Biodiversity Information Science and Standards : Conference Abstract
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Corresponding author: Laurens Hogeweg (laurens.hogeweg@naturalis.nl)
Received: 19 Aug 2019 | Published: 20 Aug 2019
© 2019 Laurens Hogeweg, Theo Zeegers, Ioannis Katramados, Eelke Jongejans
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Hogeweg L, Zeegers T, Katramados I, Jongejans E (2019) Smart Insect Cameras. Biodiversity Information Science and Standards 3: e39241. https://doi.org/10.3897/biss.3.39241
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Recent studies have shown a worrying decline in the quantity and diversity of insects at a number of locations in Europe (
We propose a monitoring network for insects in the Netherlands, consisting of a large number of smart insect cameras spread across nature, rural, and urban areas. The aim of the network is to provide a labor-extensive continuous monitoring of different insect groups. In addition, we aimed to develop the cameras at a relatively cheap price point so that cameras can be installed at a large number of locations and encourage participation by citizen science enthusiasts. The cameras are made smart with image processing, consisting of image enhancement, insect detection and species identification being performed, using deep learning based algorithms. The cameras take pictures of a screen, measuring ca. 30×40 cm, every 10 seconds, capturing insects that have landed on the screen (Fig.
Time sequences of images were analyzed semi-automatically, in the following way. First, single insects are outlined and cropped using boxes at every captured image. Then the cropped single insects in every image were preliminarily identified, using a previously developed deep-learning-based automatic species identification software, Nature Identification API (https://identify.biodiversityanalysis.nl). In the next step, single insects were linked between consecutive images using a tracking algorithm that uses screen position and the preliminary identifications. This step yields for every individual insect a linked series of outlines and preliminary identifications. The preliminary identifications for individual insects can differ between multiple captured images and were therefore combined into one identification using a fusing algorithm. The result of the algorithm is a series of tracks of individual insects with species identifications, which can be subsequently translated into an estimate of the counts of insects per species or species complexes.
Here we show the first set of results acquired during the spring and summer of 2019. We will discuss practical experiences with setting up cameras in the field, including the effectiveness of the different set-ups. We will also show the effectiveness of using automatic species identification in the type of images that were acquired (see attached figure) and discuss to what extent individual species can be identified reliably. Finally, we will discuss the ecological information that can be extracted from the smart insect cameras.
machine learning, camera traps, biodiversity, insects
Laurens Hogeweg
Biodiversity_Next 2019