Drone-based Solution for leaf sampling in primary tropical rainforests

Project Motivation

The ability to collect intact leaf specimens from trees in highly diverse tropical rainforests is important for identifying tree species and understanding their different responses to human impact, as well as to provide biological specimens for research on ecophysiology and foliar nutrients. The latter usually require leaves that are exposed to sunlight, as opposed to in the shade. However, collecting leaves from tropical rainforests is challenging, given that the canopy and emergent trees are about 45 to 70 meters tall – beyond the reach of conventional collection method such as the extension pruner and slingshots. Moreover, tree density in tropical rainforests is high and crowns of trees in these rainforests are often overlapping.

Design Solution

In this project, a proof-of-concept prototype of a drone-based solution for leaf sampling was developed. It comprises a remotely-operated sampling tool with a collapsible boom which is carried by a drone to a target tree in order to collect samples from the topmost branches of the tree. Such a solution has the potential to reduce the labor required and improve the efficiency of the leaf sampling process. Results from flight tests of the prototype has demonstrated the ability of the proposed solution to meet its design requirements, providing a quick and effective sampling method for botanists to collect leaf samples.

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Awards

  • College of Design and Engineering Innovation & Research Award (High Achievement) 2022
  • NUS Outstanding Undergraduate Researcher Prize 2022

Project Team

Students:

  • Bryan Lim Min Kwang (Engineering Science, Class of 2022)
  • Loh Yun Ying (Electrical Engineering, Class of 2022)
  • Oung Yong Sheng Kennedy (Computer Engineering, Class of 2022)
  • Tan Sian Hern, Ivan-Darien (Mechanical Engineering, Class of 2022)

Supervisors: