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    Detection of Apple Leaf Diseases using Faster R-CNN
    (2020) Tuncer, Adem; Özen, Yunus; Sardogan, Melike
    Image recognition-based automated disease detection systems play an important role in the early detection of plantleaf diseases. In this study, an apple leaf disease detection system was proposed using Faster Region-BasedConvolutional Neural Network (Faster R-CNN) with Inception v2 architecture. Applications for the detection ofdiseases were carried out in apple orchards in Yalova, Turkey. Leaf images were obtained from different appleorchards for two years. In our observations, it was determined that apple trees of Yalova had black spot (venturiainaequalis) disease. The proposed system in the study detects a large number of leaves in an image, thensuccessfully classifies diseased and healthy ones. The disease detection system trained has achieved an average of84.5% accuracy.