DOI: 10.5061/dryad.tht76hf6g
title: Phenotype pictures of wheat heads infected with Fusarium graminearum

description: Fusarium head blight (FHB) of wheat (Triticum aestivum), caused by the
 fungal pathogen Fusarium
 graminearum (Fg), reduces grain yield and quality due to
 the production of the mycotoxin deoxynivalenol. Manual rating for
 incidence (percent of infected wheat heads/spikes) and severity (percent
 of spikelets infected) to estimate FHB resistance, is time-consuming and
 subject to human error. This study uses a deep learning model, combined
 with a spectral index, to provide rapid phenotyping of FHB severity. An
 object detection model was used to localize wheat heads within boundary
 boxes. Corresponding boxes were used to prompt Meta’s Segment Anything
 Model to segment wheat heads. Using 2576 images of wheat heads point
 inoculated with Fg in a controlled environment, a spectral index was
 developed using the red and green bands to differentiate healthy from
 infected tissue and estimate disease severity. Stratified random sampling
 was applied to pixels within the segmentation mask, and the model
 classified pixels as healthy or infected with an accuracy of 87.8%. Linear
 regression determined the relationship between the index and visual
 severity scores. The severity estimated by the index was able to predict
 visual scores (R2=0.83, p=&lt;2e-16). This workflow was also applied
 to plot size images of infected wheat heads from an outside dataset with
 varying cultivars and light conditions, to assess model transferability.
 It correctly classified pixels as healthy or infected with a prediction
 accuracy of 85.8%. These methods may provide rapid estimation of FHB
 severity to improve selection efficiency for resistance or estimate
 disease pressure for effective management.
description: Single wheat heads from six different winter wheat varieties were
 point inoculated with <em>Fusarium graminearum</em> and kept
 in a controlled environment optimal for fungal growth. Images were taken
 of each wheat head 7, 10, and 14 days post-inoculation. Each image was
 taken with a standard iPhone 14 pro camera with automatic settings under
 consistent lighting conditions, with the head placed upon a black
 background. Each Image was taken from approximately 30 cm. In addition,
 visual disease severity notes were taken for each head by counting the
 number of infected spikelets on each head at each day an image was taken.
 Two approaches were then used to estimate severity within the heads, a
 deep learning only approach and a deep learning combined with a spectral
 index approach.  A gain and exposure test was then
 conducted to determine the effect of camera settings on the workflow.
 Images were taken of the same infected wheat plants under varying outdoor
 light conditions, some with automatic gain and exposure camera settings
 and others with set settings. Annotations were then created for the wheat
 heads within the images, and index values calculated to determine
 thresholds for these images.
description: # FHB Infected Single Wheat Head Images
 [https://doi.org/10.5061/dryad.tht76hf6g](https://doi.org/10.5061/dryad.tht76hf6g) This dataset contains an excel file with disease severity measurements of infected single wheat heads. Each corresponding image is also uploaded. ## Description of the data and file structure The excel file contains 3 severity estimates for each image. The first "Threshold Sev (%)" is the estimate produced by using an object detection and instance segmentation model to segment the wheat head, then applying a spectral index that separates healthy from infected tissue. The "Instance Sev (%)" refers to severity estimates produced by the instance segmentation model that segmented healthy and infected tissue, then determines the percent infected. The "Severity" is the visual estimate determined by counting the number of infected spikelets on each head. The remaining columns refer to the image data: "Isolate" is the *Fusarium graminearum* isolate used to inoculate the what head, "Cultivar" is the wheat variety, "Replication" is the rep of the wheat head, "Head" is which head on the plant, and "DAI" is the days after infection the notes/images were taken. These remaining columns correspond to the image names, with each piece of information being separated by an underscore. The Database_Single_Head_Images folder contains all 2576 images of single wheat heads used in this analysis. These images were used to train an object detection and instance segmentation model (see our GitHub Repo for the models), as well as test the efficacy of the spectral index. The Gain_Exposure_Images folder contains the images and annotations for the gain and exposure test. The annotations were used to segment the wheat heads, at which point the spectral index was applied. The index values for each set (Set gain and exposure and automatic gain and exposure, indicated by image name) were combined and the corresponding histogram inspected to see how camera settings impact this workflow. The images were taken outdoors under varying lighting conditions. ## Code/Software All code used in this study can be found at [https://github.com/RileyMccon/FHB-Severity-Evaluation. ](https://github.com/RileyMccon/FHB-Severity-Evaluation.)