Abstract
Grape moths are a significant pest in vineyards, causing damage and losses in wine production. Pheromone traps are used to monitor grape moth populations and determine their developmental status to make informed decisions regarding pest control. Smart pest monitoring systems that employ sensors, cameras, and artificial intelligence algorithms are becoming increasingly popular due to their ability to streamline the monitoring process. In this study, we investigate the effectiveness of using segmentation as a pre-processing step to improve the detection of grape moths in trap images using deep learning models. We train two segmentation models, the U-Net architecture with ResNet18 and InceptionV3 backbonesl, and utilize the segmented and non-segmented images in the YOLOv5s and YOLOv8s detectors to evaluate the impact of segmentation on detection. Our results show that segmentation pre-processing can significantly improve detection by 3% for YOLOv5 and 1.2% for YOLOv8. These findings highlight the potential of segmentation pre-processing for enhancing insect detection in smart pest monitoring systems, paving the way for further exploration of different training methods.
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Acknowledgments
This work was supported by the project “DATI-Digital Agriculture Technologies for Irrigation efficiency”, PRIMA-Partnership for Research and Innovation in the Mediterranean Area, (Research and Innovation activities), financed by the states participating in the PRIMA partnership and by the European Union through Horizon 2020. This work was also financed by National Funds through the Portuguese funding agency, FCT-Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020.
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Teixeira, A.C., Carneiro, G.A., Morais, R., Sousa, J.J., Cunha, A. (2023). Segmentation as a Pre-processing for Automatic Grape Moths Detection. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_31
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