What Can Africa Learn from Leading Countries' Paths to Unmanned Aerial Vehicle Technology?
*Names in bold indicate Presenter
We begin our analysis by defining leading countries in the area of UAV technology according to the number of publications (both paper and patent). According to the number of UAV-related patent applications, East Asia (in particular, China, Korea, and Japan), North America (United States and Canada), Europe (in particular, United Kingdom, France, and Germany), Russia (and Ukraine), and Israel are major players in the technology market. We then employ technology S-curve (or technology life-cycle) approach, using year-to-year publication data, and identify exponential functions that fit best each leading country’s S-curves.
Unlike leading countries, African countries have few publications that are directly related to UAV, providing too small of a sample for S-curves. To solve this problem, we break UAV down into its technological components and categorize them according to the engineering disciplines required to produces them. The essential technology parts (TPs) for a UAV include:
- TP 1 - aeronautical/material part (for which knowledge is required in aerospace, mechanical and manufacturing engineering, and materials science)
- TP 2 - power/propulsion part (requiring dexterity in energy & fuels, electrochemistry, and chemical engineering); and
- TP 3 - electronic/operation part (including, electrical & electronic engineering, computer science, telecommunications, industrial engineering, automation, remote sensing, imaging science & photographic technology).
Based on this analysis, we follow Frenken and Leydesdorff (2000) and Yeo et al. (2015)’s approaches, highlighted by Kullback-Leibler divergence (Dkl) and critical transition (Tc). Based on those approaches, we calculate year-to-year differences in the shares of aforementioned TPs that are essential for UAV, and transition points that are critical for UAV technology emergence. As a result, we find times at which the TPs needed for UAV are emergent and in which countries.
Lastly, we compare leading countries’ publication patterns with those of African countries. To understand the implications of the technology paths for Africa, we seek to match S-curves of leading and African countries, and identify Dkl and Tc of African countries. Findings will help shed light on which technology path African countries should pursue, in which TP to invest and/or from which other country to obtain the technology. Understanding how to capitalize on agricultural UAV technology may help alleviate food insecurity in Africa.