A compilation of UAV applications for precision agriculture

By Panagiotis I. Radoglou-Grammatikis, Panagiotis G. Sarigiannidis, Thomas D. Lagkas, Ioannis D. Moscholios. Comput. Networks, 2020

835 citations recorded by the source corpus.

Abstract Climate change has introduced significant challenges that can affect multiple sectors, including the agricultural one. In particular, according to the Food and Agriculture Organization of the United Nations (FAO) and the International Telecommunication Union (ITU), the world population has to find new solutions to increase the food production by 70% by 2050. The answer to this crucial challenge is the suitable adoption and utilisation of the Information and Communications Technology (ICT) services, offering capabilities that can increase the productivity of the agrochemical products, such as pesticides and fertilisers and at the same time, they should minimise the functional cost. More detailed, the advent of the Internet of Things (IoT) and specifically, the rapid evolution of the Unmanned Aerial Vehicles (UAVs) and Wireless Sensor Networks (WSNs) can lead to valuable and at the same time economic Precision Agriculture (PA) applications, such as aerial crop monitoring and smart spraying tasks. In this paper, we provide a survey regarding the potential use of UAVs in PA, focusing on 20 relevant applications. More specifically, first, we provide a detailed overview of PA, by describing its various aspects and technologies, such as soil mapping and production mapping as well as the role of the Global Positioning Systems (GPS) and Geographical Information Systems (GIS). Then, we discriminate and analyse the various types of UAVs based on their technical characteristics and payload. Finally, we investigate in detail 20 UAV applications that are devoted to either aerial crop monitoring processes or spraying tasks. For each application, we examine the methodology adopted, the proposed UAV architecture, the UAV type, as well as the UAV technical characteristics and payload.

Subfields: Aerospace Engineering, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/1947351b4dee2a5104a2305ffd0b8ca452768bc7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Lei Shu
2. Ferdous Sohel
3. Hamid Laga

In Plant Science

1. Lei Shu
2. Mohamed Amine Ferrag
3. Leandros A. Maglaras

Citations in the corpus

• Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies (2021, background): “It is an essential factor in determining animal agitation and stress [15].”
• A Survey of Deep Learning Techniques for Weed Detection from Images (2021, methodology): “Sa et al. (2017) also applied SegNet with the pre-trained VGG-16 as the base model (source code is available here: https://github.com/inkyusa/weedNet). They trained the model by varying the number of channels in the input images. They then compared the inference speed and accuracy of different arran”
• Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges (2020, methodology): “Fixed wing UAV and rotary wing UAV are both used in precision agriculture embedded with Multipectral or thermal infrared imaging cameras [36].”