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Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Efficient Deep Learning Approach for Olive Disease Classification

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DOI: http://dx.doi.org/10.15439/2023F4794

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 889–894 ()

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Abstract. Olive culture is one of the most important for the Mediterranean countries. In recent years, the role of Artificial Intelligence in agriculture is increasing: its use ranges from monitoring of cultivated soil, to irrigation management, to yield prediction, to autonomous agricultural robots, to weed and pest classification and management for example by taking pictures using a standard smartphone or a unmanned aerial vehicle, and all this eases human work and makes it even more accessible. In this work, we propose a method for olive diseases classification, based on an adaptive ensemble of two EfficientNet-b0 models, that improves the state-of-the-art accuracy on a publicly available dataset by 1.6-2.6\%. Both in terms of number of parameters and on number of operations, our method reduces complexity roughly by 50\% and 80\% respectively, that is a level not seen in at least a decade. Due to its efficiency, this method is also embeddable into a smartphone application for real-time processing.

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