Effective pest management requires accurate and continuous monitoring. This monitoring helps assess population dynamics and guides the development of integrated pest management strategies. Traps used to capture insects are an alternative applied to various crops. However, the identification and manual counting of specimens are time-consuming, require taxonomic knowledge, and depend on the expertise of specialists. Automation could reduce costs, increase accuracy, and enable scalable analyses. Current computer vision and artificial intelligence techniques can quickly and accurately identify objects in digital images. This study presents a systematic review of literature retrieved from multidisciplinary and specialized databases (Scopus, ACM, Web of Science, IET, DBLP, Springer, and ScienceDirect), focusing on the intersections of agriculture, ecology, and computer science. We found 284 studies published between 2020 and 2025. Among them, 57 fulfilled the eligibility criteria, considering applied computing solutions for insect identification and counting using digital images of specimens collected via traps or photographed in situ on plants, in both field and laboratory settings. The findings highlight the use of electronic traps for real-time data collection and improvements in convolutional neural networks, with visual transformers and attention mechanisms for multi-species and fine-grained recognition. They also indicate opportunities to leverage microscopy resources, overcome limitations in the large-scale deployment and integration of electronic trap networks, and integrate real-time monitoring data with forecasting models using weather predictions to promote early warning systems for integrated pest management.
Júnior et al. (Mon,) studied this question.
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