Thesis subject

Drone Technology and Analytics (BSc/MSc)

Drones are increasingly more popular in agri-food through digital transformation, having a hugely positive effect on effectiveness, transparency, and so on. The wide range of technologies, application areas, and analytics techniques, and given that it involves heavy up-front investment, however, make it difficult for the common user to pick a specific technology and get started. We would like to survey the state-of-the-art drone technologies and analytics and build a common drone analytics library to further endorse the use of drone technologies across Europe.

Short description

Drones, one of the more recent developments in digital technology, have the potential to significantly contribute to more effective, efficient, transparent, and secure agri-food. The global drone market is estimated to increase from USD 13 billion in 2020 to USD 41 billion by the end of 2026, at a compounded annual growth rate of 23.8%. The main reason behind the widespread use and interest in drones is the wide range of practical applications, their ability to perform increasingly complex tasks, along with the unparalleled efficiency and flexibility they offer, without damaging the environment or disrupting ecosystems. The multi-purpose application of drones presents socio-economic, environmental and regulatory challenges which limit their current exploitation across Europe. Even though drones continue to increase their popularity and become more affordable, they are still considered a pricey investment, especially in agricultural and rural areas, which are among the poorest regions in Europe.

We would like to survey the landscape of drone technologies, including the drone platforms and mounted technological components, to assist in better understanding, developing, and integrating the technology implemented in agricultural production, forestry, and the development of rural communities globally. The survey results will include information on the types of drone applications; key drone components and tools (drone modularity, payloads, ground control stations, UAV Control Systems); drone categories (e.g., fixed-wing, rotary-wing); drone data acquisition (e.g., RGB, multispectral, hyperspectral, thermal cameras); datasets released and related software.

Based on the survey results, we would like to extract the datasets and software used for drone analytics, and build a taxonomy for them. The taxonomy will help practitioners with decision-making and enable further research on drone analytics.

    Objectives

    • Review previous work on the application of drone technologies and drone data analytics
    • Extract and provide a taxonomy of datasets and software used for drone analytics

      Tasks

      The work in this thesis entails:

      • To collect full-text articles or PDFs from primary studies and SLRs in the drone technology and analytics field
      • To assess the challenges and solutions available in the literature in practice
      • To provide a taxonomy on the publicly available datasets and software for drone analytics

          Literature

          • Zhang, C., Valente, J., Kooistra, L., Guo, L., & Wang, W. (2021). Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches. Precision Agriculture, 1-46.
          • Mogili, UM Rao, and B. B. V. L. Deepak. Review on application of drone systems in precision agriculture. Procedia computer science 133 (2018): 502-509.


          Requirements

          • Courses: Programming in Python (INF-22306), optionally one or more of the analytics/machine learning courses such as Artificial Intelligence (INF-5006), Data Science Concepts (INF-34306), and Machine Learning (FTE-35306)
          • Required skills/knowledge: basic data analytics and willingness to learn new software tools, interest in drone technologies

            Key words: Drone Technology, Data Analytics, Machine Learning, Deep Learning, Remote Sensing, Computer Vision

            Contact person(s)