A development of rose leaf disease classification system using convolutional neural network

The classification of plant disease by images has been studied over past decades. In this paper, convolutional neural network models were applied to perform rose leaf disease diagnosis using simple leaves images of healthy and diseased rose leaves, through deep learning methodologies. Training of the models was performed with the use of an open database of 13,125 images, containing field and laboratory images with five different disease and healthy leaves. Based on experiments, the precision and recall are 98.7% and 97.4% and the F1-score is 0.98. The significantly high success rate makes the model a very effective advisory or early warning tool, and an approach that could be further expanded to support an rose leaf disease identification system to operate in real cultivation conditions. © Korean Institute of Electrical Engineers