Phenotype pictures of wheat heads infected with Fusarium graminearum

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 c..., Single wheat heads from six different winter wheat varieties were point inoculated with Fusarium graminearum 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 a..., , # 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 th...

由禾谷镰孢（Fusarium graminearum, Fg）引起的普通小麦（Triticum aestivum）赤霉病（Fusarium head blight, FHB），会因病原菌产生真菌毒素脱氧雪腐镰刀菌烯醇（deoxynivalenol）而降低籽粒产量与品质。传统人工评级法通过统计染病麦穗占比（发病率）与染病小穗占比（严重度）来评估小麦赤霉病抗性，但该方法耗时费力且易受人为误差影响。本研究结合深度学习模型与光谱指数（spectral index），实现小麦赤霉病严重度的快速表型分析。本研究首先通过目标检测模型（object detection model）生成麦穗边界框，再利用该边界框提示Meta的Segment Anything模型（Segment Anything Model）完成麦穗实例分割。本研究基于可控环境下禾谷镰孢单点接种的2576张麦穗图像，通过红、绿波段构建光谱指数，以区分健康与染病组织并估测病害严重度。对分割掩码内的像素实施分层随机抽样（stratified random sampling），模型……
选取6个不同冬小麦品种的单个麦穗，采用禾谷镰孢单点接种，并置于最适宜病原菌生长的可控环境中培育。分别在接种后第7、10、14天对每个麦穗进行图像采集：采用标准iPhone 14 Pro相机自动模式，在恒定光照条件下拍摄，麦穗放置于黑色背景上，拍摄高度约为30厘米。此外，在每次图像采集当日，通过统计每个麦穗的染病小穗数量，记录人工视觉病害严重度。本研究采用两种方法估测麦穗病害严重度：纯深度学习法，以及深度学习结合光谱指数法。
随后开展了相机增益与曝光测试，以探究相机参数设置对实验流程的影响。采集了同一批染病小麦植株在不同室外光照条件下的图像，部分采用自动增益……
# 染病单个麦穗图像数据集
https://doi.org/10.5061/dryad.tht76hf6g
本数据集包含一份Excel文件，记录了单个染病麦穗的病害严重度测量数据，同时上传了每张对应图像。

## 数据与文件结构说明
该Excel文件为每张图像提供了3种严重度估测值：第一种为"Threshold Sev (%)"，其通过目标检测与实例分割模型完成麦穗分割，再利用光谱指数区分健康与染病组织得到估测结果；第二种为"Instance Sev (%)"，指通过实例分割模型分割健康与染病组织后计算得到的染病占比估测值；第三种为"Severity"，即通过统计每个麦穗的染病小穗数量得到的人工视觉估测值。其余列均为图像相关数据："Isolate"指用于接种的禾谷镰孢菌株……