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.
This paper presents a portable computer vision system, that is able to attract and detect live insects. More specifically, the paper proposes detection and classification of species by recording images of live moths captured by a light trap. A light trap with multiple illuminations 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, based on deep learning analysis of the captured images then tracked and counted the number of insects and identified moth species. This paper presents the design and the algorithm that were used to determine and identify the moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5,675 images per night. A customized convolutional neural network was trained on 864 labelled images of live moths, which were divided in to eight different species, achieving a high validation F 1 -score of 0.96. The algorithm measured an average classification and tracking F 1 -score of 0.83 and a tracking detection rate of 0.79. This result was based on an estimate of 83 individual moths observed during one night with insect activity in 122 minutes collecting 6,000 images. Overall, the proposed computer vision system and algorithm showed promising results in nondestructive and automatic monitoring of moths as well as classification of species. The system provides a cost-effective alternative to traditional methods, which require time-consuming manual identification and typically provides coarse temporal solution to capturing data. In addition, the system avoids depleting moth populations in the monitoring process, which is a problem in traditional traps that kill individual moths. As image libraries grow and become more complete, the images captured by the trapping system can be processed automatically and allow users with limited experience to collect data on insect abundance, biomass, and diversity. (Kim Bjerge) measures. For many taxa, long-term population census data are also non-existing and more extensive monitoring. Thus, more especially of species rich taxa, is critically needed.More than half of all described species on Earth are insects. With more than 160,000 described species, the insect order Lepidoptera, which consists of moths (95%) and butterflies (5%), is one of the largest. Moths are important as pollinators, herbivores and prey for e.g. birds and bats. Reports of declining moth populations have come from Great Britain Fox et al. (2013) Sweden Franzén andJohannesson (2007) and the Netherlands Groe-
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