**1. Introduction**

Deficiency of water resources and arable land along with global climate change are the main limiting factors for feeding the growing population in the world. The per capita arable land worldwide from 1961 to 2018 decreased from 0.361 hectares to 0.184 hectares (97% reduction), and in Iran, the per capita arable land decreased from 0.666 to 0.179 hectares (272% reduction). The per-person renewable water in the world from 1962 to 2017 decreased from 13,407 to 5724 cubic meters (134% reduction) and in Iran from 5570 to 1593 cubic meters (250% reduction) [1]. According to the FAO, the world's population will reach 10 billion by 2050, and with moderate economic growth, the need for food will increase by 50% compared to 2013. The scarcity of production resources and reducing environmental impacts have necessitated the need to increase the productivity of the resources.

According to UNCTAD's 2019 report, the share of digital economy in relation to Iran's GDP rose from 2.2% in 2012 to 6.5% in 2020. Precision agriculture makes it possible to increase the productivity of production factors and reduce the environmental risks. As defined by the International Association for Precision Agriculture [2]: *"Precision Agriculture is a management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production."* The evolution of precision agriculture has been made possible through the automatic collection, integration, and analysis of data silos previously isolated from the field, equipment sensors, and other third-party sources, using Industry 4.0 intelligent and digital technologies, leading to Agriculture 4.0 (or Digital Agricultural) [3]. From 12,000 years ago, when the agricultural revolution led to the settlement and the emergence of civilizations, to about one hundred years ago that the agricultural mechanization revolution took place, the changes were slow. The use of modified and agrochemical products developed in the 1960s, which was completed with the advent of genetic technology in the last decade of the past century [4]. Digital agriculture by Internet of Things (IoT), cloud computing, and Big data analysis collected and analyzed the required data from the farm by sensing, data management, data processing, and data enhancement. The analyzed results for decision making or activation were provided to farmers, agricultural robots, automation, or decision support systems [5–7]. The digital agricultural revolution will change not only farm operations but also every part of the value chain of agricultural products [4]. Digital agriculture has provided the possibility of generating knowledge to support the farmer in the decision-making process in the farm enterprise.

Digital agriculture brings the possibility of higher output with lower input resources by providing tools and methods for measuring the environment, processing information and accurate operations in combination with an integrated digital system with market status information, communication between stakeholders, interaction with buyers of products, and agricultural service providers giving the farmer ability to get the most out of the market [5]. Based on wireless sensor, and positioning technologies, data analysis solutions, mobile applications, and web-based solutions, the main technologies used in digital agriculture are sensor-based field mapping, wireless crop monitoring, climate monitoring and forecasting, stats on-farm production, monitor wireless equipment, predictive analytics for crop and livestock, livestock tracking and geo-referencing, and smart logistics and warehousing [5, 8, 9]. Salam A. [10] studied the barriers to the acceptance of digital agriculture found out the main obstacle is the return on investment. The next hurdle is the lack of attention to small farm owners in the digital technology business and the focus on large farms. In addition to the diversity of digital farming technologies in the fields of topography and soil texture and the lack of decision tools for the enormous data being generated from the farm, decision-making is very time consuming for farmers. They prefer to make decisions based on their experience. Other barriers to accurate trade are cost and the availability of specialists for complex equipment, lack of manufacturer support, difficulty in putting up encompassing high value, and precision portfolios. Because of these barriers, the digital farming business is not profitable. Da Silveira et al. [11] identified 25 barriers to the development of agriculture 4.0 and, in order of importance classified them into five dimensions: technological, social, political, economic, and environmental, respectively. A review of articles on barriers to the development of agriculture 4.0 showed that the key issues were incompatibilities between technological

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

components, concerns about issues of reliability, technological complexity, lack of infrastructure, lack of R&D and innovative business models, lack of digital skills or skilled labor, information asymmetry between agricultural production chain actors, and problems in education. Less important barriers included sustainable constraints, concerns about environmental, ethical, and social costs, interruption of existing work, age group risks, and concerns about sustainable energy sources.

According to FAO and ITU (International Telecommunication Union), some of the potential risks and barriers to e-agriculture are poor ICT (Information and Communications Technology) and e-agriculture infrastructure; accessibility and inclusivity problems due to inappropriate ICT distribution; marginalization of women in the use of ICT in agriculture; a lack of an inclusive approach with ICTs—attention to differently abled, semiliterate/illiterate users; low levels of e-agriculture best practices, customization, and personalization; high cost of e-agriculture services and the absence of sustainable business models; and the decline of public expenditure on agriculture in developing countries [12]. Bagheri and Kafashian [13] considered the challenges of precision agriculture in Iran as the smallholder and the poor financial strength of most farmers, lack of accurate information on profitability due to the use of precision agricultural technologies, low tendency of mechanization levels, lack of required facilities and equipment, lack of precision agriculture infrastructure, poor knowledge of farmers and executives in the field of precision agriculture, and lack of skilled workforce to train, use, repair and maintain equipment related to precision agriculture. The results of economic analysis based on national statistics and research conducted in Iran show that the application of precision agriculture in the current agricultural conditions reduces costs by 15–40%.

Today, with the increase of the world population, water shortage, energy, arable land, and the need to provide food, traditional agricultural methods no longer meet the food needs of the world population, and the smart farming strategy has received much attention [7, 8, 14–20]. Low productivity of the agricultural sector and limited production resources, especially water, have paved the way for the transformation of the agricultural sector with the help of digital technology in Iran. Optimal use of soil and water resources and other agricultural inputs with increasing productivity and performance is one of the most important advantages of using digital farming systems. Traditional agriculture is becoming more accurate and digital, and Iran will have to adapt to the global agricultural system. The purpose of this chapter is to study the infrastructure and current situation of some digital agriculture aspects in Iran.
