Horticultural Science and Technology ISSN:1226-8763(Print) 2465-8588(Online) 원예과학기술지

In this study, a RGB sensor was installed on a rotary-wing drone to obtain RGB images of a fire blight simulation site and the actual infection of fire blight (caused by Erwinia amylopora) on pear (Pyrus communis L.) trees, and to develop a system that can determine the occurrence of fire blight in the field by analyzing the RGB images by using Deep Learning's Convolution Neural Network (CNN). The ability to detect fire blight was reviewed by analyzing fire blight learning using the fire blight simulation and applying it to the actual fire blight infection category. The learning of fire blight used the overlap-based Dice function, and the error during learning was performed based on an average of 22%. As a result, 64 of the 80 fire blight infected fruit tree images were recognized as an infection, while 16 images were recognized as non-infected and the predicted rate was 80.0%. In the case of the actual infection of fire blight, 15 of the 21 images of the infected fruit trees were recognized as being infected with fire blight, and 6 images were recognized as non-infected, with a predicted rate of 71.4%. Therefore, our results show that it is possible to detect fire blight by applying images acquired with drones and RGB sensors to a CNN of Deep Learning. The system developed in this study can be applied to the site to reduce the manpower and time required to forecast actual fire blight and also it is believed that rapid response will reduce secondary spread damage.