Soyer, Mehmet SamilYilmaz, CanOzcan, Ismail MervanCogen, FatihYildiz, Tuba Çonka2025-02-202025-02-202021978-605011437-9https://doi.org/10.23919/ELECO54474.2021.9677785https://hdl.handle.net/20.500.12846/176713th International Conference on Electrical and Electronics Engineering, ELECO 2021 -- 25 November 2021 through 27 November 2021 -- Virtual, Bursa -- 176537In this research, using image processing and Convolutional Neural Networks (CNN) together, the detection and classification of diseases in terrestrial plants have been carried out. A CNN was created and developed using a data set containing leaf images of healthy and sick plants. The training of the model was made over a data set of various leaf images containing 87867 images with 98.88% accuracy. In addition, using the same data set, an Android-based application has been developed to detect diseases of plants. Through this application, by taking a photograph of the leaf that is thought to have a disease, it is possible to determine in advance, whether the leaf is really sick and what kind of disease it has. In this way, it is possible to intervene in the spread of the disease as soon as possible. © 2021 Chamber of Turkish Electrical Engineers.eninfo:eu-repo/semantics/closedAccessDeep learningImage processingConvolutional neural networkData setDisease detectionImages processingLeaf imagesPlant diseaseTerrestrial plantsConvolutional neural networksConference Object10.23919/ELECO54474.2021.96777853984022-s2.0-85125220224