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Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning

Koc, Alexander and Odilbekov, Firuz and Alamrani, Marwan and Henriksson, Tina and Chawade, Aakash (2022). Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning. Plant Methods. 18 :1 , 30
[Research article]

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Abstract

Background High-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial. Results The results show that 40-42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of r(s) = 0.50-0.61 when measuring the correlation between predicted and observed scores. Some of the most important spectral features for prediction were the Plant Senescence Reflectance Index (PSRI), Photochemical Reflectance Index (PRI), Red-Green Pigment Index (RGI), and Greenness Index (GI). Conclusions The proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust.

Authors/Creators:Koc, Alexander and Odilbekov, Firuz and Alamrani, Marwan and Henriksson, Tina and Chawade, Aakash
Title:Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning
Series Name/Journal:Plant Methods
Year of publishing :2022
Volume:18
Number:1
Article number:30
Number of Pages:11
Publisher:BMC
ISSN:1746-4811
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Agricultural Science
Keywords:High-throughput phenotyping, Plant breeding, Yellow rust, Field phenotyping, Spectral vegetation index, Low-cost phenotyping, Winter wheat, Disease resistance
URN:NBN:urn:nbn:se:slu:epsilon-p-116572
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-116572
Additional ID:
Type of IDID
DOI10.1186/s13007-022-00868-0
Web of Science (WoS)000769459900001
ID Code:27502
Faculty:LTV - Fakulteten för landskapsarkitektur, trädgårds- och växtproduktionsvetenskap
Department:(LTJ, LTV) > Department of Plant Breeding (from 130101)
Deposited By: SLUpub Connector
Deposited On:01 Apr 2022 13:25
Metadata Last Modified:01 Apr 2022 13:31

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