Agricultural products are very important in meeting the nutritional needs of living creatures in the world. The rapid increase in the world population makes it necessary to increase the productivity in agricultural products. It is very important to ensure product productivity in limited agricultural areas and to detect diseases that can be seen in plants effectively and on time. Especially the short life of some fruit trees makes it more important to detect the diseases in these trees accurately, on time and quickly. Deep learning, which has been widely used in image processing recently, offers effective applications in agricultural activities. In this study, convolutional neural network method is proposed to detect peach tree diseases. In the proposed method, the detection of the disease with monilya laxa and sphaerolecanium prunastri in peach trees was made with the previously trained AlexNet model. Experimental studies were carried out with a dataset consisting of real disease images taken from the TRB1 region. In experimental studies, the disease was detected with an accuracy of 99.30%. Achieved 1.44% higher accuracy than existing studies.
Publication Date : April 30, 2021
|APA||Aslan, M . (2021). Derin Öğrenme ile Şeftali Hastalıkların Tespiti . Avrupa Bilim ve Teknoloji Dergisi , (23) , 540-546 . DOI: 10.31590/ejosat.883787|