Powdery mildew detection in hazelnut with deep learning

Abstract

Hazelnut cultivation is widely practised in our country. One of the major problems in hazelnut cultivation is powdery mildew disease on hazelnut 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. Using YOLOv5, diseased areas were detected with approximately 90% accuracy in diseased leaf images. Multiple leaves in one image were detected with approximately 85% accuracy in detecting healthy areas using images with complex backgrounds. The model, which has been used in different studies for the detection of disease in plant leaves, also gave effective results in the detection of powdery mildew disease in 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.