Recognize the Little Ones: UAS-Based In-Situ Fluorescent Tracer Detection

Abstract

In ecological research, a key interest is to explore movement patterns of individual organisms across different spatial scales as one driver of biotic interactions. While various methods exist to detect and record the presence and movements of individuals in combination with UAS, addressing these for smaller animals, such as insects, is challenging and often fails to reveal information on potential interactions. Here, we address this gap by combining the UAS-based detection of small tracers of fluorescent dyes by means of a simple experiment under field conditions for the first time. We (1) excited fluorescent tracers utilizing an UV radiation source and recorded images with an UAS, (2) conducted a semi-automated selection of training and test samples to (3) train a simple SVM classifier, allowing (4) the classification of the recorded images and (5) the automated identification of individual traces. The tracer detection success significantly decreased with increasing altitude, increasing distance from the UV radiation signal center, and decreasing size of the fluorescent traces, including significant interactions amongst these factors. As a first proof-of-principle, our approach has the potential to be broadly applicable in ecological research, particularly in insect monitoring.

Advances in insect pest and disease monitoring and forecasting in horticulture

“…Mini drones have even been developed as artificial predators for monitoring and killing moths in greenhouses (https://pats -drones .com/). For research purposes, drones equipped with UVA sensors could be used for detecting insects treated with fluorescent substances in ecological studies, for example, for finding out how insects move in their habitats or between habitats (Teickner et al, 2019). Fixed traps equipped with sensors measuring abiotic conditions in the monitoring area contribute to data collection on factors influencing pest occurrence and reproduction and may eventually be integrated with forecasting models for pest occurrence and population dynamics.…”