Mariano González García, Juan Carlos Noguera Polania, Rosana González Combarros, José Ignacio López Sánchez
{"title":"Automatic insect identification, counting, and monitoring techniques: a review.","authors":"Mariano González García, Juan Carlos Noguera Polania, Rosana González Combarros, José Ignacio López Sánchez","doi":"10.1002/ps.71365","DOIUrl":"https://doi.org/10.1002/ps.71365","url":null,"abstract":"<p><strong>Background: </strong>Insect pests threaten agricultural productivity, while conventional surveillance is labour-intensive and excessive pesticide use creates environmental and health risks. This review evaluated automated insect detection, counting, classification and monitoring technologies, focusing on machine-learning integration, field transferability, and operational readiness.</p><p><strong>Results: </strong>A PRISMA 2020-guided formal search covering publications from 2010 to 2024 identified 313 records relevant to the technological and methodological scope of the review. After title/abstract screening and full-text eligibility assessment, 116 publications were retained. The evidence encompassed camera- and vision-based systems; acoustic, infrared, and optoelectronic sensors; electrostatic and physical approaches; non-lethal photonic monitoring; radar and biosensing technologies; Internet of Things-enabled platforms; and machine- and deep-learning methods. Camera- and vision-based systems were among the most extensively documented and field-oriented approaches, particularly when integrated with remote image acquisition, communication networks, and automated classification. Acoustic systems provided complementary options for continuous or low-power monitoring, whereas radar, biosensing, and other emerging approaches broadened applications toward biodiversity assessment, large-area surveillance, and early detection. Reported performance was not directly comparable across publications because metrics, acquisition conditions, and validation levels differed substantially, ranging from laboratory or dataset benchmarks to semi-field trials, field validations, and operational deployments.</p><p><strong>Conclusion: </strong>No single technology is optimal for all monitoring objectives. Operational adoption requires standardized task-specific reporting, independent and sustained field validation, representative datasets, improved transferability across crops and environments, and system-level assessment of robustness, energy autonomy, connectivity, maintenance, and cost. Sensor fusion, edge artificial intelligence, and domain-adaptive or uncertainty-aware models are promising when they provide measurable decision-support benefits. © 2026 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.</p>","PeriodicalId":218,"journal":{"name":"Pest Management Science","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149163760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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