Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key t...Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key to address FHB-related challenges,but its progress is delayed by traditional methods due to the small-scale,laborious and relatively subjective nature of manual assessment.This study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once(YOLO)model to examine FHB in wheat,enabling us to perform large-scale and automated symptomatic analysis of this disease.We first established an Open FHB(OFHB)training dataset,consisting of 4867 diseased and 106,801 healthy spikes collected from 132 commercial breeding lines during FHB progression.Then,a deep learning model called YOLOv8-WFD was trained for detecting healthy and diseased spikes,followed by an adaptive Excess Green method to identify symptomatic regions and thus FHBrelated traits on spikes.To study resistance levels,we employed an unsupervised SHapley Additive exPlanations(SHAP)method to pinpoint key traits between 10 and 20 d after inoculation(DAIs),resulting in the classification of 423 varieties trialed during the 2023–2024 growing seasons into four resistance levels(i.e.,highly and moderately susceptible,and moderately and highly resistant),which were highly correlated with field specialists’evaluations.Finally,we derived disease developmental curves based on measures of key traits during 10–20 DAI,quantifying varietal disease progression patterns over time.To our knowledge,this work represents a significant advancement in large-scale disease phenotyping and automated analysis of FHB in wheat,providing a valuable toolkit for breeders and plant researchers to assess resistance levels,select disease-resistant varieties,and understand dynamics of the fungal disease.展开更多
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04025 to Xiu’e Wang)the Seed Industry Revitalization Project of Jiangsu Province(JBGS(2021)006 to Xiu’e Wang)+3 种基金the National Natural Science Foundation of China(32070400 to Ji Zhou)Ji Zhou,Robert Jackson,and Greg Deakin were partially supported by the Allan&Gill Gray Foundation’Sustainable Productivity for Crop Improvement(G118688 to the University of Cambridge and Q-20-0370 to NIAB)Ji Zhou was supported by the United Kingdom Research and Innovation’s(UKRI)Biotechnology and Bio logical Sciences Research Council(BBSRC)AI in Bioscience Grant(BB/Y513969/1 to Ji Zhou)The UK-China research activities were supported by the BBSRC’s International Partnership Grant(BB/Y514081/1 to NIAB)
文摘Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key to address FHB-related challenges,but its progress is delayed by traditional methods due to the small-scale,laborious and relatively subjective nature of manual assessment.This study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once(YOLO)model to examine FHB in wheat,enabling us to perform large-scale and automated symptomatic analysis of this disease.We first established an Open FHB(OFHB)training dataset,consisting of 4867 diseased and 106,801 healthy spikes collected from 132 commercial breeding lines during FHB progression.Then,a deep learning model called YOLOv8-WFD was trained for detecting healthy and diseased spikes,followed by an adaptive Excess Green method to identify symptomatic regions and thus FHBrelated traits on spikes.To study resistance levels,we employed an unsupervised SHapley Additive exPlanations(SHAP)method to pinpoint key traits between 10 and 20 d after inoculation(DAIs),resulting in the classification of 423 varieties trialed during the 2023–2024 growing seasons into four resistance levels(i.e.,highly and moderately susceptible,and moderately and highly resistant),which were highly correlated with field specialists’evaluations.Finally,we derived disease developmental curves based on measures of key traits during 10–20 DAI,quantifying varietal disease progression patterns over time.To our knowledge,this work represents a significant advancement in large-scale disease phenotyping and automated analysis of FHB in wheat,providing a valuable toolkit for breeders and plant researchers to assess resistance levels,select disease-resistant varieties,and understand dynamics of the fungal disease.
