Trap colour strongly affects the ability of deep learning models to recognize insect species in images of sticky traps

Our results support the development of an automatic classification of pests on a sticky trap, which should focus on colour and deep learning architecture to achieve good results. Future studies could aim to incorporate the trap system into pest monitoring, providing more accurate and cost-effective results in a pest management programme. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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Publication Details

Journal
Pest Management Science
Published
2024-10-08
DOI
https://doi.org/10.1002/ps.8464
Citations
10
Primary Topic
Insect and Arachnid Ecology and Behavior
Type
article
Field-Weighted Citation Impact
5.50
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article

Trap colour strongly affects the ability of deep learning models to recognize insect species in images of sticky traps

Song‐Quan Ong, Toke T. Høye
10 citations
Pest Management Science
Insect and Arachnid Ecology and Behavior
5.50
article

Trap colour strongly affects the ability of deep learning models to recognize insect species in images of sticky traps

Song‐Quan Ong, Toke T. Høye
article en
10 citations

Abstract

Our results support the development of an automatic classification of pests on a sticky trap, which should focus on colour and deep learning architecture to achieve good results. Future studies could aim to incorporate the trap system into pest monitoring, providing more accurate and cost-effective results in a pest management programme. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Pest Management ScienceVol. 81(2)
Universiti of Malaysia Sabah (MY), Aarhus University (DK)
Openalex Percentile: Top 4%
Insect and Arachnid Ecology and Behavior
5.50
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