Authors
Mariano González García, Juan Carlos Noguera Polania, Rosana González Combarros, José Ignacio López Sánchez
Published in
Pest management science. Oct 11, 2026. Epub Oct 11, 2026.
Abstract
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.
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.
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.
PMID:
42860703
Bibliographic data and abstract were imported from PubMed on 12 Oct 2026.
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