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Lookup NU author(s): Mukilan Deivarajan Suresh, Dr Tong XinORCiD, Professor Darren Evans
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Author(s). Agricultural and Forest Entomology published by John Wiley & Sons Ltd on behalf of Royal Entomological Society.Insects play a vital role in ecosystem functioning, but in some parts of the world, their populations have declined significantly in recent decades due to environmental change, agricultural intensification and other anthropogenic drivers. Monitoring insect populations is crucial for understanding and mitigating biodiversity loss, especially in agro-ecosystems where a focus on pests and beneficial insects is gaining momentum in the context of sustainable food systems. Biomonitoring has long played an important role in providing early warnings of insect pests and their vectored pathogens and for assessing agro-ecosystem management. However, identification of invertebrates by taxonomists is time-consuming and fraught with numerous other challenges, particularly when it comes to real-time monitoring. Recent technological advances in both computational image recognition and molecular ecology have enhanced biomonitoring efficiency and accuracy, reducing labour efforts, but leading to unprecedented volumes of data generated. This perspective article examines the significance and further potential of deep learning with image-based recognition, aided by complementary technologies, in advancing entomological biomonitoring. Using entomological sticky traps as an example, we discuss each step of the workflow required to create ecological networks using image-based recognition, multimodal data and deep learning, and we identify the challenges inherent to this method and other insect survey techniques. In order to become mainstream for global biomonitoring, access to long-term, standardised multimodal data is required for comprehending ecosystem dynamics, structure and function and for mitigating insect population declines.
Author(s): Suresh MD, Xin T, Cook SM, Evans DM
Publication type: Article
Publication status: Published
Journal: Agricultural and Forest Entomology
Year: 2024
Pages: epub ahead of print
Online publication date: 25/11/2024
Acceptance date: 09/11/2024
Date deposited: 02/12/2024
ISSN (print): 1461-9555
ISSN (electronic): 1461-9563
Publisher: John Wiley and Sons Inc
URL: https://doi.org/10.1111/afe.12667
DOI: 10.1111/afe.12667
Data Access Statement: No data were used in this manuscript.
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