Insect monitoring without pitfalls: seven steps for robust insect sensing systems

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Data shortages fuel controversy about an ongoing insect biodiversity crisis. Insects are immensely diverse and functionally critical for ecosystems, yet data on insect trends remain patchy and biased. Sensors, ranging from camera-equipped light traps to weather radar stations, are set to transform entomological data collection. Meanwhile, AI models that extract biological information from sensors are improving at a startling rate. Realising the potential of automated monitoring means progressing from proof-of-concept studies to scalable insect sensing systems. However, stakeholders face severe operational challenges when adopting a growing suite of sensors, models, and protocols for insect surveillance. Deployment of devices is not well co-ordinated, while the risks of relying on AI are overlooked or understated. To achieve monitoring goals, common pitfalls related to sensors and AI need to be exposed and avoided. Here, we trace a seven-step path towards an effective transnational rollout of insect sensing systems. Step (1) reviews strengths, weaknesses and synergies across visual, acoustic, radar and photonic sensors; (2) confronts species determination—a key challenge for sensors and AI—suggesting how to improve identification and make use of uncertain data; (3) promotes creating and sharing standardised, labelled data, offering ecological insights and opportunities to train and evaluate AI; (4) ensures AI is used to complement other resources, both human and digital, given it is not always the best tool for the job; (5) highlights how and why AI models can fail, calling for shrewd approaches to model training and routine evaluations; (6) aims to break barriers to wider uptake of technologies through knowledge sharing, affordable design principles, and equitable computing infrastructures. Finally, (7) emphasizes that any revolution in insect monitoring must be grounded in good sampling design, with established monitoring schemes at its core. We set a trajectory for coordinated development of insect sensing systems, focussing not only on technical performance, but on integration with human expertise, case-based evaluation and harmonisation with historical long-term datasets. We address fundamental challenges of sensors and AI for biodiversity monitoring, producing recommendations that apply to all branches of the tree of life. https://doi.org/10.32942/X2MD4V Artificial Intelligence and Robotics, Biodiversity, Computer Sciences, Ecology and Evolutionary Biology, Entomology, Life Sciences arthropods, artificial intelligence, computer vision, image classification, invertebrates, machine learning, object detection, pollinators, remote sensing, signal processing Published: 2026-02-13 20:19 Last Updated: 2026-02-17 03:03 CC BY Attribution 4.0 International Conflict of interest statement: All funding for this research has been fully acknowledged and there are no financial benefits to the authors associated with this manuscript. Data and Code Availability Statement: No data is assocaited with this review. Language: English