Grapevine Disease Identification Using Resnet- 50

Visual identification of diseases in grapevines can be a difficult task for growers. The importance of farmers in the identification of grape diseases due to control the spread of disease and lower agricultural yield losses. In this study developed a disease identification system in plants using image processing. Images of leaves on grapevines infected with the disease were taken, extracted features from the images and applied the ResNet-50 algorithm. The dataset of grape leaf images taken was 200 images for four classes, including 3 classes of leaves identified as diseased and 1 class of healthy leaves. The experimental results show that the image processing system for identifying diseases in grapes identifies the types of disease in grapevines. This research has the potential to be implemented in a farm automation system to detect early diseases in grapevines and take appropriate preventive measures to increase productivity and crop quality.