Artificial Intelligence and Smart Surveillance in Agricultural Entomology: Computer Vision, Sensing and Predictive Pest Monitoring

Agricultural pest surveillance is shifting from periodic manual inspection towards digitally mediated observation, combining automated traps, computer vision, environmental sensors and statistical or machine-learning forecasts. The technological literature reports increasingly sophisticated species recognition, remote counting and near-real-time data transmission. Whether these capabilities generate reliable estimates of pest pressure or improve integrated pest management, however, depends on ecological sampling, field validation and decisions taken after an alert. This critical narrative review evaluates the measurement-to-decision pathway rather than treating classification accuracy as evidence of agronomic benefit. Peer-reviewed research and relevant methodological literature were identified through accessible scholarly sources, with thematic emphasis on camera-equipped pheromone, light and adhesive traps; image-recognition architectures; optical, acoustic and environmental sensing; predictive modelling; and operational evaluation. The synthesis indicates that instrumented traps and image-processing methods can reduce some observation burdens and produce useful within-system temporal information. Their reported performance is nevertheless strongly conditioned by trap selectivity, target species, illumination, specimen overlap, annotation quality, seasonal distribution shifts and the choice of validation dataset. Forecasting approaches can exploit temperature, phenology and historical trap catches, but retrospective fit does not establish transferable warning value. Ecological inference also requires distinguishing insect activity, trap catches, abundance, crop injury and economic risk. The most consequential weaknesses are limited independent multi-site prospective testing, incomplete uncertainty reporting, insufficient comparison with manual scouting, and few controlled evaluations of management consequences. A deployment-oriented framework is proposed in which measurement validity, calibration, forecast skill, decision thresholds, costs and non-target effects are appraised as connected requirements. Progress will depend less on marginal improvements in benchmark detection scores than on prospective, locally calibrated studies that measure timeliness, intervention quality, labour, pesticide use, yield and biodiversity outcomes.