YOLO-Based Phenotyping of Apple Blotch Disease (Diplocarpon coronariae) in Genetic Resources after Artificial Inoculation

Abstract

Phenotyping of genetic resources is an important prerequisite for the selection of resistant varieties in breeding programs and research. Computer vision techniques have proven to be a useful tool for digital phenotyping of diseases of interest. One pathogen that is increasingly observed in Europe is Diplocarpon coronariae, which causes apple blotch disease. In this study, a high-throughput phenotyping method was established to evaluate genetic apple resources for susceptibility to D. coronariae. For this purpose, inoculation trials with D. coronariae were performed in a laboratory and images of infested leaves were taken 7, 9 and 13 days post inoculation. A pre-trained YOLOv5s model was chosen to establish the model, which was trained with an image dataset of 927 RGB images. The images had a size of 768 × 768 pixels and were divided into 738 annotated training images, 78 validation images and 111 background images without symptoms. The accuracy of symptom prediction with the trained model was 95%. These results indicate that our model can accurately and efficiently detect spots with acervuli on detached apple leaves. Object detection can therefore be used for digital phenotyping of detached leaf assays to assess the susceptibility to D. coronariae in a laboratory.