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

Henning Teickner,Jan Lehmann,Florian Meinking,Patrick Guth,David Ott

doi:10.3390/drones3010020

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.

Highlights

A fundamental question driving ecological research is finding explanations that lead to species interactions and their spatial distributions, from global down to local scale [1].Today, various methods of remote sensingare used for detecting and recording the movements of organisms in their natural environments [2,3,4,5,6].these methods often require the attachment of devices on every single individual every single individual, and are time and/or cost consuming, especially for investigations of larger insect populations
With the featured classification optimization and sampling, the support vector machines (SVM) achieved high prediction values for the classification of the used fluorescent dyes leading to almost a complete identification of visually discernible fluorescent traces in the recordings (Table 1)
All tested main parameters, the distance between the camera sensor and the object level (DTS), the size of the fluorescent trace, and the distance between a trace and the center of the UV-light cone at the object level (DTL), significantly affected the identification probability: Confirming our hypothesis, (i) identification probability decreased with increasing Data Processing sensor (DTS) (Figure 4, Table 2)

Summary

Introduction

Are used for detecting and recording the movements of organisms in their natural environments [2,3,4,5,6] These methods often require the attachment of devices on every single individual every single individual, and are time and/or cost consuming, especially for investigations of larger insect populations. The use of fluorescent powder dyes was successfully applied as a non-invasive method for vertebrates [7,8,9,10] and invertebrates [11,12,13]. It proved to be an affordable method while allowing the detection of many individuals in parallel [12]. Manually searching for fluorescent powder in the field, such as residues on flowers using UV radiation flashlights, is very labor-intensive and time-consuming

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