Abstract
The impact of climate change on global temperature and precipitation patterns can lead to an increase in extreme environmental events. These events can create favourable conditions for the spread of plant pests and diseases, leading to significant production losses in agriculture. To mitigate these losses, early detection of pests is crucial in order to implement effective and safe control management strategies, to protect the crops, public health and the environment. Our work focuses on the development of a computer vision framework to detect and classify the olive fruit fly, also known as Bactrocera oleae, from images, which is a serious concern to the EU’s olive tree industry. The images of the olive fruit fly were obtained from traps placed throughout olive orchards located in Greece. The approach entails augmenting the dataset and fine-tuning the YOLOv7 model to improve the model performance, in identifying and classifying olive fruit flies. A Portuguese dataset was also used to further perform detection. To assess the model, a set of metrics were calculated, and the experimental results indicated that the model can precisely identify the positive class, which is the olive fruit fly.
This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boukhris, L., Ben Abderrazak, J., Besbes, H.: Tailored deep learning based architecture for smart agriculture. In: 2020 International Wireless Communications and Mobile Computing (IWCMC), pp. 964–969 (2020). https://doi.org/10.1109/IWCMC48107.2020.9148182
Eurostat, E.: Agricultural production - orchards: olive trees. Statistics Explained (2019). https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Agricultural_production_-_orchards#Olive_trees. Accessed 2 Feb 2023
Kalamatianos, R., Karydis, I., Doukakis, D., Avlonitis, M.: DIRT: the dacus image recognition toolkit. J. Imaging 4, 129 (2018). https://doi.org/10.3390/JIMAGING4110129, https://www.mdpi.com/2313-433X/4/11/129/htm
Kasinathan, T., Singaraju, D., Uyyala, S.R.: Insect classification and detection in field crops using modern machine learning techniques. Inf. Process. Agric. 8(3), 446–457 (2021). https://doi.org/10.1016/j.inpa.2020.09.006, https://www.sciencedirect.com/science/article/pii/S2214317320302067
Le, A.D., Pham, D.A., Pham, D.T., Vo, H.B.: AlertTrap: a study on object detection in remote insects trap monitoring system using on-the-edge deep learning platform (2021). https://doi.org/10.48550/ARXIV.2112.13341, https://arxiv.org/abs/2112.13341
Nazir, N., et al.: Effect of climate change on plant diseases. Int. J. Curr. Microbiol. Appl. Sci. 7, 250–256 (2018). https://doi.org/10.20546/IJCMAS.2018.706.030
Pereira, J.A.: Yellow sticky traps dataset _ olive fly (Bactrocera Oleae) (2023). https://doi.org/10.34620/dadosipb/QFG85C
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection (2015). https://doi.org/10.48550/ARXIV.1506.02640, https://arxiv.org/abs/1506.02640
Shaked, B., et al.: Electronic traps for detection and population monitoring of adult fruit flies (diptera: Tephritidae). J. Appl. Entomol. 142(1–2), 43–51 (2017). https://doi.org/10.1111/jen.12422
Thenmozhi, K., Reddy, U.S.: Image processing techniques for insect shape detection in field crops. In: 2017 International Conference on Inventive Computing and Informatics (ICICI), pp. 699–704 (2017). https://doi.org/10.1109/ICICI.2017.8365226, https://ieeexplore.ieee.org/document/8365226/
Tzutalin: Labelimg (2015). https://github.com/tzutalin/labelImg
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696 (2022)
Wang, K., Zhang, S., Wang, Z., Liu, Z., Yang, F.: Mobile smart device-based vegetable disease and insect pest recognition method. Intell. Autom. Soft Comput. 19, 263–273 (2013). https://doi.org/10.1080/10798587.2013.823783, https://www.tandfonline.com/doi/abs/10.1080/10798587.2013.823783
Xia, D., Chen, P., Wang, B., Zhang, J., Xie, C.: Insect detection and classification based on an improved convolutional neural network. Sensors 18, 4169 (2018). https://doi.org/10.3390/S18124169, https://www.mdpi.com/1424-8220/18/12/4169/htm
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Victoriano, M., Oliveira, L., Oliveira, H.P. (2023). Automated Detection and Identification of Olive Fruit Fly Using YOLOv7 Algorithm. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-36616-1_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36615-4
Online ISBN: 978-3-031-36616-1
eBook Packages: Computer ScienceComputer Science (R0)