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.

Plant phenotyping relevance

リンゴ葉の病斑をYOLOv5で画像から検出し、病害感受性を評価する高スループット表現型計測法を確立・検証しており、フェノタイピング手法が中心である。

Code and data availability

The authors explicitly state that the image dataset used for model training (image_dataset_2023, 927 RGB images with 4167 annotations) and the YOLOv5s detection workflow with instructions are available in an open-source GitHub repository. This is a paper-specific, public, actionable asset directly reproducing the pheny