ArticleSensors (Basel, Switzerland)2021
An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 121 citations in OpenAlex.
- Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations.Frontiers in artificial intelligence · 2026Pooled it
- Machine learning workflows detect ground-dwelling arthropods against heterogenous backgrounds.Journal of insect science (Online) · 2026Article
- Automated monitoring of insects system: building a non-lethal and scalable solution for monitoring of nocturnal insects.HardwareX · 2026Article
- A YOLO11-MSC-Based Method for Small Pest Detection in Insect Pest Monitoring Lamp Images.Sensors (Basel, Switzerland) · 2026Article
- A structured literature review of computer vision methods for insect identification.Journal of insect science (Online) · 2026Review
- Continuous Non-Invasive Monitoring of Hive Entrance Activity Reveals Honey Bee Colony Dynamics.Biology · 2026Article
- Multi-View Pareto Optimization for Minimal-Diagnostic-Set Identification of Disease Vectors.Insects · 2026Article
- Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision.Journal of imaging · 2026Review
- Simultaneous Automated Insect Monitoring Across a Remote Tropical Elevation Gradient With Mothbox.Integrative and comparative biology · 2026Article
- Evaluating the method reproducibility of deep learning models in biodiversity research.PeerJ. Computer science · 2025Article
- The Field Automatic Insect Recognition-Device-A Non-Lethal Semi-Automatic Malaise Trap for Insect Biodiversity Monitoring: Proof of Concept.Ecology and evolution · 2024Article
- Article
- Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Towards a toolkit for global insect biodiversity monitoring.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Towards global insect biomonitoring with frugal methods.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- A novel dataset and deep learning object detection benchmark for grapevine pest surveillance.Frontiers in plant science · 2024Article
- Article
- Object Detection of Small Insects in Time-Lapse Camera Recordings.Sensors (Basel, Switzerland) · 2023Article
- Real-Time Recognition and Detection ofSensors (Basel, Switzerland) · 2023Article
- Real-time biodiversity analysis using deep-learning algorithms on mobile robotic platforms.PeerJ. Computer science · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.