Ressource-Efficient Moth Detection for Pest Monitoring with YOLOv5

Farooq MT, Leipert M, Maier A, Christlein V (2023)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2023

Series: IEEE Symposium Series on Computational Intelligence

Pages Range: 553 - 558

Event location: Ciudad de Mexico MX

URI: https://ieeexplore.ieee.org/document/10372041

DOI: 10.1109/SSCI52147.2023.10372041

Abstract

Moths pose a significant threat to agricultural crops,
and identifying them accurately is crucial for effective pest
monitoring and crop conservation efforts. However, manually
evaluating glue traps is a time-consuming and labor-intensive
process, which has led to the development of automated solutions.
In this study, we present a deep learning-based automated
detection pipeline that can detect moths in images captured
by field traps with pheromone-emitting glue pads. To train our
model, we collected a comprehensive dataset that includes moths
from various environments, such as agricultural plants, homes,
and food production facilities. We augmented this dataset and
included additional glue pad datasets, enabling the model to
detect moths regardless of the species. We base our model on
the YOLOv5 algorithm and fine-tune it using transfer learning,
which enables us to identify moths in real-time and on embedded
hardware. Our evaluation of the algorithm reveals that it achieves
an average precision of 98.2 % on a test dataset, which outperforms
reference models from previous research. We also assess the
model’s ability to handle disturbances such as other insects,
varying lighting conditions, and foreign objects. Importantly, our
solution maintains a tiny memory footprint and low inference
time of 2.3 ms, making it a highly efficient and effective tool for
moth detection in the field.

Authors with CRIS profile

Martin Leipert Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Vincent Christlein Lehrstuhl für Informatik 5 (Mustererkennung)

How to cite

APA:

Farooq, M.T., Leipert, M., Maier, A., & Christlein, V. (2023). Ressource-Efficient Moth Detection for Pest Monitoring with YOLOv5. In IEEE (Eds.), Proceedings of the 2023 IEEE Symposium Series on Computational Intelligence (pp. 553 - 558). Ciudad de Mexico, MX.

MLA:

Farooq, M. Tallal, et al. "Ressource-Efficient Moth Detection for Pest Monitoring with YOLOv5." Proceedings of the 2023 IEEE Symposium Series on Computational Intelligence, Ciudad de Mexico Ed. IEEE, 2023. 553 - 558.

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