sert, eser2025-10-242025-10-2420211308-87501308-8769https://doi.org/10.7161/omuanajas.805152https://search.trdizin.gov.tr/tr/yayin/detay/1151937https://hdl.handle.net/20.500.12899/2811The 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.eninfo:eu-repo/semantics/openAccessBilgisayar BilimleriYazılım MühendisliğiBahçe BitkileriGörüntüleme Bilimi ve Fotoğraf TeknolojisiBitki BilimleriBilgisayar BilimleriYapay ZekaAlexNetFaster R-CNNObject DetectionGoogLeNetLeaf Disease DetectionSequezeNetArticle10.7161/omuanajas.8051523621671781151937