Next Article in Journal
Sensors Integrated Control of PEMFC Gas Supply System Based on Large-Scale Deep Reinforcement Learning
Previous Article in Journal
A Time-Frequency Measurement and Evaluation Approach for Body Channel Characteristics in Galvanic Coupling Intrabody Communication
Article

An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

1
School of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
2
NaturConsult, Skrænten 5, 9520 Skørping, Denmark
3
Department of Bioscience and Arctic Research Centre, Aarhus University, Grenåvej 14, 8410 Rønde, Denmark
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 343; https://doi.org/10.3390/s21020343
Received: 18 November 2020 / Revised: 20 December 2020 / Accepted: 30 December 2020 / Published: 6 January 2021
(This article belongs to the Section Intelligent Sensors)
Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths. View Full-Text
Keywords: biodiversity; CNN; computer vision; deep learning; insects; light trap; moth; tracking biodiversity; CNN; computer vision; deep learning; insects; light trap; moth; tracking
Show Figures

Figure 1

MDPI and ACS Style

Bjerge, K.; Nielsen, J.B.; Sepstrup, M.V.; Helsing-Nielsen, F.; Høye, T.T. An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning. Sensors 2021, 21, 343. https://doi.org/10.3390/s21020343

AMA Style

Bjerge K, Nielsen JB, Sepstrup MV, Helsing-Nielsen F, Høye TT. An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning. Sensors. 2021; 21(2):343. https://doi.org/10.3390/s21020343

Chicago/Turabian Style

Bjerge, Kim, Jakob B. Nielsen, Martin V. Sepstrup, Flemming Helsing-Nielsen, and Toke T. Høye 2021. "An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning" Sensors 21, no. 2: 343. https://doi.org/10.3390/s21020343

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop