ArticleScientific reports2022
Automating insect monitoring using unsupervised near-infrared sensors.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 76 citations in OpenAlex.
- Automated long-term field monitoring reveals diel and seasonal activity patterns of Drosophila suzukii.PloS one · 2026Article
- Lidar reveals distinct insect daily activity and diversity between habitats.Scientific reports · 2025Article
- Deadliest Animals with the Thinnest Wings: Near-Infrared Properties of Tropical Mosquitoes.Applied spectroscopy · 2025Article
- Insect Decline in the Anthropocene: Historical Parallels and Emerging Monitoring Tools.Insects · 2025Review
- Automated insect detection and biomass monitoring via AI and electrical field sensor technology.Scientific reports · 2025Article
- Stratification of insect diversity and daily activity patterns in the West African virgin forest Taï assessed by entomological Lidar.Scientific reports · 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
- Integrated Pest Management: An Update on the Sustainability Approach to Crop Protection.ACS omega · 2024Review
- Towards global insect biomonitoring with frugal methods.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Temperature Dependency of Insect's Wingbeat Frequencies: An Empirical Approach to Temperature Correction.Insects · 2024Article
- An optical system to detect, surveil, and kill flying insect vectors of human and crop pathogens.Scientific reports · 2024Article
- Article
- Discrimination of Hover Fly Species and Sexes by Wing Interference Signals.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest.Scientific reports · 2023Article
- Monitoring the abundance of flying insects and atmospheric conditions during a 9-month campaign using an entomological optical sensor.Scientific reports · 2023Article
- A New Approach for Detecting Sublethal Effects of Neonicotinoids on Bumblebees Using Optical Sensor Technology.Insects · 2023Article
- Dual-Band Infrared Scheimpflug Lidar Reveals Insect Activity in a Tropical Cloud Forest.Applied spectroscopy · 2023Article
- Remote Nanoscopy with Infrared Elastic Hyperspectral Lidar.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Insect biomass density: measurement of seasonal and daily variations using an entomological optical sensor.Applied physics. B, Lasers and optics · 2023Article
- From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring.Frontiers in plant science · 2023Review
Corrections and comments
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Authors and funding
14 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. Conventional monitoring methods of trapping and identification are time consuming and thus expensive. Automation would significantly improve the state of the art. Here, we present a network of distributed wireless sensors that moves the field towards automation by recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor's capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman's rank correlation coefficient of 0.61 and a p-value = 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional yellow water trap monitoring.
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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.