Design and Implementation of an Amphibious Unmanned Aerial Vehicle System for Agriculture Applications

Introduction

In present days, Unmanned Aerial Vehicles (UAVs) are the emerging technology in the modern technological world. The UAVs are also called Drones, and an aircraft system operated without a human pilot. The drones are smaller than the commercial aircraft system, which can be performed in an autopilot mode (Tezza and Anjudar, 2019). A ground-based controller controls the onboard electronic components of the drones via wireless communication. The drones can be operated either through remote control by a human or autonomously by programmed computers. Self-automated drones are employed in recent advancements which carried out missions without human involvement like surveillance systems, artificial intelligence, cloud computing, machine, and deep learning. In earlier days, drones were designed and implemented in military applications (Mogili and Deepak, 2018). UAVs have been used in various commercial and agricultural applications in recent days. Future UAV-based networks are needed to supply high data rates, security, range, and dynamicity. During this context, operative UAVs victimization the coming tactile web surroundings and low latency 5G networks will solve network coverage and data rates (Hayat et al., 2016).

Furthermore, the employment of computer code outlined Networking (SDN), Network performs Virtualization (NFV), and Intent-based Networking (IBN) will solve the difficulty of dynamic network management? The applications of UAVs are speedily increasing in most civilian domains. Air taxis, Food drones, drones for medication delivery are several UAVs' main civilian applications. It's imperative to verify the UAVs' genuineness and operations in such applications that Blockchain technology may be an excellent resolution. UAVs' power constraints may be self-addressed by recharging batteries on the go with solutions like star panels or wireless charging. Besides, higher algorithms may be enforced to create the UAV computations additional energy economical (Mozaffari et al., 2016). Most sensible applications of UAVs would usually need a swarm of the many drones instead of one drone. Correct management, cooperation, and autonomy of such hives would need numerous computing and Machine Learning algorithms. The geographic vary of operations, clearance of access to civilian and military airspaces, network coverage, the period of flight, security needs, autonomy, among several others, are several the problems that should be self-addressed before such applications become a reality. Broadly, these are the class of measurability and security. This special issue explores application-specific UAV platforms that use novel techniques for multiple new applications that are scalable and secure (Alzenad et al., 2017).

The UAVs are real-time systems that require a fast response to changing the sensor information. Therefore, UAVs depend on single board programmed computers for their computational necessities. The agricultural applications of drones are weather analysis, monitoring crops' healthiness, spraying fertilizers, sowing seed balls, irrigation systems, etc. The drones are classified into four types based on the type of aerial platform.

Multi-Rotor Drones

The multi-rotor drones are commercially used, easy to manufacture, and more economical. The speed and flying time of these drones are limited. It is used for standard applications like photography and video surveillance systems. The flying time of these drones is around 30 minutes. Hence these drones are not suitable for larger-scale applications.

Further, these drones are classified into four types depending on the number of rotors used. The four types of Tricopter have three rotors, Quadcopter has four rotors, Hexacopter has six rotors, and Octocopter has eight rotors. Among these types, quadcopters are widely used for commercial purposes (Mogili and Deepak, 2018)

Figure 1.

a. Tricopter; b. Quadcopter; c. Hexacopter; d. Octocopter