**1. Introduction**

Agricultural production has continuously progressed from primitive techniques and tools to modern comprehensive digitized processes and systems. This evolutionary process can be presented in four main steps from Agriculture 1. To Agriculture 4.0. Agriculture 1.0 is based on simple tools, manpower, and animal forces and can be placed up to the nineteenth century. Agriculture 2.0 follows first industrial revolution and introduces various agricultural machinery operated by farmers and use of plenty chemicals. Agriculture 3.0 emerged in the twentieth century through the usage of automation and robotic techniques thanks to the rise of information and communication technologies (ICTs). Production became more efficient, and some environmental problems were reduced. In the present day, the main aims of Agriculture 4.0 are associated with the introduction of further automation and new digital technologies such as Internet of things (IoT), big data, artificial intelligence (AI), remote sensing, cloud computing, wireless sensor network in agriculture production, allowing

a transition toward smart and sustainable farming. This advanced automation and process digitalization have resulted in emergence of the precision agriculture (PA), a farming management concept that utilizes the available technology with aims to improve productivity, efficiency and profitability, quality of the crops and product, along with sustainability and the protection of the environment. Although the principles of PA have been known for more than 25 years, they became interesting to farmers in the last decade due to technological advances and the adoption of new technologies. Thanks to intensive research and technological advances, unmanned aerial vehicles (UAVs) have also undergone through tremendous technical progress over the last decade, which is why they are used today to perform a variety of tasks in many industries. The global agriculture unmanned aerial vehicles (UAVs) market is expected to reach 5,7 billion of USD by 2025. One of the promising areas of application is also the use of UAVs in PA where they are used for a whole range of tasks, from data collection to smart spraying tasks. The utilization of various technologies in PA has been extensively researched and documented in several scientific papers. Nowadays, some of the key terms related to PA are remote sensing, automated hardware, control systems, software, global positioning system (GPS) guidance, robotics, unmanned ground vehicles (UGVs), UAV, and so on.

Information technologies (ITs) used in PA and criteria for their comparison and selection, to store, recover, transmit, and manipulate agricultural data are identified in [1]. The identified IT are GPS, multimedia devices (devices that allow capturing images or videos, such as smartphones or cameras), nano sensors, remote sensors, sensors in general, unmanned aerial systems (UASs), UAV, UGV, variable rate technology (VRT), and wireless sensor networks (WSNs). A survey given in [2] includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behavior, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyze spectral images, and applications of WSN in agriculture. Authors have also proposed a smart solution for crop health monitoring based on the Internet of things (IoT) and comprising two modules, the wireless sensor network–based system to monitor real-time crop health status and a low-altitude remote sensing platform to obtain multispectral imagery. The work [3] deals with the influence of the fourth industrial revolution on PA. The revolution is expected to spur new technological innovation in six areas: artificial intelligence, robotics, IoT, unmanned vehicles, three-dimensional printing, and nanotechnology. Additionally, it will include a range of new technologies that use big data to incorporate the physical, biological, and digital worlds. Detailed analysis of UAV applications for PA is given [4], where all applications are divided into three categories: UAV-based monitoring applications, UAV-based spraying applications, and multi-UAV applications where multiple UAVs are used to accomplish a task. The application of small UAS for mapping and monitoring in PA is discussed in [5].

PA must quantify variations in soil and crop within agricultural fields, hence the following works also discuss various remote sensing technologies [6], sensor fusion [7], and deep learning techniques [8] to be able to automate processes and make decisions based on the sensor readings. Some research papers also deal with specific types of corps, such as orchard management [9], monitoring of nitrogen status of potatoes [10], detecting green weeds in preharvest cereals [11], and rice [12]. The main driver of PA was tractor GPS technology, which enabled accurate terrain mapping and meeting individual crop needs with different dosages of pesticides for different areas, depending on the reading from different sensors that can be fixed or mobile. Nowadays, ground vehicles of various types, sizes, and power sources

#### *Autonomous Aerial Robotic System for Smart Spraying Tasks: Potentials and Limitations DOI: http://dx.doi.org/10.5772/intechopen.103968*

are used to accomplish various tasks for PA purposes. Design and field evaluation of a ground robot as a new phenotyping platform that can measure individual plant architecture traits accurately over large areas at a subdaily frequency is demonstrated in [13]. Autonomous mobile robot based on a commercial agricultural vehicle chassis as a robotized patch sprayer is presented in [14], while in [15], the development of a small electrical robot intended to use for autonomous spraying is shown. In work [16] solar-powered UGV is presented that has multiple degrees of freedom positioning mechanism, and it is equipped with a robotic arm and vision sensors, which allow to challenge irregular terrains and to perform precision field operations with perception. There are many applications of solar systems used in agricultural production, and some are listed in the paper [17]. Numerous studies have been conducted, which consider heterogeneous robotic systems, mainly combinations of UGV and UAV. Ground and aerial measurements used for estimating nitrogen levels on-demand across a farm are presented in [18]. The main tasks of UGV in the context of UAV-UGV cooperation are considered in research [19]. The capability of images acquired from UAVs with multispectral cameras to detect weed patches and to support herbicide patch spraying is presented in [20]. Furthermore, the research [21] described a fleet of heterogeneous ground and aerial robots, developed, and equipped with innovative sensors, enhanced end effectors, and improved decision control algorithms to cover a large variety of agricultural situations.

UAVs have been used in a wide range of applications to support digital agriculture, including field scouting [22], precision management of oil palm plantation [23, 24], estimating plant's parameters such as leaf area index and height [25], health assessment [26], and variable rate spraying [27, 28]. The technologies of aerial electrostatic spraying using UAVs are being investigated [29], as well as the development of automatic aerial spraying systems based on UAVs [30, 31]. The design of an embedded real-time UAV spraying control system, based on low-cost hardware, which supports onboard image processing, is proposed in [32]. The use of computer-controlled swarms of UAVs for crop spraying enables nonuniform coverage of high precision and time efficiency, therefore an algorithmic control method for autonomous UAV swarm spraying is proposed in [33]. The static configuration usually adopted in the literature deals with the development of spraying processes have shortcomings in terms of changing weather conditions (e.g., sudden changes of wind speed and direction). To overcome this deficiency, in paper [34], an adaptive approach for UAV-based pesticide spraying in dynamic environments is presented. Also, in the paper [35], an algorithm for adjustment of the UAV route with respect to changes in wind intensity and direction is described, input of which is the feedback obtained from the WSN deployed in the crop field. Furthermore, the influence of windward airflow and droplet size on the movement of droplet groups is investigated. In [36], a numerical simulation and computational fluid dynamics analysis on spray drift movement are conducted for multirotor UAVs. Since the different spray requirements are possible, the variable spray system, which can rapidly adjust the flow range of the nozzle, is presented in [37]. The key problem in the task of smart spraying using drones is the distribution of droplets, so many scientific papers have been published on this topic [38–40].

In this chapter, a concept of an autonomous aerial robotic system intended for smart spraying tasks is presented. The presented system consists of a mobile base station and a multirotor UAV armed with spray equipment and a spraying tank. The main purpose of the concept is autonomous execution of spraying tasks on parcels of different surface ranges. The advantages and current problems related to the use of UAVs in smart spraying tasks are stated, and guidelines for the design of the base station are given.

Since multirotor UAVs are characterized by high energy consumption, special emphasis is placed on the characterization and adequate selection of components in order to obtain satisfactory flight performance and necessary flight duration. Furthermore, the aircraft system is divided into four subsystems (equipment and payload, electric energy, electric propulsion, and control subsystem), thus achieving a certain degree of modularity. In the last part of the paper, guidelines for designing a real system through the phases of characterization, analysis, and simulation are presented.
