Derin öğrenme ağları kullanılarak mısır yapraklarında hastalık tespiti

Abstract

— Nowadays, people need easy access to basic nutrients to live a healthy life. In addition to providing calories that can meet the physiological needs of human beings, maize, which is one of the basic foods, contains valuable minerals and vitamins such as vitamin B6, sodium, magnesium, zinc, potassium, calcium, vitamin A. As a result of the increase in the world population in the world and our country, the need for maize is increasing day by day. Herein, it is important to detect the diseases seen in maize leaves that reduce the efficiency of maize production. Thanks to the developing technologies, producers should be encouraged by using technological opportunities in maize cultivation. In the study, it is aimed to detect maize rust, gray leaf spot, and leaf blight on maize leaves. In addition, two models based on the EfficientNetB5 network and convolutional neural network have been developed to detect diseases found in maize leaves using deep learning methods. To increase the performance metrics of created models, the number of images has been increased by using data augmentation techniques (mirror, rotation, scale). From the results, it is seen that the prediction success rates obtained in the EfficientNetB5 transfer learning model and the developed deep learning model are equal to 92.12% and 89.88%, respectively.