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

Annals of Computer Science and Information Systems, Volume 35

Segmentation Methods Evaluation on Grapevine Leaf Diseases

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DOI: http://dx.doi.org/10.15439/2023F7053

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 1081–1085 ()

Full text

Abstract. The problem of grapevine disease detection (VDD) was addressed in a number of research papers, however, a generic solution is not yet available for this task in the community. The region of interest segmentation and object detection tasks are often complementary. A similar situation is encountered in VDD applications as well, in which crop or leaf detection can be done via instance segmentation techniques as well. The focus of this work is to validate the most suitable methods from the main literature on vine leaf segmentation and disease detection on a custom dataset containing leaves both from the laboratory environment and cropped from images in the field. We tested five promising methods including the Otsu's thresholding, Mask R-CNN, MobileNet, SegNet, and Feature Pyramid Network variants. The results of the comparison are available in Table 1 summarizing the accuracy and runtime of different methods.

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