36]. In the second method, sensors of soil moisture, temperature and relative humidity of the environment, and wind speed are installed in the field (**Figure 10**). The amount of plant evapotranspiration is calculated by receiving environmental information by sensors and online data of the meteorological stations. The amount of crop water requirement is calculated based on the field climatic conditions, irrigation frequency, and type of cultivation. The farmer can irrigate his farm automatically or manually.

The system is based on IoT, and the user can log in to the system website online at any geographical point and while viewing a variety of graphic reports, he can be aware of the system's operational status and control irrigation remotely with his mobile phone. The sensors used in these systems are not wireless.

The third method is based on mobile application or device and like the first method can be used to calculate water needs with field data and meteorological data, but it is possible to install various sensors such as soil moisture or temperature and relative humidity of the environment. The system can perform the calculation based on the data collected from the sensors. In regions where the Internet is not available, the data are transferred to the mobile phone or device via Bluetooth, and after arriving the area where the Internet is available, and the data are analyzed and made available to the

*Digital Agriculture in Iran: Use Cases, Opportunities, and Challenges DOI: http://dx.doi.org/10.5772/intechopen.103967*

#### **Figure 9.**

*Screenshot of the homepage of the ibbrain.com, the first real smart irrigation for Iran [36].*

#### **Figure 10.**

*Equipment (a) and monitoring (b) of the second approach smart irrigation.*

user. The system also uses artificial intelligence and learning machines for better estimates.

Most farmers are not familiar with information knowledge, and the high cost of installing smart hardware-based irrigation systems on farms and their maintenance along with a small area of farmland and orchards and lack of full Internet coverage in rural areas make it difficult to develop smart irrigation systems. The mentioned

factors have led to the development of platform-based approaches that do not require the installation of any hardware on farms and determine the volume of water and irrigation schedule from meteorological information and soil water balance. The costs of this method are much lower. In platform-based approaches, all the data are analyzed on the server and then provided to the farmer, and in case of interruption or failure of the server, the user's access to information is cut-off. Of course, there are backup servers, but due to exchange rate fluctuations, companies have problems renting servers or providing services.

#### **3.3 Robotics**

Although robots are not used in the agricultural sector of Iran, there is some research on the ir use in farming [37–39]. In Iran, a lot of research has been done on the development and efficiency of agricultural robots, especially in greenhouses. One of the fields of robotic research in greenhouses is a positioning system that can be classified as follows [38, 40]: Odometry; Inertial Navigation; Magnetic Compasses; Active Beacons; Global Positioning Systems; Landmark Navigation; and Model Matching. The positioning system was the most important research on agricultural robots, especially greenhouse robots, and is still one of the most important issues related to greenhouse robotics in Iran. Greenhouse sprayers are another research field on the usage of greenhouse robots [41]. Maneuvering and controlling these bots has created a fundamental challenge in greenhouse robots. Hence, researchers like [42] tried to solve this problem using mechanical manipulation robots.

Masoudi et al. [39] designed and constructed a three-wheel differential steering vehicle to act as the greenhouse sprayer (**Figure 11a**). Power was transmitted from two DC motors to two drive wheels through a gearbox and shaft system. A proportional controller was developed and tested to control the left and right motors, which navigated the aisles using the information provided by ultrasonic sensors. The robot was tested inside a greenhouse along a U-shaped path 0.98 m in width. Spraying, safety, and obstacle detection units of the vehicle were evaluated. The accuracy of the spray function was 99.47% and, the no-spray function was 99.92%, which is acceptable for greenhouse applications.

Haidari and Amiri Parian [38] designed and constructed a four-wheel differential steering mobile robot to act as a greenhouse robot (**Figure 11b**). The robot navigation was evaluated at different levels and actual greenhouses. The robot navigation algorithm was based on path learning so that the route was stored in the robot memory

**Figure 11.** *Robots designed by Masoudi et al. [39](a) and Haidari and Parian [38] (b).*

**Figure 12.** *Agricultural drone is spraying a farm [44].*

using a remote control based on the pulses transmitted from the wheel encoders; then, the robot automatically traversed the path.

Gezavati, et al. [37] designed and built a precision seed planting robot for planting trays. First, based on the parameters designed in the laboratory, a prototype of wind seed planting was simulated by SolidWorks design software, and it was then constructed and evaluated for tomato seed planting. The planter consists of several parts operating harmoniously to yield the desired results. These parts include a chassis and conveyor belt mechanism, primary and secondary fertilizer tanks, squashing unit, seed metering device, and vibrating reservoir of the seed. The results showed that the nominal capacity of the seed robot was between 17,000 and 35,000 cells per hour. The accuracy of the designed system was 88% on average, and the nominal seed planting capacity of the system was 170 trays per hour. Drones have also been considered a specific field in agricultural robots. Shahrooz et al. [43] developed drone research to spray agricultural land in Iran. The production and sale of these robots require strong companies with appropriate services and support. Agricultural drones are often used for spraying and foliar spraying of farms (**Figure 12**). The most important problem of using UAVs (*Unmanned Aerial Vehicle*) is the price and depreciation of lithium polymer batteries. Security restrictions on obtaining flight permits are another problem with the use of drones in agriculture. The Ministry of Agriculture-Jihad supports the use of UAVs in the agriculture sector, and thus, UAV market is developing.
