ArticleScientific reports2021
Advances in automatic identification of flying insects using optical sensors and machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 76 citations in OpenAlex.
- Combined effects of canola plant density and insecticide management strategies on flea beetle abundance, canola defoliation, and yield across the Canadian prairies.Pest management science · 2026Article
- Stratification of insect diversity and daily activity patterns in the West African virgin forest Taï assessed by entomological Lidar.Scientific reports · 2025Article
- The challenge of measuring mosquito flight performance: going beyond sterile insect technique and into transgenic and gene drive-based approaches.Open biology · 2025Review
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- A Systematic Review of Species Classification Using Deep Learning Algorithms and Gender Identification ofZoological studies · 2025Article
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- 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
- Recognizability bias in citizen science photographs.Royal Society open science · 2023Article
- Insect biomass density: measurement of seasonal and daily variations using an entomological optical sensor.Applied physics. B, Lasers and optics · 2023Article
- Perspectives for integrated insect pest protection in oilseed rape breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Review
- A Review of Automated Bioacoustics and General Acoustics Classification Research.Sensors (Basel, Switzerland) · 2022Review
- Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities.Sensors (Basel, Switzerland) · 2022Article
- Integrated pest management strategies for cabbage stem flea beetle (Global change biology. Bioenergy · 2022Review
- Automating insect monitoring using unsupervised near-infrared sensors.Scientific reports · 2022Article
- Deep learning-based system development for black pine bast scale detection.Scientific reports · 2022Article
- Identification of Flying Insects in the Spatial, Spectral, and Time Domains with Focus on Mosquito Imaging.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Worldwide, farmers use insecticides to prevent crop damage caused by insect pests, while they also rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests. In order to target pesticides to pests only, farmers must know exactly where and when pests and beneficial insects are present in the field. A promising solution to this problem could be optical sensors combined with machine learning. We obtained around 10,000 records of flying insects found in oilseed rape (Brassica napus) crops, using an optical remote sensor and evaluated three different classification methods for the obtained signals, reaching over 80% accuracy. We demonstrate that it is possible to classify insects in flight, making it possible to optimize the application of insecticides in space and time. This will enable a technological leap in precision agriculture, where focus on prudent and environmentally-sensitive use of pesticides is a top priority.
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Registered trials
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