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    A deep learning based approach for the detection of diseases in pepper and potato leaves
    (2021) sert, eser
    The present study proposes a Faster R-CNN Object Detection Approach with GoogLeNet Classifier (Faster R-CNN-GC) using image stitching, Faster R-CNN and GoogLeNet to detect pepper and potato leaves as well as leaf diseases in them. It is widely known that for a successful object detection performance, Faster R-CNN requires performing image labelling on a very high number of data, which will later train Faster R-CNN. However, this process is often very time-consuming. The present study mainly aims to shorten this process by designing an object detection approach which benefits from Faster R-CNN and GoogLeNet architecture. Firstly, Faster R-CNN and GoogLeNet were trained. Later, for the testing process, some of two-piece images were combined using an image stitching approach. Finally, using Faster R-CNN and GoogLeNet, pepper and potato leaves are detected and diseases are written on them. In addition, the proposed system was compared with Faster R-CNN Object Detection Approach with AlexNet Classifier (Faster R-CNN-AC), Faster R-CNN Object Detection Approach with SequezeNet Classifier (Faster R-CNN-SC) and Faster R-CNN. The findings of the experimental studies demonstrated that Faster R-CNN-GC displayed a higher object detection performance compared to other approaches.