Two South African students reach for the Blue Sky Awards
Two Master's in Engineering students from Stellenbosch University, Chris Erasmus and Yasmin de Raay, have advanced to the international round of the 2025 Blue Sky Young Researchers and Innovation competition.
Their projects impressed the organisers of the local leg of the competition, the Paper Manufacturers Association of South Africa (PAMSA). First-placed Erasmus won R15,000, and runner-up De Raay was awarded R10,000.
The International Council of Forestry and Paper Associations (ICFPA) manages the annual competition. It considers projects from researchers under 30 working in forest-based science, products using wood, pulp or paper as a raw material, process improvements, or other innovations throughout the forest sector value chain.
The projects will now be judged against those from the United States, Canada, Europe, and Australia. An international panel will select the top three global finalists, who will then present their projects at the ICFPA CEO Global Roundtable in May 2025 in New York.
The submissions focused on monitoring aspects of tree health, which plays a critical role in tree resilience and resource optimisation by using technology to capture and analyse data continuously and remotely.
Erasmus, completing a Master's in Electrical and Electronic Engineering, has developed a wireless dendrometer and environmental sensing system tailored for the forestry industry. This solar-powered device accurately tracks tree growth patterns, water dynamics, and environmental stressors such as temperature, humidity, and soil moisture. It offers a cost-effective, low-maintenance solution, reducing the need for human intervention while ensuring consistent accuracy and reliability.

De Raay is pursuing a Master's degree in Industrial Engineering, specialising in agritech, with a strong focus on integrating technology and nature conservation.

Her project introduces a mini rhizotron system to monitor root growth and dynamics in forestry plantations remotely. Using machine learning techniques and taking microscopic photos of roots, the system provides continuous, cost-effective access to vital root growth data, even in remote field settings, improving resource optimisation without sacrificing quality.
