Author ORCID Identifier

Jamie Alison: 0000-0002-6787-6192

Luca Pegoraro: 0000-0001-6219-1285

Jarrett Blair: 0000-0002-9132-81

Yuval Cohen: 0000-0001-7556-0895

Birgen Haest: 0000-0002-8739-6460

Jacob Idec: 0000-0003-0976-8297

Jacob Kamminga: 0000-0003-2153-5250

Jenna Lawson: 0000-0001-5166-5510

Meng Li: 0000-0003-4931-5647

Leandro Aparecido Do Nascimento: 0000-0001-8254-2900

Charlotte L. Outhwaite: 0000-0001-9997-6780

Benjamin Rutschmann: 0000-0001-6589-6408

Maximilian Sittinger: 0000-0002-4096-8556

Mariana Abarca: 0000-0002-6944-2574

Complete list in paper.

Document Type

Article

Publication Date

9-2026

Publication Title

Approaches Entomology Method

Abstract

1. Data shortages fuel controversy about an ongoing insect biodiversity crisis. Insects are immensely diverse and functionally critical for ecosystems, yet data on their trends remain patchy and biased. Sensors, ranging from camera traps and acoustic recorders to weather radar stations, are set to transform data collection in entomol- ogy. Meanwhile, AI models that extract biological information from sensors are improving at a startling rate.

2. 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 coordinated, 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.

3. 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. Focussing on data processing and scalability, step (4) proposes using AI 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 fail, calling for routine evaluations and shrewdness during model training; (6) aims to break barriers to wider uptake of technologies through knowledge sharing, affordable design principles and equitable computing infrastructures, and finally, (7) emphasises that any revolution in insect monitoring must be grounded in good sampling design, with established monitoring schemes at its core.

5. We set a trajectory for coordinated development of insect sensing systems, focus- sing not only on technical performance but also 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.

Keywords

arthropods, artificial intelligence, computer vision, image classification, invertebrates, machine learning, object detection, pollinators, remote sensing, signal processing

Volume

1

First Page

e70005

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Rights

© 2026 The Author(s).

Version

Version of Record

Download

Included in

Biology Commons

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.