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

Song-Quan Ong and Toke Thomas Høye (2024) Trap colour strongly affects the ability of deep learning models to recognize insect species in images of sticky traps. Scientific Reports. pp. 1-13. ISSN 2045-2322

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Abstract

BACKGROUND: The use of computer vision and deep learning models to automatically classify insect species on sticky traps has proven to be a cost- and time-efficient approach to pest monitoring. As different species are attracted to different colours, the variety of sticky trap colours poses a challenge to the performance of the models. However, the effectiveness of deep learning in classifying pests on different coloured sticky traps has not yet been sufficiently explored. In this study, we aim to investigate the influence of sticky trap colour and imaging devices on the performance of deep learning models in classifying pests on sticky traps. RESULTS: Our results show that using the MobileNetV2 architecture with transparent sticky traps as training data, the model predicted the pest species on transparent sticky traps with an accuracy of at least 0.95 and on other sticky trap colours with at least 0.85 of the F1 score. Using a generalised linear model (GLM) and a Boruta feature selection algorithm, we also showed that the colour and architecture of the sticky traps significantly influenced the performance of the model. CONCLUSION: 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.

Item Type:	Article
Keyword:	Glue trap; transfer learning; deep convolutional neural network; RGB sensor; artificial intelligence; precision biodiversity
Subjects:	Q Science > Q Science (General) > Q1-390 Science (General) > Q300-390 Cybernetics Q Science > QL Zoology > QL1-991 Zoology > QL360-599.82 Invertebrates > QL461-599.82 Insects Q Science > QL Zoology > QL1-991 Zoology > QL750-795 Animal behavior
Department:	INSTITUTE > Institute for Tropical Biology and Conservation
Depositing User:	ABDULLAH BIN SABUDIN -
Date Deposited:	07 Apr 2025 17:16
Last Modified:	07 Apr 2025 17:16
URI:	https://eprints.ums.edu.my/id/eprint/43419

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