Automatic Detection and Classification of Some Vineyard Diseases with Faster R-CNN Model

Abstract

Türkiye is one of the countries with the most important vineyard areas in the world, where the most grape production is made. Vineyard diseases are one of the most important reasons that adversely affect the productivity in viticulture. In this study, some vineyard diseases were detected and classified using the Faster R-CNN deep learning model, which is an artificial intelligence approach. These diseases are powdery mildew, downy mildew, dead arm disease, grapevine leaf roll-associated virus disease (GLRaV) and grapevine fan leaf nepovirus (GFLV) diseases that are common and cause economic problems. The proposed method is trained and tested using 11000 images. At the end of the study, the overall accuracy rate was found to be 92%. The proposed approach gave better results than similar methods in the literature. Therefore, it was concluded that the method can be used reliably in the detection and classification of some vineyard diseases.