Abstract
The use of computer vision, deep learning, and drones has revolutionized agriculture by enabling efficient crop monitoring and disease detection. Still, many challenges need to be overcome due to the vast diversity of plant species and their unique regional characteristics. Olive trees, which have been cultivated for thousands of years, present a particularly complex case for leaf-based disease diagnosis as disease symptoms can vary widely, both between different plant variations and even within individual leaves on the same plant. This complexity, coupled with the susceptibility of olive groves to various pathogens, including bacterial blight, olive knot, aculus olearius, and olive peacock spot, has hindered the development of effective disease detection algorithms. To address this challenge, we have devised a novel approach that combines deep learning techniques, leveraging convolutional neural networks, vision transformers, and cloud computing-based models. Aiming to detect and classify olive tree diseases the experimental results of our study have been highly promising, demonstrating the effectiveness of the combined transformer and cloud-based machine learning models, achieving an impressive accuracy of approximately 99.6% for multiclass classification cases including healthy, aculus olearius, and peacock spot infected leaves. These results highlight the potential of deep learning models in tackling the complexities of olive leaf disease detection and the need for further research in the field.
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Acknowledgements
This work has been supported by the European Union’s Horizon 2020 research and innovation programme under Secure and Seamless Edge-to-Cloud Analytics (GA 957286), EU H2020, ICT-50-2020 - Software Technologies and Next Generation IoT as part of Next Generation Internet project (GA 957246), EU H2020, ICT-56-2020 - Next Generation Internet of Things.
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Sarantakos, T., Gutierrez, D.M.J., Amaxilatis, D. (2024). Olive Leaf Infection Detection Using the Cloud-Edge Continuum. In: Chatzigiannakis, I., Karydis, I. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2023. Lecture Notes in Computer Science, vol 14053. Springer, Cham. https://doi.org/10.1007/978-3-031-49361-4_2
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DOI: https://doi.org/10.1007/978-3-031-49361-4_2
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