Segmentation Methods Evaluation on Grapevine Leaf Diseases.

The problem of vine 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 I summarizing the accuracy and runtime of different methods.