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This review evaluated automated insect detection, counting, classification and monitoring technologies, focusing on machine\u2010learning integration, field transferability, and operational readiness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>A PRISMA 2020\u2010guided 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\u2010text eligibility assessment, 116 publications were retained. The evidence encompassed camera\u2010 and vision\u2010based systems; acoustic, infrared, and optoelectronic sensors; electrostatic and physical approaches; non\u2010lethal photonic monitoring; radar and biosensing technologies; Internet of Things\u2010enabled platforms; and machine\u2010 and deep\u2010learning methods. Camera\u2010 and vision\u2010based systems were among the most extensively documented and field\u2010oriented approaches, particularly when integrated with remote image acquisition, communication networks, and automated classification. Acoustic systems provided complementary options for continuous or low\u2010power monitoring, whereas radar, biosensing, and other emerging approaches broadened applications toward biodiversity assessment, large\u2010area 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\u2010field trials, field validations, and operational deployments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>\n                      No single technology is optimal for all monitoring objectives. Operational adoption requires standardized task\u2010specific reporting, independent and sustained field validation, representative datasets, improved transferability across crops and environments, and system\u2010level assessment of robustness, energy autonomy, connectivity, maintenance, and cost. Sensor fusion, edge artificial intelligence, and domain\u2010adaptive or uncertainty\u2010aware models are promising when they provide measurable decision\u2010support benefits. \u00a9 2026 The Author(s).\n                      <jats:italic>Pest Management Science<\/jats:italic>\n                      published by John Wiley &amp; Sons Ltd on behalf of Society of Chemical Industry.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ps.71365","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T05:14:55Z","timestamp":1791782095000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic insect identification, counting, and monitoring techniques: a review"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5617-493X","authenticated-orcid":false,"given":"Mariano","family":"Gonz\u00e1lez 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