Tulip Disease Detection by Camera Images

Abstract

In the process of producing tulip bulbs, it is very difficult to find and extract diseased strains, so the mechanization of that work has been delayed. Therefore, in order to contribute to the mechanization of this process, we attempted to detect the diseased strain using images of tulip strains being cultivated. Tulip strains cultivated in the open field were photographed at each growth stage for some viral symptoms of some varieties and an image database was created. Using these image data, a discriminator using a convolutional neural network was trained, and the discriminant accuracy was evaluated. As the results, high discrimination accuracy was generally obtained.