DOI: 10.13020/8jwk-xx41
title: An RGB deep-neural network approach for high-throughput phenotyping of Fusarium head blight in wheat

description: Fusarium head blight (FHB) in wheat is an economically important disease which can cause yield losses exceeding 50%. Breeding for host resistance remains the most effective disease control method; however, time, labor, and human subjectivity during disease scoring limits selection advancements. In this study we describe an innovative, high-throughput phenotyping rover for capturing in-field RGB images and a deep neural network pipeline for wheat spike detection and FHB disease quantification. The image analysis pipeline detects wheat spikes from images collected by the phenotyping rover under variable field conditions, segments those spikes and the amount of diseased tissue in the spikes, and quantifies disease severity as the region of intersection between the spike and disease masks. To validate disease inferences, individual spike and plot aggregate FHB estimates from the pipeline were compared with visual disease scores from the field and on images. The precision and throughput of the pipeline surpassed traditional field rating methods. Aggregate plot disease levels as estimated by the pipeline correlated highly with field and manually annotated image disease scores; however disease assessments on individual spikes were influenced by field location. The pipeline was able to quantify FHB from images taken with different camera orientations than the original training data, which demonstrates strong generalizability. This innovative pipeline represents a breakthrough in FHB phenotyping, offering precise and efficient assessment of FHB on both individual spikes and plot aggregates. The pipeline is robust across different environments and the potential to standardize disease evaluation methods across the research groups make it a valuable tool for studying and managing this economically significant fungal disease.
description: Julian Cooper
Hirsch Lab

An RGB deep-neural network approach for high-throughput phenotyping of Fusarium head blight in wheat

File contents:

1_image_analysis_results: Fusarium head blight field and image data frames and analysis
data_frames:
 - FHB_disease_human_field: For each row/plot at each rating date/location, 0-100% plot average disease scores from five raters, A-E.
 - FHB_disease_pipeline_image: For each row/plot at each rating date/location, individual spike disease percentage (fhb_percentage) and gradability classification (gradability_prob, 0.50 = gradable) for all detected wheat heads.
 - FHB_disease_human_pipeline_200image: For the 200 spike images used to measure inter-rater reliability, individual spike disease percentage from each rater, A-E, and from the FHB image analysis pipeline (Pipeline).
 - FHB_disease_human_pipeline_10Kimage: For all spikes determined to be gradable by raters during manual image annotation of the 10K images used to compare human and pipeline disease annotations on a spike and plot level, individual spike disease percentage from the FHB image analysis pipeline (pipeline_image_perc_FHB) and from manual image annotation by a human rater (human_image_perc_FHB) as well as the rater ID. 
- pipeline_field_plot: Compare pipeline results to average of five raters scoring disease in the field
- pipeline_n10K_plot: Compare pipeline results to five raters manually annotating disease on separate images at plot scale
- pipeline_n10K_spike: Compare pipeline results to five raters manually annotating disease on separate images at spike scale
- pipeline_n200_spike: Compare pipeline results to five raters manually annotating disease on same images at spike scale

2_image_analysis_pipeline_workflow: Image analysis pipeline for analyzing images 
3_wheat_spike_detection_model: Contains grain_head_and_other_detection model for wheat spike detection to be loaded into image analysis pipeline
4_wheat_spike_gradability_model: Contains umn_full_growth_cycle_unet_128x128 model for wheat spike gradability classification to be loaded into image analysis pipeline
5_wheat_spike_segmentation_model: Contains wheat_head_fhb_seg model for wheat spike segmentation to be loaded into image analysis pipeline
6_disease_segmentation_model: Contains wheat_spike_fhb_disease_non_gradable_classification_input_is_BGR model for disease segmentation to be loaded into image analysis pipeline

7_n200_inter_rater_reliability_images: Five raters and pipeline annotating disease on same 200 spikes to compare inter-rater reliability
- Pipeline: Raw images and disease annotations from the FHB image analysis pipeline
- raterA: Raw images and manual images annotations of FHB disease by rater A
- raterB: Raw images and manual images annotations of FHB disease by rater B
- raterC: Raw images and manual images annotations of FHB disease by rater C
- raterD: Raw images and manual images annotations of FHB disease by rater D
- raterE: Raw images and manual images annotations of FHB disease by rater E

8_rover_20220713_StP_images_1-4: Raw rover images of Fusarium head blight on wheat from camera C and D for Saint Paul, MN on July 13th 2022 used for FHB image analysis pipeline evaluation
9_rover_20220718_StP_images_1-4: Raw rover images of Fusarium head blight on wheat from camera C and D for Saint Paul, MN on July 18th 2022 used for FHB image analysis pipeline evaluation
10_rover_20220720_StP_images_1-4: Raw rover images of Fusarium head blight on wheat from camera C and D for Saint Paul, MN on July 20th 2022 used for FHB image analysis pipeline evaluation
11_rover_20220728_Crk_images_1-4: Raw rover images of Fusarium head blight on wheat from camera C and D for Crookston, MN on July 28th 2022 used for FHB image analysis pipeline evaluation