hittite j sci eng Hittite Journal of Science and Engineering 2148-4171 Hitit University 10.17350/HJSE19030000267 Engineering Mühendislik Powdery Mildew Detection in Hazelnut with Deep Learning https://orcid.org/0000-0002-5797-1284 Boyar Tülin MARMARA ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ https://orcid.org/0000-0001-6999-1410 Yıldız Kazım MARMARA ÜNİVERSİTESİ 09 28 2022 9 3 159 166 05 11 2022 09 16 2022 Copyright © 2014, Hittite Journal of Science and Engineering 2014 Hittite Journal of Science and Engineering

Hazelnut cultivation is widely practiced in our country. One of the major problems in hazelnut cultivation is powdery mildew disease on hazelnut tree leaves. In this study, the early detection of powdery mildew disease with the YOLO model based on machine learning was tested on a unique data set. Object detection on the image, which is widely applied in the detection of plant diseases, has been applied for the detection of powdery mildew diseases. According to the results obtained, it has been seen that powdery mildew disease can be detected on the image. In the network trained with the Yolov5 model, diseased areas were detected with 95% accuracy in leaf images containing many diseases. Detection of healthy leaves, on the other hand, was tried on images with complex backgrounds and could detect more than one leaf on an image with 85% accuracy. The Yolov5 model, which has been used in many studies for disease detection on plant leaves, also gave effective results for the detection of powdery mildew disease on hazelnut leaves. Early detection of powdery mildew with a method based on machine learning; will stop the possible spread of disease; It will increase the efficiency of hazelnut production by preventing the damage of hazelnut producers.

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