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Combining ultralow-altitude drone phenotyping with deep learning analytics to assess resistance and disease dynamics of Fusarium head blight in wheat

Shuchen Liu Jin Xiao Ji Zhou Jie Dai Jinlong Huang Zhenjie Wen Wenli Zhang Liyan Shen Robert Jackson Xiu'e Wang Greg Deakin

作物学报（英文版）2025，Vol.13Issue(5)：1372-1385,14.

作物学报（英文版）2025，Vol.13Issue(5)：1372-1385,14.DOI:10.1016/j.cj.2025.08.009

Combining ultralow-altitude drone phenotyping with deep learning analytics to assess resistance and disease dynamics of Fusarium head blight in wheat

Shuchen Liu ¹Jin Xiao ²Ji Zhou ³Jie Dai ¹Jinlong Huang ¹Zhenjie Wen ¹Wenli Zhang ¹Liyan Shen ¹Robert Jackson ⁴Xiu'e Wang ²Greg Deakin⁴

作者信息

1. College of Engineering,Academy for Advanced Interdisciplinary Studies,Plant Phenomics Research Centre,Nanjing Agricultural University,Nanjing 210095,Jiangsu,China
2. State Key Laboratory of Crop Genetics and Germplasm Enhancement,Cytogenetics Institute,Nanjing Agricultural University/Zhongshan Biological Breeding Laboratory/JCIC-MCP,Nanjing 210095,Jiangsu,China
3. College of Engineering,Academy for Advanced Interdisciplinary Studies,Plant Phenomics Research Centre,Nanjing Agricultural University,Nanjing 210095,Jiangsu,China||Data Sciences,Crop Science Centre(CSC),National Institute of Agricultural Botany(NIAB),Cambridge CB3 0LE,UK
4. Data Sciences,Crop Science Centre(CSC),National Institute of Agricultural Botany(NIAB),Cambridge CB3 0LE,UK
折叠

摘要

关键词

Fusarium head blight(FHB)/Ultralow-altitude drone phenotyping/YOLO-based symptomatic detection/FHB resistance/Disease dynamics/Wheat

Key words

Fusarium head blight(FHB)/Ultralow-altitude drone phenotyping/YOLO-based symptomatic detection/FHB resistance/Disease dynamics/Wheat

引用本文复制引用

Shuchen Liu,Jin Xiao,Ji Zhou,Jie Dai,Jinlong Huang,Zhenjie Wen,Wenli Zhang,Liyan Shen,Robert Jackson,Xiu'e Wang,Greg Deakin..Combining ultralow-altitude drone phenotyping with deep learning analytics to assess resistance and disease dynamics of Fusarium head blight in wheat[J].作物学报（英文版）,2025,13(5):1372-1385,14.

基金项目

This work and project partners at NAU were 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),and the National Natural Science Foundation of China(32070400 to Ji Zhou).Ji Zhou,Robert Jackson,and Greg Deakin were partially sup-ported by the Allan & Gill Gray Foundation'Sustainable Productiv-ity 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).The authors would like to thank all members of the Zhou laboratory and the Wang laboratory at the Nanjing Agricultural University(NAU,China)and Cambridge Crop Research,the National Institute of Agricultural Botany(NIAB,UK) （2023ZD04025 to Xiu'e Wang）

Guohui Ding for his help in planning field experiments and phenotyping,Jie Zhou and Gang Sun for DL modelling and trait analysis.The authors are also grateful to Jiangsu Provincial Dahua Seed Enterprise Co.,Ltd.for providing the commercial wheat breeding lines studied in the research. （）

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