**1. Introduction**

Topical in Nigeria today is, therefore, the intractable problem of sustainable economic growth whose precursor are people and capital formation. The proximate cause of economic growth is an increase in knowledge and its application in increasing the amount of capital or other resources per head. It is axiomatic that the motivation for agricultural mechanization is to reduce drudgery, increase productivity and return on investment and ultimately enhance capital formation for economic growth. But for a labour surplus, country like Nigeria agricultural mechanization is considered toxic to employment since the sector is responsible for the livelihood of over 70% of the population who are mostly into subsistence agriculture. Today agriculture has become a poverty incubator of the country wherein over 40% of the population are within the poverty bracket earning less than one dollar a day [1]. This is not because farming is not a profitable vocation but the native approach to the business of farming

#### *Aeronautics - New Advances*

fall below global best practice creating a poverty trap for 70% of the population with the concomitant avalanche of societal problems such as high crime rate, insurgency, hyper inequality in income distribution and unstable national structure. As Lewis [2] noted, the traditional agricultural sector is characterized by zero marginal labour productivity.

Farmers in more developed climes use sophisticated technologies such as robots, temperature and moisture sensors, aerial imaging, drones and GPS technology in the area of precision agriculture to ensure high productivity, more profit, efficiency, safe and environmentally friendly farming [3]. As a result, only 12–15% of the gainfully occupied population suffice to feed the people in developed countries [2]. In contrast and at the low level of productivity 60–70% of the gainfully occupied population is needed in agriculture for the same purpose in Nigeria. For instance, Ukrainian GDP comes from the agriculture sector that employs just around 20% of its population [4]. It, therefore, becomes imperative that all hands especially those entrusted with the responsibility for defending and building the nation must be on deck to salvage the nation.

A sure step toward this is to unleash the power of modern agricultural technology on this critical sector to free this trapped population to the various value chains of agriculture expected to be induced by higher production and productivity of mechanized farming. More importantly is that the released population will create a huge market for farm produce as they no longer depend on the below optimal subsistence farming to feed and cater for their daily need. Today this tepid population adds nothing to the off-take of agricultural products and services as they can neither afford to buy nor have felt the need for them, choosing instead to live on their meager production or go into crime.

This debilitating state of affairs demands massive attacks from both grounds tractorization and air-agricultural UAV. The development of advanced electronics, global positioning systems (GPS) and remote sensing have enabled advancements in the practice of precision agriculture whereby agronomic practices are based on variations in soil, nutrition and crop stress [5]. Fortunately, Nigeria is endowed with enormous land resources relative to its population. It will create not only jobs and market up front but also provide the adequate raw material for the various high capacity processing plants doting the country and currently operating far below-installed capacity due to lack of raw materials and limited market. Typical examples abode such as the tomato, sugar cane, rubber, oil palm, breweries, rice mills and other plants which cannot adequately source the local content of their raw material need.

The proper implementation of artificial intelligence (AI) in agriculture will help the cultivation process and create ambiance for the agricultural value chain market. Such development in Nigeria's agriculture sector will boost rural development and rural transformation and eventually result in structural transformation of the national economy.

#### **2. Unmanned aerial vehicles (UAVs)**

UAVs come in different forms and for different purposes. Purposes include military and civil applications such as firefighting, reconnaissance for natural disaster, border security, traffic surveillance etc. UAVs are deployed in agriculture in many areas such as spraying and fertilizer application, seed planting and weed recognition, diagnosis

#### *Review of Agricultural Unmanned Aerial Vehicles (UAV) Obstacle Avoidance System DOI: http://dx.doi.org/10.5772/intechopen.103037*

of insect pests and artificial pollination, irrigation assessment, mapping and crop forecasting [6]. Pedestrian classification of UAVs is by use; photography, aerial mapping, surveillance, cinematography, agricultural etc. UAVs are better categorized by type; multi-rotor, fixed-wing, single rotor and fixed wing multi rotor hybrid with varying capabilities on altitude, control range, flight endurance and air speed. UAVs are further classified by their drive to include electric; where electric battery is used, solar; where solar cells are the source of energy and internal combustion engines; where they are driven by gasoline or methanol-fueled combustion engines [7]. For agricultural purposes, Vroegindeweij et al. [8] categorized UAVs into three as—fixed wing, vertical take-off and landing (VTOL) and bird/insect. According to Banjo and Ajayi [9], hybridization resulted in higher flying altitude, wider control range, increased speed and longer flights time, factors which are at variance with the demands on Ag UAVs of low altitude, low speed but agrees with Ag UAVs need of longer flight time and wider control range. Hence, for low altitude remote sensing (LARS), most Ag UAVs are of low cost, low speed, lightweight, low payload weight capabilities and short duration. UAVs for pesticide spraying and fertilizer application, however, need to be of higher payload weight and able to support longer flight endurance [5].

#### **2.1 Agricultural unmanned aerial vehicles (Ag AUV)**

The use of aerial system is necessary not only to ensure precision in agriculture for optimum utilization of inputs and efficacy and mitigation of the environmental impact of excess application inorganic agrichemical, but also times the only mechanized means to overcome obstacles limiting ground operations such as terrain, soil compaction, swamp etc. Moreover, with an increasing knowledge-based population inclined more to the mental white-collar job than to physical blue-collar job sourcing farmhands for even routine farm operations as pest and disease control (PDC) is becoming arduous necessitating deployment of agricultural technology to achieve the desired outcome. The innovative UAV platform for farming may lure the youth to rural areas having afforded a comfortable working environment and reduced farm drudgery thereby generating employment opportunities in the rural sector which may address social balance. Piloted aircrafts are used to carry out spraying and aerial imaging on large fields in a short time but these are not readily available in all areas and so UAVs are used especially on smaller fields. UAVs are considered to have high efficiency, low labour intensity and low comprehensive cost [10].

The deployment of UAV platforms for agriculture started in 1983 when the first remote-controlled aerial spraying system (RCASS) was built, followed in 1990 by an R50 helicopter with a payload limit of 20 kg and a laser system for height determination. Currently, low-volume UAV helicopters with fully autonomous unmanned vertical take-off and landing (VTOL) and spraying capacity of up to 7.7 kg, integrated with the flight control system of the UAVs show high potential for vector control in areas not easily accessible by ground services. Some agricultural spraying unmanned helicopters now have plant protection parameters as shown in **Table 1** [7, 11].

Agricultural unmanned vehicles (Ag UAVs) are used to optimize agricultural operations, increase crop production, monitor crop growth, sensor and produce digital imagery and to give a clear picture of the status of farm to a farmer, especially on large farms. Ag UAVs have, therefore, become a prerequisite for precision agriculture toward rapid industrialization of agriculture.


#### **Table 1.**

*Plant protection parameter (QF170-18L AgriSpraying Helicopter).*

The limited scale adoption of aerial services relative to other methods of farming service tools is not unconnected with the high cost of the service, short flying time, the unreliability of the equipment, the uncertainty of the quality of operations, risk of liquid spray being airborne to unintended targets and the fact that flying of UAVs is under aviation laws [7]. Moreover, most Ag UAVs operations are done through manual remote control rendering the outcome susceptible to the skill level of the operator [5]. It is therefore imperative as a solution, to realize real-time autonomous OA technology to assuage the fears of many farmers on the safety and quality of UAVs services.

The deposition of pesticides on plants with the use of UAVs is the combined effect of the jet of liquid being sprayed and the stream of air generated by the rotors [7]. Hence, the efficacy of aerial spraying depends on the speed, droplet drift, flight altitude, weather, type of pesticide, temperature and terrain, the goal being to achieve blanket spraying or spot spaying of targets. The efficacy of the spaying is therefore subject to the behavior of the UAV airframe. The rush of air from the rotor changes because of the varying operation load of the UAV on the discharge of the spraying mix content of the tank thereby inducing a difference in the concentration of droplets in the air stream between the start, along with and end of spraying operation. Hence, the quality of UAV spraying may not meet that of manned aerial and ground systems [7].

### **2.2 Agricultural unmanned aerial vehicles (Ag AUV) obstacle avoidance technology**

The objective of this chapter is to examine the literature on existing capabilities and ongoing studies to overcome difficulties associated with the deployment of agricultural UAVs on obstacle-rich farms for pesticides and fertilizer application.

Small size, fragmentation, multiple farmlands, undulating, fallow, beast, human and meandering boundary are all characteristics of a typical Nigerian farm holding. Forested areas, tall trees, electric poles and wire, farm structures, birds and some reflecting objects, wireless networks and hot and stormy weather abound in the flying environment. Furthermore, when operating on farmland, Ag UAVs are typically 1–1.5 m above the ground, posing ground operation challenges such as small trees in the middle of the farm, stacking poles, robes, molehills, undulation terrain, out-growths and so on. Flying dust and liquid smudge the spraying farm environment, making it impossible to use visual obstacle avoidance systems. As a result, an

#### *Review of Agricultural Unmanned Aerial Vehicles (UAV) Obstacle Avoidance System DOI: http://dx.doi.org/10.5772/intechopen.103037*

autonomous system is required to manage these complex and constantly changing aerial farm environments and spraying variables.

An agricultural unmanned vehicle obstacle avoidance (Ag UAV OA) technology, according to Wang et al. [12], is the core intelligent technology that allows an agricultural drone to autonomously identify farm obstacles and complete the specified avoidance action. It is an inbuilt capacity for sensing and avoidance (S&A) of threat [13]. In sensing depth, especially of frontal obstacles, studies have been on mimicking biological systems such as motion parallax, monocular cues and stereo vision [14]. An Ag OA UAV functions, therefore, include real-time perception, rapid image analysis, intelligent identification, potential areas detection and decision making on obstacle avoidance. To this end, radar ranging, laser ranging, ultrasonic ranging, monocular and binocular vision are deployed as tools for sensing or detecting obstacles.

Sensor fusion is a process by which data from multi-sensor UAV are fused for computations in multispectral remote imaging in precision agriculture to capture both visible and invisible images of crops and vegetation. The sensors feed the data back to the flight controller which runs on obstacle avoidance algorithms for processing the image data of the scanned surrounding [15]. Many Ag UAV systems use on-site suspension, planned travel routes, and autonomous obstacle avoidance as obstacle avoidance methods after detecting an obstacle. However, Leonetang pointed out [16] that autonomous actions that require the UAVs to evade the algorithm and regenerate the route come at the cost of battery life, which may be insufficient to tackle additional obstacles during route regeneration. In agriculture, as noted earlier UAV have been primarily applied in remote sensing, crop production and protection materials, precision seeding, vegetation testing (NDVI) etc. with various types of obstacle avoidance (OA) systems under remotely or programmed flight control [5]. Initially, there were two main sense and avoid (S&A) technologies—radar that sends out radio waves and measures their reflections from obstacles and light detection and ranging LiDAR optical sensor that uses laser beams instead of radio waves to provide detailed images of nearby features [17]. UAVs control modes are categorized as linear, non-linear and learning-based by Kim et al. [6]. They opined that linear and nonlinear control systems based on linear-quadratic (LQ ) are used to control UAV and handle wind and weather but not storm and snow. Learning-based controls use a type of fuzzy logic that learned using data obtained from the flight and does not require models.

Many variants of controls and obstacle detection and avoidance devices adorn the UAV shelves and aircraft market today, virtually solving the earlier version of sensors' problems of bulkiness, weight, low energy efficiency and high cost. This includes RTK sensors, ultrasonic sensors, laser sensors, infrared sensing technology, structured light, TOF ranging, millimetre-wavelength radar, monocular visual ranging and binocular stereo vision [7]. The sensors are further supported by a variety of algorithms that enable real-time obstacle perception, rapid analysis and actionable image interpretation. These are broken down into three categories [13] to include a geometric relationship (relative distance, speed, acceleration, angle etc. between the drone and the obstacle), real-time planning (artificial potential field, ANN algorithm—a software-based approach to replicate the biological neurons, artificial heuristic, path-planning etc.) and decision making (Markov and Bayesian decision theory). In the genre of emerging OA technique is the reinforcement learning whereby the UAV selects the actions on the basis of its past experiences (exploitation) and also by new choices (exploration) such as autonomous mental development (AMD) algorithm [13] that simulates the mental development process of a human being using neural

network algorithm [18], online free path generation and navigation system [19], multi-UAVs genetic algorithm [15], and the simultaneous localization and mapping (SLAM) technology which maps in real-time, recognizing own position and identifies obstacles while autonomously traveling or performing tasks [6]. The technique of UAV swarm control is also evolving using linear and nonlinear controls with strong resistance to external influence based on the K-means algorithm (K-means clustering) to prevent collisions and another to map allocated areas. However, as Corrigan pointed out in his paper [15], the challenge of these technologies is accuracy, as measurements must be taken continuously as the UAV moves through its environment and assimilated to update the models and account for noise introduced by both the device's movement and the measurement method's inaccuracy. This task is accomplished by updating the model state variables with measured values. The state variables are updated sequentially i.e. each time an observation is available. Kalman filter is deployed in estimating the states of the systems from the sensor data, estimating variables that are not directly observable and to minimize the noise [20].

The plethora of demands on OA system according to Wang et al. [12] to ensure the safety of agricultural UAV and continuous operations at low altitude and ultralow volume spraying include the following capabilities: action response time and implementation efficiency, autonomous adjustment of flying speed, height and attitude of the drone, re-planning of flight path after obstacle avoidance, deployment of signal loss prevention and anti-magnetic field interference, decision making on single, multiple static or dynamic obstacles.

To achieve the above objectives of all-weather autonomous operation of Ag UAV, multi-sensor obstacle avoidance technology is evolving while the development of auxiliary classification (indirect identification) of farmland obstacles and standardization of UAVs processes is ongoing. In July 2015, the Republic of South Africa became the first country to implement and enforce a comprehensive set of legally binding rules governing unmanned aerial vehicles. By 2016, 15 countries had published dedicated drone regulations. Nigeria is one of the countries with legally binding rules on the use of unmanned aerial vehicles (UAVs) [21].

## **2.3 Applicability of the various UAV obstacle sensing and avoidance technologies to obstacle rich farmland**

#### *2.3.1 Real-time kinematic (RTK)*

It is more suitable for building obstacle maps of farmland than real-time obstacle avoidance. The positioning technology has not been fully applied to Ag UAV because of its high cost, difficulty of deployment and time consuming and labour intensive features [12]. However, Global Navigation Satellite System (GNSS) with RTK allows for centimeter level high position accuracy [6].

#### *2.3.2 Ultrasonic sensors*

These are used as an auxiliary safety device to get flight altitude parameters, achieve autonomous take-off and landing or fly in complex terrains at very low altitudes. Image processing is used to recognize the position of an obstacle while the ultrasonic determines the distance [22]. The obstacle avoidance principle is based on the sound wave reflected by the obstacle and measuring the echo time difference to determine the distance [16]. On Ag quadrotor UAV the ultrasonic sensors are used to detect objects

#### *Review of Agricultural Unmanned Aerial Vehicles (UAV) Obstacle Avoidance System DOI: http://dx.doi.org/10.5772/intechopen.103037*

and to calculate the distance between the obstacle and the UAV. The effective method is subsequently devised to avoid the obstacle [23]. They are not affected by light intensity, color variation and are of simple structure, low cost and easy to operate. However, its performance is limited by the small range and sound absorption, ambient temperature, humidity, atmospheric pressure and ground effect of grass in addition to losses caused by ultrasonic reflection and crosstalk between sound waves [6]. Further, acoustically, soft materials like cloth may be difficult to detect [9]. Moreover, Gibbs et al. [24] argued that their resolution can be as low as 60 degrees and cannot, therefore, identify the angular location of an obstacle in its view. Their study using two sonar receivers and scalar Kalman filter to reduce the effect of the signal noise was however salutary, especially with the energy and power spectral density metrics (out of the four signal metrics tested). Others being maximum (peak) frequency and cross-correlation of raw data and PSD. Further work by Davies et al. [25] using ultrasonic transit-time flow meters' reveals that the autonomous performance of ultrasonic sensors in-flight instrumentation of UAVs can be improved upon by optimal design of two variables—the mounting configuration and the optimal angle of incidence for the transducer mounting.
